AI in Foreign Language Learning and Teaching 9798891133846, 9798891134355

Table of contents :
Contents
Preface
Chapter 1
Technology in Foreign Language Teaching
Prologue
The Language Laboratory
The Audio-Lingual Method
The Role of the Native Language
The Digital Language Laboratory
Computer-Assisted Language Learning and Teaching
CALL Pedagogy
The Role of the Teacher
AI in Foreign Language Teaching
Generative AI
General Features
Caveats
The Contemporary Language Classroom
Classrooms without Walls
A Partnership
Epilogue
Chapter 2
Foreign Language Learning
Prologue
Theories and Models
Interlanguage Theory
The Input Hypothesis
Universal Grammar Theory
Pragmatic Models
Learning Variables
Motivational Factors
Enhancing Motivation
Machine Learning
Chatbots
Conceptualization
Epilogue
Chapter 3
Developing Linguistic Competence
Prologue
Pronunciation and Spelling
Listening and Speaking
Writing
Grammar
Words and Sentences
Grammar and Usage
Vocabulary
Polysemy
Thematic Vocabulary
Epilogue
Chapter 4
Promoting Communicative Competence
Prologue
Communicating in a Foreign Language
Communicative Competence
Role of Context
Conversations
Structure of Conversations
Conversation Analysis
Interactional Verisimilitude
Focused Interactions
Interactional Pedagogy
Epilogue
Chapter 5
Enhancing Conceptual Competence
Prologue
Conceptual Fluency
Cultural Meanings
Calquing
Metaphorical Competence
Conceptual Metaphor
Metonymy and Irony
Contrastive Conceptual Analysis
Translation Analysis
Metaphor Plugin
Epilogue
Chapter 6
Blended Foreign Language Teaching
Prologue
The Learner
The Main Competencies
Learner-Chatbot Interactions
The Teacher
Learner-Centric Pedagogy
A Teaching Assistant
The Course
Blended Pedagogy
A Paradigm Shift
Epilogue
Appendix
Language Learning Apps
Babbel
DALL-E
Duolingo
Rosetta Stone
Memrise
Busuu
Drops
Mondly
References
Index
Blank Page

Citation preview

Marcel Danesi

AI in Foreign Language Learning and Teaching THEORY AND PRACTICE

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NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Names: Danesi, Marcel, 1946- author. Title: AI in foreign language learning and teaching : theory and practice / Marcel Danesi. Other titles: Artificial intelligence in foreign language learning and teaching Description: New York : Nova Science Publishers, [2024] | Series: Languages and linguistics | Includes bibliographical references and index. | Identifiers: LCCN 2023057308 (print) | LCCN 2023057309 (ebook) | ISBN 9798891133846 (paperback) | ISBN 9798891134355 (Adobe pdf) Subjects: LCSH: Language and languages--Study and teaching--Technological innovations. | Artificial intelligence--Educational applications. Classification: LCC P53.855 D36 2024 (print) | LCC P53.855 (ebook) | DDC 418.0078/5--dc23/eng/20240109 LC record available at https://lccn.loc.gov/2023057308 LC ebook record available at https://lccn.loc.gov/2023057309

Published by Nova Science Publishers, Inc. † New York

Contents

Preface

.......................................................................................... vii

Chapter 1

Technology in Foreign Language Teaching ....................1 Prologue...............................................................................1 The Language Laboratory ...................................................2 The Audio-Lingual Method ............................................... 4 The Role of the Native Language ...................................... 7 The Digital Language Laboratory .................................. 10

Computer-Assisted Language Learning and Teaching .....................................................................12 CALL Pedagogy.............................................................. 13 The Role of the Teacher .................................................. 14

AI in Foreign Language Teaching .....................................15 Generative AI.................................................................. 16 General Features ............................................................ 17 Caveats ........................................................................... 19

The Contemporary Language Classroom ..........................19 Classrooms without Walls .............................................. 20 A Partnership.................................................................. 22

Epilogue.............................................................................23 Chapter 2

Foreign Language Learning ...........................................25 Prologue.............................................................................25 Theories and Models .........................................................26 Interlanguage Theory ..................................................... 27 The Input Hypothesis ...................................................... 31 Universal Grammar Theory ........................................... 33 Pragmatic Models........................................................... 35

Learning Variables ............................................................37 Motivational Factors ...................................................... 37 Enhancing Motivation .................................................... 38

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Contents

Machine Learning ..............................................................39 Chatbots.......................................................................... 41 Conceptualization ........................................................... 42

Epilogue.............................................................................43 Chapter 3

Developing Linguistic Competence ................................45 Prologue.............................................................................45 Pronunciation and Spelling ................................................47 Listening and Speaking ................................................... 48 Writing ............................................................................ 49

Grammar ............................................................................51 Words and Sentences ...................................................... 52 Grammar and Usage ...................................................... 54

Vocabulary ........................................................................58 Polysemy ......................................................................... 58 Thematic Vocabulary ...................................................... 59

Epilogue.............................................................................61 Chapter 4

Promoting Communicative Competence .......................65 Prologue.............................................................................65 Communicating in a Foreign Language ............................67 Communicative Competence .......................................... 68 Role of Context ............................................................... 69

Conversations ....................................................................72 Structure of Conversations ............................................. 74 Conversation Analysis .................................................... 78

Interactional Verisimilitude ...............................................82 Focused Interactions ...................................................... 82 Interactional Pedagogy .................................................. 86

Epilogue.............................................................................86 Chapter 5

Enhancing Conceptual Competence ..............................89 Prologue.............................................................................89 Conceptual Fluency ...........................................................90 Cultural Meanings .......................................................... 92 Calquing ......................................................................... 96

Metaphorical Competence .................................................98 Conceptual Metaphor ..................................................... 99 Metonymy and Irony ..................................................... 102

Contrastive Conceptual Analysis.....................................103 Translation Analysis ..................................................... 104

Contents

v

Metaphor Plugin ........................................................... 106

Epilogue...........................................................................107 Chapter 6

Blended Foreign Language Teaching ..........................111 Prologue...........................................................................111 The Learner .....................................................................113 The Main Competencies ............................................... 114 Learner-Chatbot Interactions ....................................... 118

The Teacher .....................................................................119 Learner-Centric Pedagogy ........................................... 120 A Teaching Assistant .................................................... 121

The Course.......................................................................122 Blended Pedagogy ........................................................ 123 A Paradigm Shift .......................................................... 124

Epilogue...........................................................................127 Appendix

.........................................................................................129 Language Learning Apps.................................................129 Babbel ........................................................................... 129 DALL-E......................................................................... 129 Duolingo ....................................................................... 130 Rosetta Stone ................................................................ 130 Memrise ........................................................................ 130 Busuu ............................................................................ 130 Drops ............................................................................ 131 Mondly .......................................................................... 131

References

.........................................................................................133

Index

.........................................................................................143

Preface

In 2011, the popular American television quiz show called Jeopardy featured two human contestants competing against IBM’s Watson, an Artificial Intelligence (AI) system programmed on purpose for the competition. Watson won the match by a large margin. Then, in 2017, another AI system, called AphaGo, beat the world’s Go champion at the time, Ke Jie, with a creative move that was previously unknown, surprising Go experts. After the match, an even more “intelligent,” self-taught system, called AlphaGo Zero, was built and it achieved, amazingly, a complete victory against AlphaGo (Silver, Hubert, Schrittwieser, Antonoglou, Lai, Guez, Lanctot, Sifre, Kumaran, Graepel, Lillicrap, Simonyan, and Hassabis 2018). Significantly, the boardgame Go was considered much more difficult for computers to win than other games such as chess, because its strategic nature makes it hard to assess a changing board configuration in a straightforward probabilistic way. It truly requires what we call “creativity.” Such mindboggling events have made it saliently obvious that AI has come of age, bearing many implications for understanding what intelligence is as well as how we learn as humans, given that the computer game systems described above learned to adapt to an ever-changing game scenario with apparent imagination and creativity. AI clearly has implications for how learning itself unfolds. If an AI system can be devised to come up with a truly intelligent move in the game of Go, previously unbeknownst to humans, then the question arises: Can AI engage in creative conversations as well, making it practically indistinguishable from a human interlocutor? A positive answer to this question has emerged with the spread of chatbots throughout the social sphere, from business to education, from the arts to science, and so on. A chatbot is, essentially, a computer program that simulates and processes human conversation (written or spoken), allowing humans to interact with the chatbot as if they were communicating with a real human being. Although an early chatbot prototype was developed in the mid-1960s by computer scientist

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Joseph Weizenbaum (1966), it has been only since 2022 that chatbot technology and its everyday uses started spreading broadly—the year when OpenAI’s ChatGPT came forward as a truly remarkable chatbot that was finetuned to respond to prompts asking it to carry out specific tasks, such as conversing with humans, and aiding learners acquire subject-matter in all domains, from mathematics to foreign languages. Chatbots are truly becoming partners or assistants to humans in various fields of endeavor. As research in AI becomes ever more sophisticated, it might even be possible for a chatbot to become even more “intelligent” than humans in some tasks, as Ray Kurzweil claimed in his 2005 book, The Singularity is Near, in which he maintains that there will come a moment in time when AI will have progressed to a point that it will autonomously outperform human intelligence. That moment, known as the (technological) singularity, will occur when an upgradable software becomes self-sufficient without human intervention, thus becoming capable of self-improvements. Each new self-improvement will bring about an intelligence explosion that will, in turn, lead to a powerful artificial intelligence (Kurzweil 2012). The singularity may already be upon us, as the AlphaGo and chatbot systems mentioned here make obvious. The implications are rather daunting at the same time that they are exciting As Good (1965: 31) put it, enthusiastically, decades ago: “Thus the first ultraintelligent machine is the last invention that man need ever make.” Whether or not the singularity is a plausible event, the point is that AI is now a fact of everyday life, and it is moving into the educational domain, influencing specific pedagogies such as foreign language teaching (FLT), where it raises several key questions regarding both how a language is learned and how language teaching is changing. As Robert Godwin-Jones (2022: 124) has aptly observed, the new AI systems “have the potential to provide openended conversational practice and language development which aligns with an ecological, usage-based perspective on language development.” It is clear that the time has come to look concretely at the pedagogical questions that the new AI technologies raise for the traditional language teaching classroom. The purpose of this book is, in fact, to examine the central issues related to the use of AI in foreign language learning and teaching, including the following questions: Can AI can teach languages independently of humans? What does it tell us about teaching and learning? Can it discover new ways of teaching a foreign language effectively? What are the implications for language education in general?

Preface

ix

The thrust of this book concerns how to make FLT, at the very least, relevant in an age of an ever-expanding AI culture, so as to make the language learning experience consistent with what students are exposed to in their daily lives as well as in other areas of education, which are increasingly opting for AI as a complementary tool of instruction. Teaching and learning foreign languages today is no longer restricted to the “walled-in classroom,” where materials, pedagogy, and face-to-face interactions have always been considered to be the central elements in bringing about successful learning outcomes. Today, online courses, YouTube videos, digitally-available materials, and the like have come forward to rival the traditional classroom as the primary locus for foreign language learning. However, the question arises as to whether nor not the outcomes are any more successful than they were in the past. Some initial work in using Deep Learning AI systems, which have the ability to interact with students and teachers in tandem, is showing how classroom-teaching can be reinforced and perhaps even enhanced with an “algorithmic tutor,” so to speak (for example, Fryer, Coniam, Carpenter, and Lăpușneanu 2020, Bibauw, François, Van den Noortgate, and Desmet 2022). This book will provide an overview of what the research says and what prospects AI bears for FLT at present and in the future. It will delineate concrete ideas that can potentially make foreign language teaching relevant to the techno-savvy student, without compromising the role of the human teacher. The book is based both on relevant research I have conducted on using text analysis with AI (Neuman, Danesi, and Vilenchik 2023), and on reviewing the relevant literature in AI-assisted FLT. I should mention that I will be using ChatGPT as the main AI system in the discussions and illustrations throughout this book for two reasons: (1) at the time of writing this book it was the most commonly used one in foreign language classrooms, and (2) it is the easiest one to use for teachers, at least in my opinion. ChatGPT stands for “Chat Generative Pre-trained Transformer,” which is a large language model chatbot developed by OpenAI. It enables users to refine and guide a conversation in terms of desired length, format, style, level of detail, and type of language used. Successive prompts and replies are the cues that shape a human-ChatGPT conversation. How will AI affect the human teacher and the overall structure of foreign language teaching? Traditional pedagogy has often seemed incapable of satisfying the individualized needs of learners; nevertheless, the teacher as part of the learning system (wherever and however it occurs) cannot be replaced. Rather, AI provides the teacher with a means to furnish individualized, innovative, and cooperative learning instruction. Overall, the fact is that we

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can no longer, as language teachers, ignore the ever-evolving technologies, or else we will truly be left behind. AI is not going to replace teachers, but teachers who use AI are likely going to gradually replace those who do not. As entrepreneur Mark Cuban has bluntly put it (in Ringelberg 2023): “Whatever you are studying right now, if you are not getting up to speed on Deep Learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.” Marcel Danesi University of Toronto, 2023

Chapter 1

Technology in Foreign Language Teaching Prologue Whenever there has been a methodological or technological innovation, bringing with it the promise of facilitating and enhancing both language learning and teaching, there has always been a concurrent period of reflection and debate on the soundness of the promise itself. The classic example is the language laboratory, which became a staple of foreign language teaching (FLT) in the 1960s, leading to the establishment of the so-called AudioLingual Method, which was initially welcomed with enthusiasm, but also with some skepticism on the part of language teachers. As time went on, it became obvious to everyone that it was not the “magic pill” it was thought to be. Since then, teachers have proceeded with caution vis-à-vis new methods and technologies that come with a similar promise. The language laboratory brought about the first real “technological revolution” in FLT; the second one was computer-assisted-language-learning (CALL), which came about after the spread of personal computers in the 1980s. Both these events will be discussed further subsequently. Suffice it to say at this point that, even though their relevant technologies have been upgraded, the language lab and CALL remain essentially ancillary teaching systems, designed to supplement or complement the pedagogical process that occurs in a classroom environment. However, since the advent of Generative AI, and the chatbots, which became available broadly at the start of the 2020s, a third technological revolution has occurred in FLT which promises to go beyond the adjunct function of the previous two. Some educators even believe that Generative AI systems will even replace human teachers. But is this truly possible? In a pertinent study, Carmelina Maurizio (2021), from the University of Turin, expressed the dilemma facing language teachers today as follows: Can AI algorithms truly enhance the learning of foreign languages and even substitute the human instructor? In several relevant studies (Yin and Satar 2020, Dokukina and Gumanova 2020), the answer appears, apparently, to be yes. Since we are at the beginning of a truly momentous revolution in language teaching, examining the implications of this technological event is of obvious importance for teachers and students alike. That is the aim of this book. This

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chapter will take an initial look at the role of technology in FLT, historically and pedagogically. For years, big tech companies have been developing algorithmic systems that are getting closer and closer to simulating natural dialogue—they are able to answer not only a wide range of questions but also understand and produce metaphorical speech and even irony. The Duolingo platform, for example, can simulate a human conversation in different languages; ELSA (English Language Speech Assistant) supports English language learning, and is used by millions of students worldwide. There are now chatbots that can be used for different specific learning tasks. Given that Generative AI is a constantly evolving technology, the two key questions it raises for FLT are the following ones: Will AI actually replace the human teacher? Or is it simply another technological tool that can be very useful in FLT as was the language lab and CALL? There are those who enthusiastically say yes to the first question, and there are those who express cynicism to both questions. Perhaps the sober words of writer Douglas Adams (2002: 95) are needed in this regard, which, though humorous, contain a grain of wisdom: Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works. Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it. Anything invented after you’re thirty-five is against the natural order of things.

The term foreign language teaching is used throughout this book in lieu of second language teaching, although the two are often used interchangeably. The reason is that the former term is typically constrained to indicate any language studied after the first in a formal setting, whereas the latter can also mean any language acquired after or at the same time with the native language in any setting, or else a language learned in immigrant situations out of necessity. In this book, the term foreign language is constrained to refer to any language learned formally in a school or school-like environment.

The Language Laboratory The first technology designed specifically to aid language students in achieving successful learning outcomes was, as mentioned, the language laboratory. The initial layout and structure of the language lab consisted of a

Technology in Foreign Language Teaching

3

circumscribed space or room where students accessed audio or audio-visual devices. The students could listen and respond to prerecorded content, through headsets and microphones in isolated sound booths, to which a teacher could simultaneously listen and thus manage, evaluate, and assess student oral productions. Language labs were common in schools across the United States, and many other parts of the world, starting in the two decades following World War II (see Figure 1.1):

Figure 1.1. The Traditional Language Laboratory circa 1970 (Wikimedia Commons).

For the sake of historical accuracy, it should be mentioned that rudimentary pre-language labs go back considerably in time, emerging right after the invention of the phonograph (Léon 1962). These consisted of nothing more than phonographs to play vinyl records, and simple phonograph-based tapes that allowed students to record their voices. They were not included in individual booths. As Kitao (1995) comments: It was Edison’s invention of the tin foil phonograph in 1877 that made the first language laboratories possible. It was used for a foreign language class for the first time in 1891. At first, records were mainly used to preserve rare languages, but in the late 1800s and early 1900s, correspondence courses were developed using records. Students listened to records, recorded their own voices speaking the languages, and sent their recordings back to the company for evaluation. The procedures used by these early correspondence schools established methods that were

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Marcel Danesi later used in language laboratories. Between 1900 and 1950, equipment became more sophisticated, with the invention and development of tape recorders and television, and schools began establishing language laboratories.

In the 1940s, linguists at the University of Michigan developed the socalled Audio-Lingual Method (ALM) for foreign language learning. The method relied on repeated listening and speaking drills, and hence the use of new language labs became central to the method. By the mid-1950s, hundreds of language labs were constructed in the US, with the majority in colleges and universities. A decade later, thousands of language labs were built in all kinds schools across the country as the ALM became a mainstream model of FLT. However, by the early 1970s use declined as language teaching shifted away from behaviorist models of learning, on which the ALM was based, to different views of the learning process (discussed in chapter 2).

The Audio-Lingual Method The language laboratory was incorporated into mainstream FLT from the late 1940s to the early 1970s—an era in which it subserved mainly the learning goals of the ALM. It consisted, as mentioned, of individual booths for students, equipped with a microphone, audio tapes, headphones, and a control console operated by a teacher or assistant. The teacher console included a tape recorder to play the selected recording, a headset and system of switches enabling the teacher to monitor individual student responses, and a microphone for communicating with the students. The ALM was inspired by the confluence of behaviorist psychology and structuralist linguistics in the late 1920s and early 1930s. The former stressed habit-formation, and the latter structural patterns in language design. In the early labs, there were several behaviorist-focused features built into the recorded content, consistent with the ALM: •

•

The students listened to pre-recorded tapes, in which native speakers of the foreign language (FL) enunciated syllables, words, phrases, sentences, and larger texts in order of complexity. The idea was to allow for a fine-tuning of student comprehension of the spoken FL. Students would repeat the FL items imitatively and then, in later sessions, engage in question-and-answer drills with the recorded

Technology in Foreign Language Teaching

•

•

•

•

5

material or with teachers so as to enhance their production skills in the FL. In the playback mode, students could listen to their responses on tape, and compare them to the responses given by native speakers, a phase that was clearly intended to encourage self-correction and to boost self-learning. As technology in lab design advanced, a visual component was added to many labs, including slides, films, and photos designed to simulate reality-based and vivid FL-usage situations. The visual component was also used to impart images of the FL culture, so as to connect the verbal learning process to the cultural-conceptual one indirectly. A separate method, called the Audio-Visual Method, arose from the new technology, which retained the main pedagogical objectives of the ALM, but stressed the role of visualization in the learning process much more. Like the ALM, the new method stressed pattern practice, habit formation, and the teaching of oral skills before reading and writing skills. But it added an innovative feature to this basic blueprint—the new dialogical and conversational material was introduced visually via filmstrips and other kinds of visual aids either in the classroom but more normally in the language lab.

Overall, the underlying learning principle behind the construction and design of the early language labs was the behaviorist view that imitation, repetition, and habit-formation through comparison and self-correction were at the core of the learning process. As mentioned, the pre-recorded tape would present the correct model of a syllable, word, or sentence spoken by a native speaker and the students would listen to it, repeat it, listen to the model again, and then repeat it again. The tape would then continue presenting new items for the students to repeat and then apply within a structural pattern—a pedagogical technique called pattern-practice. So, if the pattern was the present tense of first conjugation verbs in Italian, the recording would first present the conjugation paradigm of a verb such as parlare (“to speak”): Io parlo. The student would repeat this and then the tape would leave out the verb, allowing the student to fill it in: Io …. It would then introduce another verb mangiare (“to eat”) and this time just the pronoun with a blank, for the student to fill in: Io …. At first, therefore there was no explicit grammar instruction. The belief was that by repeating the pattern, memorizing it, and

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then using it with novel information, the student would learn it by induction and thus be able to apply it to other areas of the FL spontaneously. The lab lessons were thus built on pattern-practice drills over which the students had little or no control. Pattern practice was introduced by American linguist Charles Fries (1927, 1945), the director of the English Language Institute at the University of Michigan, the first of its kind in the United States. Fries believed that learning FL structure and usage through imitation and controlled repetition was the starting point for the student. The earliest types of language lab drills conformed to Fries’ view of learning, consisting of a four-part sequence: • • • •

Repetition: students repeated an utterance as soon as they heard it: “Io amo la pizza” (“I love pizza”). Inflection: one word in the utterance is replaced by another form for the students to repeat: “Io mangio la pizza” (“I am eating pizza”). Further replacement: another word is then replaced by another: “Io amo la frutta” (“I love fruit”). Restatement: students are then asked to use the relevant patterns, typically in response to a pre-recorded question: Question: “Ami la pizza e la frutta?” (“Do you love pizza and fruit”). Response: “Si, amo la pizza e la frutta”) (“Yes, I love pizza and fruit”).

Of course, more than one type of drill would be incorporated into a practice session, and the drills and patterns would become increasingly complex. The point was to get the students to extrapolate any grammatical or lexical pattern and then be able to use it spontaneously. Native-like competence was thought to crystallize on its own over time via the same methodology. However, by the early 1970s this pedagogical model of FLT was largely abandoned, and along with it, the language lab. The main reason was, as the research started showing, that students could not map the patterns induced from the drills to real-life communicative situations in the FL (Rivers 1964, Lamendella 1979). By discarding the use of controlled repetition and pattern practice from FLT, subsequent approaches may have ignored the need for such type of instruction—after all, it is central to other kinds of learning tasks such as learning to play an instrument proficiently (Castagnaro 2006). This topic will be taken up again subsequently. The point here is that the ALM introduced the first technology used to support the learning process.

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In updated, current uses of the language lab the stimuli presented have been expanded in nature considerably and include the following: • • • • •

A dialogue is generally used as the initial input, which can also be supported visually with video technologies and techniques. Pattern practice is based on material taken from the dialogue. The meanings of new vocabulary items are illustrated both through pattern practice and visual aids. Students can interact with the content via role-playing exercises. Follow-up activities are designed to help with comprehension, production, and creative uses of the FL.

While strict ALM-based teaching is no longer found, by and large, in FL classrooms, the approach has embedded several pedagogical principles into the profession broadly. One is that imitation and emulation cannot be completely eliminated. Another is that correct pronunciation plays a much more crucial role in acquiring the FL than some more communicatively-based methods have tended to believe. As Pardede (2010) has aptly observed in this regard: “Although the usefulness of teaching pronunciation is one of the most widely debated subjects in the field of language teaching, current pedagogical thinking and research on pronunciation reveals that intelligible pronunciation is a very essential component of communicative competence.”

The Role of the Native Language A third principle on which the ALM was based was that that FL-learning is shaped, in large part, by the students’ knowledge of their native language (NL); that is, the NL was thought to play a determinative role in shaping and guiding the FL-learning process, especially during the initial stages. The evidence for this was gleaned from the typical errors as well as the successful efforts that students consistently demonstrate in learning the FL, which show a direct influence from the NL—a process called transfer. Positive transfer, as it came to be called, occurs when the patterns of the two languages, the NL and the FL, are isomorphic in some way; and this facilitates the learning process considerably. Negative transfer, on the other hand, occurs when the patterns are asymmetrical or non-existent. Transfer Theory became a major aspect of FL-learning theory and of FLT practice. By comparing and

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contrasting the FL and the NL in specific ways (pronunciation patterns, grammatical patterns, and so on), the practitioners of the ALM aimed to predict where positive and negative transfer might surface in the learning process—a technique known as Contrastive Analysis (CA). The results of CA were then used to prepare classroom and language lab materials in a sequential flow, whereby patterns that involved positive transfer were presented at first, while those that activated negative transfer processes were delayed to later learning phases. The idea was to allow the students to transfer their NL knowledge to the FL at first, focusing on differential patterns after this initial phase. The ALM, however, did not surface in a historical vacuum. It involved refinements and elaborations of various aspects of the so-called Direct Method (DM) that emerged as the first psychologically-designed approach to FLT at the end of the nineteenth and early twentieth centuries. The main assumption in that approach was actually rather different from the assumptions that shaped the ALM. The DM believed that the same psychological mechanisms and processes enlisted by the child in acquiring the NL were involved in FLlearning. Initially, the DM was a reaction to the use of explicit grammatical training in the classroom. The congeners of the DM saw grammar as complicating the learning process; after all, children were not taught to speak their native language through grammatical training, but by simple exposure to it in context. The FLT reformers in that era, such as François Gouin (1880) and Wilhelm Viëtor (1886), actually saw grammar training as an obstacle for the typical student, since it delayed and even blocked access to the FL. So, for the first time in foreign language teaching history, teachers turned their attention to psychology for insights on how to develop a psychologicallycompatible model of language pedagogy. Their decision to do so was further influenced by five crucial events: •

•

•

Psychology itself had emerged as a scientific enterprise. The reformers claimed that teachers should use the findings of psychologists on language learning to guide them in selecting what was to be taught and when to teach it. Linguistics was emerging at the same time as an autonomous discipline (Saussure 1916). This gave rise to a new scientific view of language. The Modern Language Association of America was founded in 1883 and the Modern Language Association of Great Britain in 1889, both

Technology in Foreign Language Teaching

•

•

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of which focused attention on the importance of the teaching method itself in enhancing learning outcomes for all types of learners. The International Phonetic Association was established in 1886, raising awareness about the importance of accurate pronunciation in language learning. National languages came to play increasingly important roles on the stage of world relations after the Industrial Revolution. This led to a practical need on the part of the average person to learn foreign languages in school. The reformers wanted to turn this need into an achievable goal for all, not just for those with a talent for languages.

This new Zeitgeist led to the construction of the Direct Method. Its main features were as follows (Titone 1968: 100-101, Richards and Rodgers 1986: 7-10): •

•

•

•

•

All instruction and classroom activities were carried out in the FL from the very outset. For this reason, it was considered preferable for teachers themselves to be native speakers of the language, so that they could provide native-like input constantly. Utilization of the students’ NL was discouraged. The new vocabulary was imparted directly through actions, gestures, pictures, objects, etc. (hence the term Direct Method). Every lesson began with listening and imitation activities revolving around a dialogue. To ensure that these were learning-effective and appropriate, uniformly-constructed textbooks were introduced as the basis of instruction. These contained the dialogues and follow-up exercises arranged in order of increasing complexity. Pattern practice was used consistently, whereby the learner was expected to induce the relevant rules of grammar inherent in the patterns. Reading and writing instruction and practice followed initial oral learning phases. From this, the chronological teaching of the four skills was introduced into pedagogy: listening-speaking-readingwriting.

Although some of the DM’s techniques had historical parallels—pattern practice drills, for instance, were actually traceable to the substitution tables of sixteenth and seventeenth-century grammars, and pedagogically-designed

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situational dialogues to the writings of Comenius (the seventeenth-century Czech educator). But never before were they organized as systematically and coherently into a model that was believed to be compatible with the psychology of foreign language learning. Many of the DM’s techniques continue to be used to this day (whether or not it is known) in a variety of guises. The DM was also based on an implicit transfer learning model; in this case, it was assumed that the learning of the FL followed the same developmental path as the acquisition of the NL. Via imitation and repetition of basic patterns the student, like the child, gradually builds competence in the new language. However, the DM ignited controversy almost from the outset. Henry Sweet (1899), for instance, argued that adolescents and adults, unlike children, brought previous language experience to the learning task. He also warned against the assumption that all students learned in the same way, according to purported psychological principles. A little later, the British educator Harold Palmer (1917, 1921, 1922) expressed similar reservations, emphasizing that the diverse backgrounds and motivations of learners warranted the need for instructional flexibility. Rather than develop a single, standard method, it was better to have a set of principles that could be tailored according to situation. By the early 1920s, disenchantment with the DM had reached a critical mass. It was abandoned en masse shortly thereafter. The Audio-Lingual Method was the second major FLT method. Its promise to promote successful learning for all students was strengthened by the language laboratory, as discussed. The method differed from the DM by taking the NL directly into account via Transfer Theory and Contrastive Analysis. So, while the DM believed that the FL learner acquired the new language tabula rasa as did the child, the ALM saw the process as being shaped directly by the habits and unconscious knowledge of the NL. Classroom and language lab materials were thus designed according to Transfer Theory.

The Digital Language Laboratory The language lab was largely abandoned starting in the 1970s, because of the shift in FLT models, which were becoming increasingly focused on communication in the FL. However, the advent of the personal computer in the 1980s brought back the language lab, bolstered by digital technologies. Levy (1997: 1) described the promise of the computer and the digital lab as

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“the search for and study of applications of the computer in language teaching and learning.” One of the first such labs was constructed in 2005 at the prominent women’s college at Kochi in southwestern India. This new type of lab was a self-contained classroom, with teachers pegged as language coaches or debriefers. As digital technologies expanded dramatically in the first two decades of the millennium, the digital language lab became increasingly “smart,” with software that was increasingly more responsive to individual learner needs. It provided instant access to native-speakers interacting in the FL via the Internet, immersing the students virtually into communicative contexts that reflected situations of interaction that occurred commonly in the foreign country (see Figure 1.2):

Figure 1.2. Digital Language Lab circa 2015 (Wikimedia Commons).

The purpose of the language labs of the ALM era was for students to gain auditory exposure to the language they were studying, within the confines of the limited technology of the times. Today, the language lab has made possible a whole new dimension to FL-learning that can both reinforce and amplify classroom learning, and even replace it. Among its features, the following stand out: • •

Multimedia-based input: the software allows learners to interact with multimedia content constructively. Interactivity: the digital lab provides learners with interactive capabilities that enable them to practice and expand language skills, including games and simulations.

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• • • • •

Feedback: the software makes instant feedback and assessment possible, allowing for self-monitoring. Individualized learning: the software allows students to learn at their own pace on the basis of individualized feedback. Virtuality: the lab has the ability to provide learners with virtual environments that simulate realistic situations in the foreign culture. Artificial Intelligence: the software may incorporate AI to provide learners with personalized and expanded learning experiences. Cloud-based learning: the digital lab allows cloud-based learning, whereby students can access language learning resources from any device with an Internet connection.

To indicate the radical break from the behaviorist-based ALM language lab, the term “language lab” fell into disfavor at the millennium, replaced with terms such as “language media center” or “learning resource center” (Scinicariello 1997). However, today the historical memory with regard to the ALM has faded considerably, and the initial term of language lab has resurfaced, but with the expanded design and functions just discussed. So, it is fair to say that the language laboratory today has made a kind of comeback, having expanded its capacities from simple native speech models and drill practices to using the Internet, virtual reality, and AI to support FL-learning. And significantly, the lab does not have be located in a special room, given that a computer can download the required software allowing students to learn anywhere and at any time.

Computer-Assisted Language Learning and Teaching The second major technological revolution in FLT dovetailed with the spread of computer technologies in the late 1990s. It was named CALL (Levy 1997, Warschauer and Healey 1998). The term CALI (computer-assisted language instruction) was used before CALL, reflecting its origins in computer-assisted instruction already in the late 1960s. CALL today emphasizes computer training systems that allow learners to work on their own. Research has largely corroborated the efficacy of CALL, both as an ancillary system of learningteaching and as a self-contained learner-based system. In the former, it is used, more or less, like a portable language laboratory; in the latter case, it is essentially a self-contained digital classroom, allowing for individualized

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learner needs to be met concretely. The findings of a large-scale survey of over 2,000 college students studying nineteen different FLs, conducted by Sydorenko, Hsieh, Ahn, and Arnold (2017), found that CALL was perceived as effective, motivating, and beneficial overall to acquiring FL competence. The higher the use of computer technology outside of the classroom also resulted in more positive views, independently of the FL. Among the features that any CALL system now makes available, the following stand out, showing that digital technology now makes FLT and FLlearning more and more dependent on systems that go beyond the classroom and the single teacher in it—a development that will be discussed in more detail in the final chapter: • • • • • • • • •

image storage and sharing, based on scenes and situations in the FL culture; social bookmarking, allowing users to save links to web pages that they want to remember or share; discussion lists, blogs, wikis, social networking platforms with native speakers of the FL; chat rooms and virtual worlds that allow for interaction with FL speakers; FL podcasting; sophisticated audio tools, video sharing applications, and screen capture tools for self-instruction purposes; animation tools, such as comic strips, cartoons, etc. that provide sophisticated exposure to the FL; mashups if various kinds in the FL; games and simulations in the FL.

CALL Pedagogy It was the emergence of the Web at around the same time as the spread of CALL that made integrated pedagogy plausible as a mode of training students to learn via computer systems in tandem with human actors, such as native speakers and classroom teachers. A problem that arose, early on, was that this led to considerable timewasting through random Web navigations. This implied that CALL on its own might not be as productive as it was claimed to be. As a corollary, it implied

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the need for the teacher to act as a central figure in the whole process. Overall, CALL brought about a new lexicon for discussing FLT and for envisioning what should occur in a typical classroom. Among the items in this lexicon, four stand out as now embedded deeply into any view of FLT: 1. As ancillary tools: using digital technologies to amplify or extend classroom learning. 2. As integrative tools: using the technologies in tandem with traditional learning materials, such as print textbooks. 3. As collaborative tools: using technologies to get students to interact among themselves and others outside the classroom, such as with virtual language communities. 4. As embedded tools: taking into account what students bring to the classroom in terms of technological skills that have become embedded in their lives. However, as Myrdene Anderson (2013: 298) has warned, we should be very careful to embrace technology as an educational panacea simply because it is new and trendy, because it can itself become a source of new and unwanted problems in education if we do not understand what it is and how it blends with social life: I question silver bullet solutions to perceived social problems, among which our public education makes an easy target. This allows me to slide from teaching and learning in the early grades right into issues of later training in the social sciences, all larded with prescriptive exposure to the quantitative. Technology, computers included, while frequently suggested as solutions to social and educational problems often contribute to those very problems.

The Role of the Teacher The main impact of CALL pedagogy was that it opened up FLT to an integrated way of looking at the classroom, changing the role of the teacher as a magister instructor to that of a facilitator or coach, along with computer technology. The question even arose, early on, if the teacher was needed at all, given that the computer could carry out most, if not all, of the normal pedagogical duties associated with the human teacher. Training in computer

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literacy for the students was thus seen as key to transforming the classroom, empowering students to dictate the course of events in a teaching program. However, as became obvious in experimental teacher-less programs, students variegated considerably, achieving differential outcomes and even leading to a high proportion of discouragement from further learning. The traditional classroom in which a teacher and students are in physical and dialogical contact is being altered constantly. Some educators (Gobbi 2012, Neilsen 2012) believe that the virtual classroom, without physicallypresent teachers, is inevitable for three main reasons: (1) it is more economical (it costs less than paying many teachers to deliver the same content individualistically); (2) it is in synch with the expectations of a society that has become dependent on computer technologies; and (3) it reflects how learning occurs in the contemporary world, namely through screens and interactive software. While this is certainly occurring, it is also true, as Anderson warned (above), that the virtual classroom is unlikely to replace the important contact between a real teacher and students in some shared physical space, given the need for such contact in human learning. Integrating the traditional classroom with computer technologies and the resources of the Internet is not replacing teachers, but simply changing their role—as any innovation has implied throughout FLT history (Hartsell and Yuen 2006). It is true that the first two major technological revolutions in FLT—the language lab (traditional and digital) and CALL (in any of its forms)—were critical in instilling a new view of the teacher, as a partner with technology, leading over time to what can be called blended pedagogy—to be discussed in the final chapter.

AI in Foreign Language Teaching The third technological revolution in FLT is an ongoing one, again having profound implications for everything, from the role of the teacher to models for developing appropriate pedagogy. And, as in the case of previous technologies, there is no turning back the clock (Gkountara and Prasad 2022). As McLuhan and Leonard (1967: 24) predicted decades ago, changes in technology invariably bring about changes in how teachers and students perceive their own roles: Tomorrow’s educator will be able to set about the exciting task of creating a new kind of learning environment. Students will rove freely through this place of learning, be it contained in a room, a building, a

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As Holmes, Persson, Chounta, Wasson, and Dimitrova (2022: 10) have aptly noted, the advent of AI is raising profound new issues on how we perceive education and its objectives. With its ability to analyze data on student performance and teacher and curricular needs, AI is becoming more and more a tool for educators to create customized lesson plans and assessments that align with each student’s learning style, which can improve student experience and motivation, and ultimately lead to better outcomes.

Generative AI To grasp the magnitude of the revolution we are undergoing, it is useful to go over a few technological aspects of Generative AI, as a backdrop to gleaning any implications it bears for FLT. It is defined as artificial intelligence capable of generating text, images, or other media, with the ability to learn the patterns in novel input and then generate new data with similar characteristics (Taulli 2023). Starting in 2020, advances in so-called transformer-based deep neural networks led to the creation of a Generative AI systems capable of accepting natural language prompts as input, called chatbots—computer programs that process human conversation, allowing humans to interact with digital devices as if they were communicating with a real person—of which ChatGPT (Chat Generative Pre-trained Transformer) is the most widely-used one (at present). Simply put, a deep neural network is a class of Machine Learning algorithms that mimic the information processing of the brain. ChatGPT is a large language model-based chatbot launched on November 30, 2022, which enables users to refine and guide a conversation towards a desired length, format, style, level of detail, and language used. It is based on the transformer architecture developed by Google, fine-tuned for conversational applications. It successor, GPT-4, is one of the most adaptive of all chatbots available at the time of writing this book. Chatbots can produce essays, carry on interactive dialogues, and translate from any language to another, with almost perfect grammar and appropriate style and register. Someone enters a written prompt, to which ChatGPT responds with an answer, as though it was a person chatting with the prompter

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rather than an AI application. The use of plugins can expand the range of tasks that ChatGPT can carry out, including producing simulated oral speech and reacting to speech commands, rather than just written ones. Like previous technologies, the chatbots are transforming the FL classroom, serving as language-learning aids, providing learners with opportunities for engaging in meaningful conversations in the FL, and getting instant feedback on their grammar and vocabulary usage. And like the language labs of the past, interacting with ChatGPT helps learners improve their listening, speaking, reading, and writing skills through the simulation of real-life language-based scenarios. However, as will be discussed throughout this book, it is essential to maintain a critical stance when using chatbots. As an AI system, it may not always provide contextually appropriate responses. Moreover, replacing real teachers completely with AI would be counterproductive, given that a large part of learning is an embodied process, requiring the meaningful signals that the body emits and receives which bolster the learning process. A key project headed by Bernd Hackl (2016) has shown that the constant interaction between teachers and students in a physical classroom creates a social environment that is supportive of learning, combining bodily language with verbal signals that enhances meaning exchanges. The study found overall that successful learning hinges on the physical presence of teachers and the physical learning context they create in the classroom.

General Features The main strength of AI language models is that they can adapt to student interactions and to teacher requirements literally on the spot. Among the features that current chatbots make available, the following are of specific importance to FLT: •

New information. Chatbots can either supplement or introduce the new information related to the FL as required in a course of study. For example, teachers of Chinese can use ChatGPT to introduce pinyin (a phonetic notation system for representing the sounds of Mandarin Chinese with Latin alphabetic characters). By prompting ChatGPT with a question such as, “What are the rules for Pinyin?” the AI can provide a detailed explanation of the essential rules for

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•

•

•

•

•

pinyin and provide illustrations and practice material tailored according to prompts. Differentiated instruction. Given the diverse backgrounds and learning styles of students, ChatGPT can quickly adapt to them via its analysis of both the different prompts and the actual performances of students in specific areas of the FL. As a result of its analysis, ChatGPT can generate individualized instruction and exercise materials at various difficulty levels, as well as providing appropriate translation into the learners’ NL. In several tests that I myself conducted with ChatGPT, asking it to translate an utterance into Italian and to provide stylistic variants to it, the result was truly amazing, given its accuracy and its ability to even provide regional variation correctly, coming up with versions that might have escaped the attention of even a veteran teacher. Materials. As I also discovered in the case of Italian, ChatGPT is able to generate quizzes, tests, and appropriate classroom activities, with the relevant prompts and trained on actual materials already used in a course. ChatGPT was able to instantly produce cloze tests, multiplechoice exercises, true-or-false questions, as well as to suggest roleplaying and pair-based activities. Moreover, as I was able to confirm, it had the capacity to adjust to learning level as required. ChatGPT can also generate lesson plans, word lists and flash cards that are essential for teaching a specific theme. Personalized teaching. As mentioned, ChatGPT has the ability to generate materials and activities tailored to the individual needs and competence levels of each learner. For instance, I gave ChatGPT a prompt to generate a dialogue on asking for coffee at an Italian bar, which it did competently. I also asked it to show sensitivity to the identities of the interlocutors and to create a relevant dialogue that revolved around them, which it did as well. Expanded exposure. ChatGPT can provide learners with access to a vast repertoire and diversity of learning materials in the FL, including those that involve real-life situations and authentic language use, since it can navigate relevant sites and platforms on the Internet. Immediate feedback. Learners receive immediate feedback on their language, including the errors they make, followed by suggested corrections, and explanations concerning the nature of the errors. As such, the AI facilitates self-evaluation. Learners can then prompt

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ChatGPT to generate follow-up materials and quizzes for selfassessment so that they can monitor their progress on their own, without fear of being wrong in a classroom environment. Motivation. Overall, a chatbot in the classroom can help foster a congenial motivating learning environment, allowing learners to feel secure and comfortable and to not be concerned about losing face.

Caveats There are, needless to say, some caveats with chatbots that are worth mentioning at this point, and which will be elaborated upon, along with the advantages, in subsequent chapters. In addition to the potential threat to academic integrity, the chatbots might provide incorrect responses, without knowing it. For instance, they might give incorrect instructions on the number of strokes needed in writing, say, a Chinese character. Grading may also not be accurate, especially since the traditional handwritten tests need to be converted into digital formats, which are not flexible to interpretive variability. Student work that involves subjective evaluation (such as creativity, style, etc.) might also not be suitable for the AI system to guide and effectuate. Interestingly, it has been found that chatbots sometimes “lie,” so to speak; that is, they come up with invented information on their own, making them somewhat unreliable. Rather than lies, they are called ‘hallucinations.” Technically, these are (amazingly) confident responses by an AI that do not seem to be justified by its training data. For example, a hallucinating chatbot might embed plausible-sounding random falsehoods within its generated content. In the case of FLT, this makes it even more important to maintain a human teacher in the whole process, since only humans have the ability to interpret content, not just generate it. To students, who may lack knowledge of the FL and its culture according to their level of competency, hallucinations might appear plausible because the chatbot’s answer is coherent.

The Contemporary Language Classroom Putting aside faults, such as hallucinations, and given that chatbots require human interlocutors with all their inconsistencies, there is little doubt that they constitute a technological revolution in FLT. The traditional way of teaching

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with textbooks and the usual apparatus of exercises and tests written on paper is increasingly out of synch with the times, even though it may still have utility in various ways. Even in the early era of the language lab, quizzes and tests, as well as some instructional functions, took place in the laboratory via audio (and visual) recordings. With the arrival of AI and the Internet, the situation has changed (or is changing) once again. Knowledge-attainment now is guided not only by listening and reading, but also by navigating cyberspace with the help of AI. The Canadian communications theorist, Marshall McLuhan (1962, 1964) claimed that the medium used to deliver content can alter the way the content is understood. Today, this has revealed itself to be a veritable law of media. It certainly cannot be denied that technology has been re-shaping the world, including the academy. Since the ALM and the language lab, the FL classroom has evolved according to the McLuhanian law of media. The classroom consisting of desks in which students sit in front of an instructor who writes on a blackboard is now a quaint picture from the past. There is little doubt, however, that the presence of a real teacher with real students in direct contact with each other is still vital to the learning process, as discussed briefly above. It is in the classroom where students, along with teachers, help one another understand the ideas, tasks, and skills reflected in a particular lesson through an interactive format. In this type of learning environment, students can practice how to listen to one another, how to make meaning, and how to find common ground while participating in a conversation or even in some drill session. This is impossible to do with a machine alone, no matter how intelligent it might be. Simply put, humans are social beings, machines are not, and thus require social interaction so that real learning can unfold.

Classrooms without Walls One of the main educational changes that the Internet and chatbots (trained on the Internet) have brought about is what McLuhan called “classrooms without walls” (McLuhan 1960). By this, he meant that classroom procedures can occur via electronic media anywhere on Earth, being no longer restricted by the time and space constraints of the physical classroom. Of course, even in previous eras, when print materials such as books were the basis of education, teachers and students could access ideas beyond the classroom, such as in libraries, and share ideas through letter correspondences. But print moves slowly, since books and journals must be bought, read, and then discussed

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through dialogue in class or through further paper-based correspondence. Electronic media make content access and communication instant. In a discerning and quasi-prophetic way, McLuhan (1960: 3) foresaw the radical educational changes that current technologies would bring about long before these came into existence: These new developments, under quiet analytic survey, point to a basic strategy of culture for the classroom. When the printed book first appeared, it threatened the oral procedures of teaching and created the classroom as we now know it. Instead of making their own text, their own dictionary, their own grammar, students started out with these tools. They could study not one but several languages. Today these new media threaten, instead to merely reinforce, the procedures of this traditional classroom. It’s customary to answer this threat with denunciations of the unfortunate character and effect of movies and TV, just as the comic book was feared and scorned and rejected from the classroom. Its good and bad features in form and content, when carefully set beside other kinds of art and narrative, could have become a major asset to the teacher.

Because people the world over can now see themselves as participants in events going on in some other part of the world by simply looking at a computer screen, they truly tend to feel interconnected. Real-space villages and communities involve territorialities that imply knowledge of the same native languages and symbolic-ritualistic systems that bind people within them together. But in the Internet-connected global village (a term introduced by McLuhan), made up of interactive social networks, the forms of language and culture of real-space communities are no longer requirements for entry. Indeed, the global village has generated new lingua francas which are ensconced in new forms of literacy, some of which are divorced from historical linguistic from cultural practices. Thus, a new dynamic has emerged uniting people in non-traditional ways. And these have great socio-emotional power. The global village has also made the classroom without walls a concrete reality, since information and training can be gleaned individualistically on platforms and sites such as Google, YouTube, and the like.

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A Partnership Education today is on the threshold of a veritable paradigm shift. To put it figuratively, students now live on both sides of a computer screen. At the threshold of the shift, linguist and political critic Noam Chomsky (2002) sensed an inherent danger in it, because he saw technology as potentially making us more passive and inclined to let machines do the thinking for us, thus making us even more inclined to abrogate our responsibility to think and act on our own as individuals, thus debilitating true democracy and meaningful discourse. On the other hand, the same technologies have given everyone a voice, which can be used for benefit, despite inherent dangers. Whatever the truth, the revolution has occurred, whereby we now merge with our technologies in a new kind of partnership in how we carry out daily activities. This same partnership is becoming intrinsic to education and, in our case, specifically to FLT. Above all else, it involves an ever-broadening outward reach away from strict classroom-based learning made possible by the technologically-shaped global environment in which people now interact and learn. It is relevant to cite McLuhan and Leonard’s (1967: 25) prescient assessment of a technologized world—an assessment that they put forth decades before the Internet: The world communications net, the all-involving linkage of electric circuitry, will grow and become more sensitive. It will also develop new modes of feedback so that communication can become dialogue instead of monologue. It will breach the wall between “in” and “out” of school. It will join all people everywhere. When this has happened, we may at last realize that our place of learning is the world itself, the entire planet we live on. The little red schoolhouse is already well on its way toward becoming the little round schoolhouse. Someday, all of us will spend our lives in our own school, the world. And education—in the sense of learning to love, to grow, to change—can become not the woeful preparation for some job that makes us less than we could be but the very essence, the joyful whole of existence itself.

In effect, the isolated classroom with a single teacher instructing and interacting with a small group of students in one specific region of the world is becoming more and more an anachronism in the current age. The shift from the traditional “walled-in classroom” to the “classroom without walls” has started to gain momentum. The partnership between humans and machines is

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defining the classroom more and more, with both, being needed to enhance learning and teaching in ways that were unimaginable in the not-too-distant past.

Epilogue With the advent of the language lab, FLT had merged, for the first time, with a technology in an effort to solve the dilemma of FL-learning in a formal environment. This was followed by CALL-based pedagogy, which introduced computers into FLT. With the advent of AI the third revolution is well underway. The question of whether AI can truly make FL-learning successful in a formal environment is ultimately a moot one. Technology is how humans evolve socially and intellectually. As McLuhan (1968: 85) so aptly put it, we live in “a man-made environment that transfers the evolutionary process from biology to technology.” There are various kinds of ways in which technology extends human capacities: physical (the wheel extends the capacities of the foot for locomotion), intellectual (the alphabet extends the ability to record knowledge efficiently), symbolic (numeral systems extend the ability to count), mechanical (the printing press extended the use of writing), mnemonic (the computer allows for greater storage of information than single brains can ever expect to store), and so on. As we invent new and more powerful technologies, so too do we change our modes of knowledge-making, of understanding the world, of transmitting information, of interacting socially, and so on. In previous eras, the print book was the main tool for conducting knowledge enterprises and learning. It was a tool that humans knew how to use efficiently and effectively for various reasons. It also formed the basis of how we learned information and grasped knowledge. As the age of print gave way to the electronic age, and now to the AI age, the tools for knowledge-making and communicating have also changed, as has our concept of ourselves and especially our bodies. To cite McLuhan (1970: 180) again, “When the evolutionary process shifts from biology to software technology the body becomes the old hardware environment. The human body is now a probe, a laboratory for experiments.” The lesson to be learned from McLuhan is that social evolution is guided by the forces of technological innovations but that such innovations do not cut the chains in the historical chronicle that we ourselves have fashioned. And this is why humans are still crucial to social processes, including education.

Chapter 2

Foreign Language Learning Prologue The ultimate sign of successful FL-learning is bilingual competence—the ability to navigate meaning and communication equally or in large part in both the native language (NL) and the new language (FL). And the sign that this end goal has been achieved to greater or lesser degrees is the student’s capacity to translate accurately and in culturally-appropriate ways from the NL to the FL and vice versa. It is intriguing to note that the new chatbots are highly expert at translating, and for this reason their design and functions may offer relevant insights into FL-learning itself. As mentioned briefly in the previous chapter, translating from English to Italian using ChatGPT indicated to me that the AI was highly expert at doing so. What was particularly impressive was the chatbot’s ability to infer contextual meaning from my prompts. In a similar anecdote, Maxwell Timothy (2023), asked ChatGPT to translate the Spanish phrase, “Gracias por preguntar, pero estoy bastante seguro aquí.” The chatbot produced the following accurate translation: “Thanks for asking, but I’m quite safe here,” adding further information about other possibilities for translating the expression that varied according to Spanish social context. Anecdotes like this abound, all pointing to the expertise that ChatGPT shows in translating, seemingly knowing what items are contextually and semantically appropriate. Another relevant example comes from the online site Summa Linguae (summalinguae.com/translation/how-to-use-chatgpt-fortranslation/), where even a Chinese idiom is found to be no challenge for ChatGPT, which accurately translated “未雨绸缪” (wèi yǔ chóumóu) as “prepare for a rainy day,” and as an option it generated, “make provision against possible trouble.” The Chinese idiom does indeed convey the idea of taking precautions or making preparations in advance so as to prevent or mitigate future difficulties, emphasizing the importance of planning for potential challenges—a cultural concept imprinted in the idiomatic expression. Further navigations throughout the Internet have revealed that ChatGPT has the ability to not only take cultural connotations into account in the translation, but regional-dialectal differences as well. Although postediting human-based interpretation is still needed in many instances, the fact

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that the AI is able to translate from one language to another so accurately is a rather remarkable achievement. As mentioned, this has implications for FL-learning theories, since the architecture of the chatbot may provide clues as to what language is and how we process it. This chapter will look at the traditional FL-learning theories in reference to chatbot architecture. One of the main challenges that FLT has always faced is to create conditions in a classroom that are conducive to a meaningful, personalized experience for students, leading to varied proposals in the past of how to synchronize teaching practices with the learning styles and emotional needs of the students (Witkin and Goodenough 1981). This implied, above all else, the use of pedagogical tactics matched to the particular learning situation. It is in this area that AI seems to have particular relevance, as will be discussed, given that as it is able to quickly analyze not only the linguistic behavior of the students, but also their level of knowledge and their needs, suggesting the most suitable pedagogical techniques to facilitate learning for each individual, as well as finding a middle ground that would apply to the class in general. Thus, AI presents itself, above all, as a support to teachers and students, not only in terms of enhancing self-learning, but also in terms of activities to be developed for the entire classroom. However, only the teacher can intuit the crucial emotional needs characterizing any situation and, therefore, to interpret more accurately what the algorithm proposes. According to research, for some types of learners, interactive exercises with an artificial system or exercises using a computer can be extremely stimulating (for example, Peterson 2013). Other students learn better, however, via social interaction within a real student work group. It is up to the teacher, therefore, to determine how AI could be used in specific ways to maximize the learning experience and outcomes.

Theories and Models Since the Direct Method (chapter 1), a major objective of FLT has been to find ways to activate the brain’s innate learning mechanisms and steer them towards the native-like acquisition of the FL as effectively and efficiently as possible. One of the central features of the early language lab was, in fact, to expose students to native speech, even if the drills were controlled pedagogically and thus somewhat artificially. Given the implications that AI systems such as chatbots now harbor for enhancing and facilitating FL-

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learning, it is useful to go over some of the major theories and models of the past here in a schematic fashion, so as to assess how the use of AI fits in with them. In many of the previous models, there is a central premise—namely, that students must be involved directly in processing FL input via exposure to authentic materials, speech samples, instruction, and exercises. This is based on a view of FL-learning that goes back considerably in time, whereby students were expected to be participants, not just observers in the learning process. This was, in fact, the implicit goal of the reformers at the end of the nineteenth century (chapter 1), who turned to psychology to gain insights into how learning occurs in the brain and then translating these into classroom pedagogical models. The belief was that the students would feel included in the learning process psychologically, rather than being treated as passive recipients of instruction, as was the case largely in eras before the reform movement, when pedagogy was centered on grammatical instruction in the student’s NL and then getting students to translate from the NL to the FL and vice versa—a method that was designated “Grammar-Translation” by the reformers. Ever since, the sciences of psychology and linguistics have been enlisted to both understand FL-learning and to derive from the relevant research effective classroom pedagogy. So, trends in both these theoretical domains have directly influenced trends in the pedagogical domain. The question now becomes: How do the uses of AI transformer models in the language classroom fit in, if at all, with the traditional theories of FL-learning? In order to attempt an answer to this question, it is useful to outline the major models here that may still have some resonance as to how FLs are acquired even today.

Interlanguage Theory One of the most widely-used models of FL-learning is Interlanguage Theory, introduced by Larry Selinker in 1972, which is a derivative of Transfer Theory (chapter 1). Like the latter model, Interlanguage Theory is based on the common observation of the constant emergence of systematic errors that students make due to interference from the NL (negative transfer). To this view, however, Interlanguage Theory adds errors that result, not only from the NL, but from general processes of overgeneralization. The previous errors are called interlinguistic and the latter intralinguistic. As an example of an interlinguistic error, an English-speaking learner of Italian typically adds the

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preposition per (“for”) to the Italian version of “I am waiting for the bus” = “Aspetto per l’autobus”; instead of the native Aspetto l’autobus, without the preposition. In this case the NL syntactic structure is transferred to the FL, producing the error (see Figure 2.1):

NL FL

I  Io

am waiting  aspetto

error source  for  per

Mary  Maria

Figure 2.1. Interlinguistic Transfer.

Figure 2.2. Interlanguage Theory.

Now, an error made by students of English as a foreign language such as “She goed to the store” (rather than “She went to the store”) is intralinguistic, since its source is not interference from any NL form, but rather the result of the tendency to assume regularity by the process of analogy with other forms in the relevant verb category, where the suffix -ed is used to indicate past tense—she played, she worked, etc. The consistency of such non-random errors in students’ efforts to speak or write the FL produces an “interlanguage” that defines learner speech—a kind of “student dialect” of the FL that is specific to non-native students of the language, whose characteristics result from an overlap between errors due to NL transfer and errors due to processes of analogy-based overgeneralization. The errors are seen as evidence that learners are constructing an unconscious preliminary theory of the forms and

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uses of the FL based on the linguistic data to which they have been exposed, on their way to acquisition of the FL (see Figure 2.2 above). These two error types give an interlanguage its unique linguistic organization. An interlanguage can become fossilized, that is, become the actual form of the FL that the learner takes away from the classroom. Interlanguages can vary according to learning context, and especially the type of experiences that students bring to the FL-learning task. As mentioned, Interlanguage Theory is an elaborated version of Transfer Theory, claiming that both NL transfer and “natural” learning mechanisms, such as analogy, are involved in shaping FL-learning (Dulay, Burt, and Krashen 1982). The Direct Method, on the other hand, did not envision any impact of the NL on the learning process; rather it believed that FL and NL learning were essentially isomorphic; that is, the same psychological mechanisms and processes enlisted by the child in acquiring the NL were involved in FL-learning. In Interlanguage Theory, these mechanisms were seen as manifesting themselves as intralinguistic errors. Initially, as briefly discussed, the DM was a reaction to the Grammar-Translation method that emerged as far back as the Renaissance. The late nineteenth-century reformers saw the emphasis on grammar training as an obstacle for the typical student, since it did not provide access to the language itself directly, and thus posed an impediment to many students, since it required a background knowledge of grammar itself. The DM was tailored instead to reflect how children learn their NLs, using techniques such as the following (Richards and Rodgers 1986: 7-8): • • • • •

All instruction and classroom activities were carried out in the FL, at least as much as possible . Vocabulary was imparted not through translation, but through gestures, pictures, etc. Every lesson began with listening and imitation activities revolving around a dialogue. Pattern practice drills followed, from which the learner was expected to induce the relevant rules of grammar inherent in them. Reading and writing activities were delayed till after the acquisition of listening and speaking skills.

These techniques were believed to simulate the inductive and generalization processes involved in NL acquisition (chapter 1). However, there were doubts from the outset about the validity of this learning model, as

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mentioned. After the demise of the DM, language educators started to look more closely at the effects of the students’ NL habits on the learning process. This led, as discussed, to Transfer Theory and the Audio-Lingual Method. The origins of Transfer Theory can be traced, as discussed, to the key work of Charles Fries (chapter 1), who saw the persistence of regularly-occurring errors that students typically made as traceable to differences between the NL and the FL. So, identifying which features of pronunciation, grammar, and vocabulary were the same or different entailed two premises: (1) teaching should start with features that the FL shared with the NL, given that the learner was already familiar with them, and would thus purportedly acquire them automatically; and (2) those that differed would instead receive much more instructional salience later on in the pedagogical process. In this way, positive transfer could be maximized and negative transfer minimized through pedagogical means. Of special relevance to the discussion here is that most of the early theories saw FL-learning as an inductive process. In the case of the theory underlying the DM, induction was claimed to be similar to the process shown by children in acquiring their NL; in both Transfer and Interlanguage Theory, the errors produced by students were seen as derived in some part from the incorrect induction of pattern on the basis of the given pedagogical input. Now, the relevant aspect of AI language models is that they too are trained to induce patterns from large databases. The relevant program can then extract and use the relevant information as required by an input request. In effect, AIlanguage learning matches what is known about human induction, but in an artificial-algorithmic way. The simplest learning algorithm, in fact, receives a set of examples drawn from the language in question, from which a pattern is established by the model that can be applied to similar data. This is not unlike the pattern-practice concept of both the DM and the ALM. When the AI algorithm generates a hallucination, it reflects the same kind of overgeneralization process characterizing interlanguages. Without going into the technical details here, the point is that induction and generalization are intrinsic to AI learning systems, including the ChatGPT one. In an indirect way, the method used to train AI language models and the kinds of hallucinations these make are consistent with theories of human foreign language learning, suggesting that these may be inherent in any learning system—human, animal, and machine. Specifically, in machine-learning systems, a generalization error (hallucination) is a measure of how accurately, or not, an algorithm is able to predict outcome values for previously unseen

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data—a process that is remarkably similar to human learner efforts in guessing what FL forms and uses may be like without having learned them formally. Needless to say, there is much more to learning in humans than the foregoing discussion. Of special pedagogical relevance is that ChatGPT (and other chatbots) can easily identify interlanguage errors and then provide students with explanations as to the source of the errors and subsequently design exercises and other materials that can help redirect the FL construction process away from its interlanguage phase towards a more native-like understanding of the language and its uses (Algaraady and Mahyoob 2023).

The Input Hypothesis The prompts and input data that ChatGPT receives guide its ability to perform tasks in a language. The notion of input has, actually, always been a central one in FL-learning theories, albeit from a different perspective. The DM, for instance, constrained the input to be similar to the kind to which children were exposed as they acquired their NL, gradually becoming more complex as FL learners acquired competence. There has even been an entire model developed on the view of the crucial importance of input—namely, the Input Hypothesis, developed by the American applied linguist Stephen Krashen (1982, 1985). Aware of the differences between childhood and adult (adolescent) situations, due to the different environment, ages, and background knowledge, Krashen claimed that there existed a neurological difference between acquisition and learning—the former characterizes both NL and FL processes in the early stages, and is spontaneous and unconscious, while the latter involves conscious processing of the input and is guided by instruction and other formal pedagogical systems. Krashen maintained that it was crucial to ensure that the input to which students are exposed with regard to any novel task is conducive to acquisition, and thus that it activate spontaneous learning mechanisms in the brain. Learning-based input, on the other hand, involves the activation of conscious reflection on the language, which occurs when students know enough about the language to be able to reflect upon it formally, which Krashen calls monitoring. The distinction between acquisition and learning continues to have relevance because it encapsulates something that teachers have always felt intuitively—namely that students pick up certain things spontaneously and with little effort, but require much more conscious effort and focus to grasp other things. A part of Krashen’s model is based on the ideas of psychologist

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Lev Vygotsky (1962), including his “i + 1” characterization of acquisition, which corresponds to Vygotsky’s notion of “zones of proximal development.” This means that children and early FL learners progress through zones of development which expand on their own as they are able to understand new input by themselves—hence “i (input) + 1.” Krashen (1985: 1) saw classroom learning as unfolding through a natural sequence, which “does not appear to be determined solely by formal simplicity;” nor is it dependent on “the order in which rules are taught in language classes.” Now, for input to be able to instantiate acquisition processes, it must be able to lower what Krashen called the “affective filter,” a mental block that prevents students from fully acquiring the input to which they are exposed because of either fear of making mistakes or else of resistance to new information that seems too complex or abstruse. The primary strategy in getting acquisition to unfold naturally, lowering the affective filter, is to ensure not only that the input to which learners are exposed is understandable, and that it contains “a bit” of information that is beyond the student’s developing competence (Krashen 1985: 2): The Input Hypothesis claims that humans acquire language in only one way—by understanding messages, or by receiving “comprehensible input.” We progress along the natural order by understanding input that contains structures at our next “stage”—structures that are a bit beyond our current level of competence. We move from i, our current level, to i + 1, the next level along the natural order, by understanding input containing i + 1. We are able to understand language containing unacquired grammar with the help of context, which includes extralinguistic information, our knowledge of the world, and previously acquired linguistic competence.

Judiciously, Krashen claimed that his views were based on anecdotal evidence and on pedagogical tradition, rather than on strict empirical research. Nonetheless, the notion of input resonates with FL teachers. Significantly, Krashen’s ideas have parallels in Generative AI research, where the notion of “attention” seems to correspond, grosso modo, to Krashen’s notion of monitoring. Transformers, such as ChatGPT, use attention to “weigh” the influence of different words when generating a response. For instance, while generating a response to the sentence “The cat chased its tail,” the model appears to understand that “cat” is the subject and is more important than “tail.” To allow it fine-tune its attention system, the AI model is exposed to a

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large corpus of data from the Internet, allowing it to induce statisticalprobability patterns of actual language use. For instance, since a phrase such as “The sky is …” is often followed by the word “blue” in the training data, the model will learn to predict “blue” as the likely next word. In a way, this is the AI’s “i + 1” mode, whereby it chooses each word based on probabilities derived from assessing the input. Interestingly, ChatGPT sometimes introduces randomness in its word selection process coming up with truly creative responses, again much like students in a FL course, even inventing things out of the blue (so to speak), as its hallucinations show.

Universal Grammar Theory Starting with the early ideas of American linguist Noam Chomsky (1957, 1965, 1986) a view of language emerged, called Universal Grammar Theory, that was adopted by some applied linguists and FL teachers as a model to design instruction and classroom materials (Jakobovits 1971, Chastain 1971). Chomsky asserted that human infants possessed a species-specific language capacity in the brain, which he initially called the Language Acquisition Device, that allowed them to spontaneously develop their native-language grammars by simply being exposed to verbal input. The capacity was based on the belief that all languages are designed with the same innate blueprint, differing only in fine-tuning details (called parameters) that specific grammars require. The blueprint is the Universal Grammar (UG), consisting of a set of principles of language design wired into the brain at birth that are constrained by the specific parameters that characterize the grammar of the particular language to which children exposed. UG Theory is an attempt to account for the fact that childhood language acquisition follows distinct stages universally, no matter where a language is acquired or what that language is. Differences are due to organizational subprinciples (parameters) that allow a specific language grammar to take shape from the fund of general principles in the UG. But UG Theory ignores two crucial facts of childhood language acquisition: (1) it overlooks the critical role played by imitation; and (2) it excludes the role of processes such as analogy and metaphor in shaping the learning process (to be discussed subsequently). While the relevance of UG Theory for FLT has been debated since it came onto the scene, it actually brought about a rethink in how to model teaching practices on the basis of a linguistics-based learning theory. As semiotician

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Charles Morris (1938, 1946) argued, one cannot isolate any one dimension of language in any scientific consideration of its nature. There are three main intertwined dimensions that coalesce to create language as a semiotic system: (1) syntax, the organization of words into larger structures; (2) semantics, how words in isolation and in syntactic frames deliver meaning; and (3) pragmatics, how language is used context and how this influences its forms and meanings. This tripartite model is inherent, significantly, in the design of chatbots, since a computer will be able to determine the semantics of, say, an ambiguous word on the basis of the words surrounding it in different texts (syntax) and in their actual pragmatic uses. One area in which this is saliently evident is in how computers assess collocations, sequences of words that typically co-occur in speech more often than would be anticipated by random chance. Collocations are not idioms, which have fixed phraseology. Phrases such as crystal clear, cosmetic surgery, and clean bill of health are collocations. Whether the collocation is derived from some syntactic (make choices) or lexical-semantic (clear cut) criterion, the principle underlying collocations—frequency of usage of words in tandem—always applies. And it is this principle that undergirds chatbot design. First, the algorithm identifies a key word in context and then determines the frequency of the combination of other words with the key word in order to disambiguate the meaning of a phrase. Chatbots are constructed on the basis of the above tripartite model, providing insights on how the internal mechanisms of a natural language may be activated with novel inputs—hence, their theoretical value to designing or refining FL-learning theories and models. Specifically, the work in this area has become a highly relevant one on at least four counts: 1. It forces FL researchers to unravel the relation between structure and meaning in the formation of even the simplest sentences and how they relate to external information. 2. It produces machine-testable models of meaning that can then be discussed vis-à-vis the theoretical models of linguists. 3. It brings out the relation between grammar and context of use, and how it might be modeled. 4. It allows the linguist to relate language to knowledge, and specifically to how knowledge is represented linguistically.

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Pragmatic Models In the 1970s, FLT started moving away from its theory-to-practice paradigm moving instead towards the serious consideration of how to incorporate pragmatic-communicative features of language into classroom teaching. It was the linguist Dell Hymes (1971) who ignited this shift when he argued convincingly that language systems were hardly impervious to influences from real-world social interaction, suggesting that, on the contrary, verbal structures were even modified themselves over time by language use. He argued overall that the ability to apply language to specific situations in a meaningful-systematic way constituted a different kind of competence from pure linguistic competence, calling it communicative competence. Actually, as far back as the work and ideas by Harold Palmer (1922), the notion of a communicative, or pragmatic, competence was already fomenting in the domain of FLT, as witnessed by the various oral methods that surfaced after World War I, based on the idea that oral fluency would not emerge without teaching communicative aspects of the new language directly. By the mid-1970s pragmatic models started becoming broadly incorporated into FLT (Van Ek 1975, Wilkins 1976). Their organizing principle was to teach how native speakers employed language forms variably, yet systematically, to carry out specific types of social functions. A simple protocol such as saying hello or good-bye, for instance, is based on an implicit social script that requires a detailed knowledge of the appropriate words, phrases, structures, and nonverbal cues that come together cohesively to enable a speaker to carry out a successful interaction with another speaker. An infringement of any of the procedural details of this script might lead to a breakdown in communication. The topic of communicative competence will be discussed in chapter 4. For the present purpose, suffice it to say that it is based on the view that knowing how to use language during conversations is as systematic as knowing the grammar of the language being employed. Already in the early 1920s, anthropologist Bronislaw Malinowski (1922) saw language as fulfilling and serving specific social functions. For example, he coined the term phatic communion in reference to the exchange of words and phrases that are important less for their dictionary meanings than for establishing social contact. When we greet someone with “How are you?” we hardly expect a medical report, as would a doctor. The typical function of that statement is simply to make contact. Malinowski also showed that the type of language used in phatic communion could be used by the linguist to identify social

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variables—if the speakers are adults or children, if there is a difference in status between the speakers, if they are well acquainted with each other, and so on. Phatic communion is norm-based behavior. As work on pragmatics became widespread in the 1970s, Malinowski’s views were expanded to study all contexts and situations of language use. One area in which chatbots are of particular relevance is that they constitute a kind of pliable language lab, having the ability to replicate real speech and conversations with accuracy. Moreover, a chatbot can respond to prompts by students with personalized, relevant information, allowing them to practice conversations minus the anxiety they may feel when talking to a person. The chatbot also has the ability to adapt to the level of the conversation, helping students identify and fix mistakes not only in grammar but also in pragmatic appropriateness. In effect, ChatGPT is a self-contained pragmatic model of language teaching, serving as a conversation partner, at any time and at any place. An example of how ChatGPT can be used to carry out a personalized conversation in French is provided by Edwige Simon (2023), showing how the simulation is truly verisimilar to real conversations: Prompt:

ChatGPT:

S’il vous plaît, posez moi des questions sur ma famille personnelle et professionelle, une question à la fois (“Please ask me a few questions on my personal and professional family, one question at a time”). Je comprends. Voici une question concernant votre famille personnelle: Peuvez-vous me parler un peu de votre famille? Combien avez-vous d’enfants et quelles sont les choses que vous appréciez le plus dans votre role de parent? (“I understand. Here are questions concerning your personal family: Can you talk to me a bit about your family? How many children to you have and what things do you appreciate the most in your role as parent?”).

The interaction is remarkable on several counts: (1) it shows accuracy in vocabulary selection; (2) it employs appropriate grammar; and (3) it uses the correct level of formality (showing the use of the polite vous forms). However, as discussed in the previous chapter, while the chatbot can simulate a real-life situation, achieving communicative goals in a socially appropriate manner, it cannot provide the human perspective of events, which implies a true understanding of the relative importance of the events. AI is often not

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sophisticated enough to understand cultural differences in expressing and reading thoughts and emotions, making it harder to draw accurate conclusions.

Learning Variables One of the most important advantages of AI in FLT is, arguably, its ability to adapt to individualized student styles of learning and to tap into learner motivations for studying a language. In most pedagogical models, the learning styles of students have been assumed to be critical as predictors of the best teaching method to use, as was their motivation for enrolling in a course. Some learners learn best through visual materials, others through audio forms of instruction, others still by studying alone, and so on. People learn in different ways and at different speeds, and their incentives to learn may differ considerably. These variables cannot be ignored in the design of any teaching approach or curriculum—as did methods such as the DM and ALM to varying extents. It has become obvious that it is impracticable to expect everyone to learn in the same way with the same textbook and materials and be equally successful. This is where chatbots come into the picture, since they make personalized approaches practicable, while at the same time allowing for a “common pedagogy” to be used in certain situations. Chatbots adapt to learners’ needs and respond to teacher requests for ideas and materials that relate to a specific situation. Teachers can also upload content into an AI system, which can then generate textbooks tailored to a specific curriculum, course, or primary learning style of students.

Motivational Factors The two main motivational factors identified by research as affecting success in FL learning are called integrative and instrumental (Gardner 1985). The former refers to the desire on the part of students to integrate with the speakers and culture of the language they are studying; the latter refers instead to the desire to study a language in order to accomplish a task, such as passing a course or enhancing career opportunities, among other practical goals. With various modifications and elaborations, current studies confirm that these two factors are still significant as theoretical frameworks for assessing learning

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outcomes and designing teaching approaches (for example, Hong and Ganapathy 2018). A study by Khan and Takkac (2021) found that the instrumental factor is still a powerful one, no matter the students’ backgrounds, with the desire for career and economic advancement constituting a major incentive for students to enroll in a language course. Another factor identified by the researchers was the desire on the part of students today to become global citizens, whereby the language is seen as an entry point into such citizenry, reshaping the integrative factor somewhat. Interestingly, the researchers also discovered that the ways in which the language was taught would impact motivation. And this has clear implications, since in a complex world built on ever-changing technologies, it cannot be assumed that the historically relevant methods will be motivational stimuli for attracting students to a language and for facilitating learning success. The main drawback of the methods of the past (and even the present) is that they require the teacher’s and student’s total commitment to their specific pedagogical philosophy. The mid-1980s Proficiency Movement in the United States led to much discussion and interesting spin-off research on how to integrate different methods and approaches (Omaggio 1986), stressing integration and eclecticism. It is within this integrated framework that AI can become a pivotal aspect of FL learning and teaching.

Enhancing Motivation A key feature of AI is, as mentioned, the personalization of the learning experience and thus the tailoring of the teaching process to be consistent with varying learning styles. In a typical classroom situation, it is almost impossible for teachers to find an approach or materials that suits one and all. With AI, on the other hand, the specific needs of each individual student can be fulfilled outside the classroom, since the chatbot can be used as a virtual assistant. Moreover, AI systems allow for the collection of relevant data about learners, their abilities, and their styles. This can then be used not only to predict future performance but also to make effective individualized (student-centered) education truly possible. In effect, AI-supplemented FLT allows students to work at their own pace, at the same time that it allows them to engage with others using the same systems, without fear of embarrassment. AI can repeat topics, provide learners with tasks that they are capable of completing successfully, and even take factors such as student cultural background into account. AI-based platforms can also grade tests and evaluate essays

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automatically after submission, pinpointing errors and how to fix them. Students can thus take immediate action to correct their mistakes and avoid them in the future. In this way, their interlanguages may be less and less of a factor in shaping FL-learning. A study by Zhai and Wibowo (2022) found that chatbots had the capacity of establishing and maintaining the interest of students, given that they had the capacity “for dealing with learners’ emotional discomfort, the impact of humor and the consideration of learners’ cultural backgrounds.” They also found that chatbots have the ability to provide feedback to teachers for identifying how learners perceive and react to the learning content.

Machine Learning The traditional theories and models of FL-learning are not mutually exclusive; they provide complementary bits and pieces of what may be occurring in the brain as students learn to speak another language—that is, Transfer, Interlanguage, Input, motivation, and pragmatic theories furnish diverse angles from which to envision how students learn in classroom environments. Research in so-called Machine Learning (on which chatbots are built) has come forward to both confirm some of the ideas in these models, as discussed implicitly in this chapter, but also to suggest different angles from which to view FL-learning and how to approach different learning styles. Machine Learning is based on the algorithmic mining of large databases. The idea is to simulate the human use of real-world information in resolving polysemy (the different meanings of the same lexical items), allowing the algorithm to infer the appropriate meaning on the basis of probability metrics. The algorithm searches for analogous or isomorphic forms and converts them into options for the system. The details of how this is done are rather complex; and they need not interest us here as such. Suffice it to say that the computer modeling of linguistic meaning involves mining data from millions of texts on the Internet, analyzing them statistically in terms of syntactic-semanticpragmatic categories. N-gram modeling is used to predict the next item in a sequence in a chain. The idea goes back to the founder of information theory, Claude Shannon (1948), who asked the following question: “Given a sequence of letters, such as the sequence “for ex…,” what is the likelihood of the next letter?” A probability distribution to answer this question can be easily derived given a frequency history of size n: with the letters a = 0.4, b = 0.00001, …, summing

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to 1.0. In the model, the probability of a word is computed by determining the presence of a certain number of previous words and their phonemic structure. This seems to reflect what humans do when “searching” for the right word or phraseology. So, the next likely letter is a, since the sequence “for ex…” is found mainly in the phrase for example. Of course, it could be some other vowel, as in for exhilaration; but the frequency is extremely low in this case, and moreover, the algorithm is able to determine the input context on the basis of its analysis of its probable meaning. Overall, Machine Learning gauges the fidelity of a possible sequence on the basis of the following: 1. the position of a word or words in a large number of texts; 2. the linguistic features typically associated with the topic or theme of the text, which involves specific kinds of grammatical and lexical choices; 3. syntactic considerations involving the likelihood that a certain structure will follow or precede others.

Figure 2.3. Machine Learning (Wikimedia Commons).

Incredibly, contemporary learning algorithms have the ability to “discover” their “own” algorithms, without needing to be explicitly prompted as to what to do by any human-developed algorithm. As a scientific endeavor, Machine Learning grew out of the early quest for AI and became embedded in Deep Learning technologies, which train computers to process data in a way that mirrors the human brain. Deep Learning models can recognize complex patterns in pictures, text, sounds, and other data to generate accurate insights and predictions. So, Machine Learning is really a subfield of Artificial Intelligence and Deep Learning a subfield of Machine Learning (see Figure 2.3).

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Chatbots Chatbots are products of research in the AI-Machine Learning-Deep Learning integrated research paradigm. It is useful here to review the main kinds of features that they make available for FLT purposes (to be discussed in more detail in the remaining chapters of this book): •

• •

•

•

•

The chatbot can recognize the student’s-teacher’s speech patterns. It can thus automatically create comprehension, reading, writing, and speaking tests and activities for users as well as grade and categorize user responses according to proficiency level. Chatbots use Deep Learning tools to remind users of words and phrases that they may require and have difficulty recalling. With data mining from public servers, chatbots enable live, realworld conversations with human users. Some use virtual reality (VR) plugins to allow the students to immerse themselves simulatively into a real-world FL cultural context. Chatbots provide personalized content and practice activities. They are virtual teaching assistants helping both students and teachers determine what activities and instructional practices are best suited to a given situation. Because of Deep Learning, chatbots are “learning companions,” having the capacity to become increasingly familiar with users’ speech and stylistic patterns over time. As such, the chatbot offers a level of personalization that human-based teaching cannot possibly achieve. Chatbots provide instant feedback, adapting to individual needs. They can also help the teacher create customized textbooks.

Voice recognition technology can also play a role, since it enables the chatbot to understand and analyze the learner’s oral speech. Like the language lab, therefore, the chatbot provides opportunities for learners to develop a more authentic pronunciation and improve their overall listening and speaking skills, outside of any specific lab setting. Another aspect of chatbots is their ability to generate appropriate games in which students can become involved for learning purposes.

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Now, while AI has made significant progress in aiding and enhancing language learning, there are still limitations to the technology (at the time of writing this book). Chatbots may struggle to understand complex language nuances, newly-formed expressions, and cultural context. Moreover, one cannot overemphasize the importance of human-to-human interaction in the learning process. Engaging with other human beings in conversations, and immersing oneself in the FL culture physically (as in immersion programs), can provide invaluable learning experiences that AI cannot fully replicate.

Conceptualization One of the pivotal concerns of all traditional theories of language learning is the role of conceptualization—that is, how to grasp the concepts of the FL culture through the language—an aspect that Robert Lado was already emphasizing as far back as the 1950s (Lado 1957, 1964). Different languages encode concepts often in different ways. The more distant the cultural and historical relation between languages, the greater the conceptual differences between them. Although people may see the same rainbow, the number and range of the rainbow’s hues they can name will depend on how many color terms have been encoded by the languages they speak. Some languages have everyday words for a dozen colors; others can get by with only a couple. Cultures leave uncoded those aspects of reality that they consider unimportant to them, encoding only those that are important. For example, given the world they inhabit, the Canadian Inuit have had little interest in distinguishing hues for different types of plants; what they needed, on the other hand, were words to talk about different kinds of snow. That is why in some Inuit language more than 50 words referring to ice and snow are still in use. Leaving aside the controversy surrounding this view of language, suffice it to say here that it resonates with students and teachers alike as they grapple with different concepts (see Danesi 2021). At the very least, the question that the foregoing discussion raises is as follows: Can chatbots interrelate the new language to the conceptual system of the culture that uses it? Consider, as a simple case-in-point, the difference between orologio in Italian and watch and clock in English. The Italian and English words refer at a literal referential level to “a mechanical device for registering the passage of time.” But in English the two words call attention to the “portability” of the device as well—watches are worn or carried, clocks are put on tables, hung on walls, etc. No such attention is necessitated by the word orologio. The notion

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of “portability” is not relevant in the use of orologio, but that of “location” is (table clock, wrist watch, etc.). In Italian this is conveyed by da + “location”: for example, orologio da tavolo “table clock,” orologio da polso “wrist watch,” orologio da muro “wall clock,” etc. Now, chatbots seem capable of contrasting languages at this level. It becomes a little more problematic to get them to conceptualize abstract concepts such as justice and love, however. This will be discussed in detail in chapter 5. Overall, the work in Machine Learning is yielding some truly remarkable computational feats, which are of obvious relevance to assessing and validating FL-learning theories and models. As Klimová and Seraj (2023) found in their review of the relevant literature in this ever-broadening area of research, it is becoming ever more apparent that AI has increasing potential “in applying and integrating the existing theories and concepts used in EFL teaching and learning.”

Epilogue As Max Black (1962) pointed out at the start of the AI revolution, the idea of trying to discover how a computer has been programmed in order to extrapolate how the mind works has borne great fruit for the study of mind itself. But Black also expressed a caveat, namely that computers can never truly be intelligent in the human sense because the laws of nature will not allow it. Nonetheless, whether a computer system such as a chatbot is aware of what it is doing intelligently, it is an obvious aid in helping us understand what the brain does when it is processing language via simulation. AI systems now have the capacity to learn by themselves, and it is this new autonomy that is shedding even more light on our own brains. Since the Reform Movement of the nineteenth century, the goal of FL research has been to examine the ways in which we learn languages naturally and then translate the findings into relevant pedagogy. AI clearly provides a further possibility in this area. The essential problems of FL-learning apply to computer programs as well and can be broken down into four components: (1) what to say or write in certain situations, (2) when to do it, (3) how to do it, and (4) what to expect realistically at different learning stages. The central objective of the Reform Movement was to unify pedagogical practices by developing a standardized curriculum as the consolidating framework for these. AI is now an emerging, different way of envisioning FL-learning— based on adaptability to individual needs within the context of classroom

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pedagogy. As such, it needs serious consideration, otherwise FL teachers will be left behind as were the teachers using Grammar-Translation as the Reform Movement gained momentum and spread broadly.

Chapter 3

Developing Linguistic Competence Prologue Whatever school of teaching philosophy to which one might subscribe, there is consensus on what the main competencies required to use any language are. One competency is the ability to control the structural components of a language cohesively—its phonological, grammatical, and semantic systems— called generally linguistic competence. A second is the ability to use the language systematically and fluidly for expressive, interactional, and other kinds of pragmatic functions—called communicative competence. A third involves the ability to think with a language, and to perceive events in the world in terms of its classificatory-conceptual structure, an ability that can be called conceptual competence (Danesi 2017). The purpose of this chapter is to zero in on the potential and actual pedagogical uses of Generative AI apps, such as ChatGPT, to promote the learning of the three main skills associated with the development of linguistic competence in the FL—namely, the ability to use its phonology, grammar, and vocabulary correctly and effectively. This breakdown of skills is, of course, artificial, given that the three components are interactive in all speech and learning events. Their separation here is simply for convenience of exposition and discussion; however, given the nature of chatbots, not to mention natural language understanding itself, the integration of the three skills is hardly tangential to the discussion and will come up constantly. ChatGPT and similar chatbots are of importance not only to students as self-learning tools, constituting essentially interactive and highly versatile language labs on a computer, but to teachers as well, since they can be used to generate appropriate course content in a short time. For example, ChatGPT can produce word lists that are related to a theme, as well as tests and assignments. It can also customize instructional notes for the course, creating a self-contained textbook. Consider an example provided by the website ZenGengo (2023), which is worth repeating here as an initial case-in-point. The objective is to teach Spanish-speaking students of English aspects of English pronunciation that they normally find difficult. In the past this could have been done with prepared recorded material based on pre-selected word

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lists in a language lab (traditional or digital). This meant that the teacher had to conform to the contents of the pre-established drills. However, with ChatGPT, the teacher of English can produce personalized and task-relevant word lists by simply asking it to come up with a list based on sounds that Spanish speakers typically find problematic pronouncing. With its ability to mine huge databases, ChatGPT can easily come up with words that pose typical difficulties for such students, in effect, carrying out its own Contrastive Analysis for a specific pedagogical objective. Here is the example (ZenGengo 2023). The prompt on the site asks the chatbot to produce words whose sounds Spanish speakers have difficulty pronouncing: ChatGPT: Here are some examples: 1. Three 2. Think 3. This 4. The 5. Voice 6. Work 7. Ship 8. Vision 9. Cat 10. Cup Without going into the details here, suffice it to say that ChatGPT identified (on the site) the main problematic phonemes, as for example, the English interdentals produced by putting the tongue between the teeth (the [] in think, the [] in this), as well as vowels such as [æ] in cat and [i] in ship. I asked ChatGPT to come up with a similar list for English-speaking students of Italian and found a consistency with the Spanish output. The chatbot was in fact able to recognize even the minutest differences between English and Italian phonology that would commonly cause pronunciation difficulties for learners. For example, it identified the pronunciation of syllable-final /l/ as a velar sound ([ɫ]) on the part of English speakers (as in kill and bill) as an area of potential difficulty, given that in the pronunciation of Italian words, the /l/ occurring in the same position is pronounced as non-velar ([l]). So, the chatbot suggested exemplary words such as quella and bella, which would allow students to hear and then practice the different articulations of the /l/ in Italian.

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Pronunciation and Spelling What is remarkable about the word lists provided by ChatGPT is the accuracy of its phonological analysis and the pedagogical value of the words. Essentially, it can be said that there are two teachers involved here—the human and the machine, cooperating (so to speak) in coming up with appropriate material for a specific area of FLT. This recalls, in a strange new way, a form of partnership teaching that goes back to the 1940s, called the Army Specialized Training Program, which employed two teachers in its pedagogical system. One was a native speaker of the FL who would introduce dialogues and conduct exercises and pattern practice drills in the FL. The other was a linguist who provided scientific explanations of the linguistic features of new material. The chatbot is both a drill master and linguist, forming a partnership with human teachers who will prompt it accordingly. In the area of pronunciation training, the chatbot itself, with a voice plugin, constitutes an adaptive, sophisticated language lab, with which students can interact by prompting it for appropriate words and pronunciation exercises, receiving feedback from the machine teacher in real-time as to how they are speaking and how they can improve their pronunciation, free of the fear of the implicit human judgment that they may sense for their incorrect speech. As Godwin-Jones (2022: 125) observes: Their [chatbot] use has been shown to be effective for novice learners, who are able to practice pronunciation and basic conversations. The systems can serve as models of expert speakers, and, compared to humans, possess infinite patience, allowing for extensive trial and error without judgment. In that way, they have been shown to help overcome anxiety and encourage a willingness to communicate.

It is remarkable to note, as the word lists and related analyses produced by ChatGPT (above) indicate, that the AI has the capacity to conduct a Contrastive Analysis (chapter 1) on its own, which is built clearly into its choice of words in a list for pronunciation practice purposes. As such, this feature of chatbots is of great pedagogical relevance because, as Stern (1983: 46) aptly put it: “Contrastive Analysis was not intended to offer a new method of teaching; but it was a form of language description which was particularly applicable to curriculum development, the preparation and evaluation of teaching materials, to the diagnosis of learning problems, and to testing.”

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Listening and Speaking The study of the sounds of a language and how they are structured to produce larger forms such as words falls under the rubric of phonology, as is well known. Phonology has been extended into the training of AI and specifically to the development of speech recognition AI. This involves devices, which are activated by the human voice, and have the capacity to interpret voice commands and convert them into physical or other kinds of actions, eliminating the need to input information manually. One of the earliest speech recognition devices was IBM’s “Shoebox” in 1962, which had the ability to recognize 16 different words. In 1996, the company then launched Voice Type Simply Speaking—a software that had a 42,000-word vocabulary. In recent years due to advancements in Deep Learning and big data mining, many speech recognition applications and devices have become available. These allow for the customization of the technology, from nuances of speech variants to name recognition. Among the new abilities of speech recognition systems, a relevant one is a software that trains a device to adapt to an acoustic environment and speaker styles (like voice pitch, volume, and pace). The vagaries of human speech have actually made development of speech recognition technologies challenging. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output. The point here is that modern speech recognition systems are constructed on the basis of various phonological models from linguistics, and this is relevant to the ability of a chatbot to carry out an implicit Contrastive Analysis. As I discovered myself (above), an AI system such as ChatGPT allows for students to interact constructively with it by prompting it to generate relevant word lists and derivative pronunciation drills, thus allowing for a customization of the listening-speaking aspect that is critical to the acquisition of the FL. As documented by the relevant research literature (for example, Alamer and Almulhim 2021), one of the main obstacles to successful FL learning is the anxiety that students might feel in attempting to pronounce FL words, which hampers the learning process because of the affective filter that students typically bring to the classroom situation (chapter 2). Chatbots can eliminate this obstacle, given that they allow learners to listen to new speech input over and over according to their particular needs, so that they can acquire the new pronunciation habits at their own pace. They allow the learners to pronounce

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new words repetitively as well, providing comments as to how to improve their pronunciation, without the anxiety that learners might otherwise feel with a classroom audience of peers and teacher listening to their efforts. Students are usually not conscious of how they listen in their own NL, unless they encounter external interferences, such as environmental noise. The tendency of students is to ignore the pronunciation features of the speech input of the FL as well, relying on the phonological habits of the NL, which is the source of typical pronunciation errors (chapter 2). We hardly realize how important listening is to language competency, as relevant studies indicate (for example, Lipari 2014). What was especially remarkable about the chatbot that I used was the accurate phonetic quality of its pronunciation, its use of native-like prosodicintonation patterns, the accuracy of its pauses, and the instantaneous responses with which it processed my input. These features are difficult to reproduce in the same way in a traditional classroom teaching situation (Brunfaut and Révész 2015). The voice control plugins that are available for ChatGPT allow students to speak directly to it, and it will in turn recognize and respond to the prompter, allowing for personalized practice in listening comprehension and pronunciation. Some plugins even provide options for adjusting the gender of the chatbot’s voice, and the speed with which it generates content, allowing users to adjust it to their learning styles and communicative partner preferences.

Writing The relation of writing systems to phonological ones falls under the rubric of graphemics. The term orthography is used as well, but the technical difference is that the former refers to the correspondences between phonemes and graphemes (writing characters) and the latter to the set of conventions for writing a language, including norms of spelling and punctuation. Most languages today have a system of writing. While the phonology of a language changes over time, its orthography tends to remain stable, and thus often does not reflect phonological changes. An example in English is the initial /kn/ cluster (as in knight, knot, know), in which the /k/ has been eliminated in pronunciation but which the orthography has not reflected, having retained the k letter. The /kn/ cluster was a phonological structure of proto-Germanic, from which English is derived, and which continues to be pronounced in modernday German (Knopf “button”). Orthography is clearly not governed by the

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equivalent of sound shifts in phonology; it is regulated by external factors, including practices related to literacy. And this is why there is so much confusion in some orthographic systems, such as the English one when taught as a FL—for instance, [f] stands for a voiceless labiodental fricative, while in writing it is represented in various ways: • • •

the letter f in words such as fish, fun, after, flow, infer; the digraph ph: in words such as phonetics, phrase, philosophy, phenomenon, triumph; the digraph gh: in words such as enough, rough, tough, cough, laugh.

Figure 3.1. A Page from McGuffey’s Reader (1836).

While exposure to spoken language in childhood does not require any intervention on the part of adults, since children construct the language on their own without training (as discussed briefly in chapter 2), learning how to read and write requires such intervention. In English, the notion of phonics as a method to teach reading was introduced in the nineteenth century. One of the first examples of a phonics reader is the 1836 one by American educator William Holmes McGuffey. Above is a page from the reader (see Figure 3.1). Given the vastly different orthographies that might be involved in FLT, such as for example the pictographic-based Chinese ones versus the Roman alphabet ones used in English and other languages, this is where ChatGPT can have specific pedagogical functions. In addition to providing feedback to students, the chatbot can also generate individualized exercises and exposure

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to reading materials in the FL types focusing on orthographic patterns that are specific to the FL. As Keir Williams (2023) has noted, ChatGPT has been used to effectively help students identify and correct errors in their written work, including spelling, grammatical, and vocabulary errors, while providing realtime suggestions for corrections. Again, students can express their needs without worrying about their individual mistakes, since the chatbot corrects them in real time. From this type of AI interaction, students can accomplish or observe the following: •

• • •

realize what types of errors they frequently make, so that they can work towards improving them over time, without any fear or shame in committing the errors; learn directly from their own mistakes, and thus be more inclined to utilize the spelling and grammar suggestions provided by ChatGPT; view the corrections as opportunities for learning; receive in-depth explanations of the needed corrections, aiding in the development of writing skills.

Finally, the ability of ChatGPT to transliterate texts in languages with different scripts (as for example, Chinese, Hebrew, Arabic, Korean, etc.), enables learners to practice reading out loud in the FL without having mastered the script at any given point in time, making it consistent with the Input model of learning (i + 1), whereby a task can be just beyond present abilities, but is still within learning reach (chapter 2).

Grammar Grammar differences between the NL and FL are the source of many interlanguage errors and learning difficulties. Asking ChatGPT to write, say, a paragraph using a specific grammar point, such as the subjunctive in French, requesting at the same time that it highlight the relevant forms by writing them in capital letters, as did Simon (2023), is an example of how personalized chatbot grammar instruction and exemplification can unfold outside the classroom. Here is Simon’s example, which constitutes an exercise providing students with a native-like paragraph that aims to make understanding the subjunctive much more pointed and focused:

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Write a paragraph using the subjunctive in French, and write all the subjunctive forms in capital letters. Il est essential que nous AGISSIONS maintenant pour protéger l’environnmement. Il faut que chaque individu PRENNE conscience de l’urgence de la situation. Il est crucial que les gouvernements PRENNENT des mesures concrètes pour réduire les émissions de carbone. Il est nécessaire que les entreprises ADAPTENT leurs pratiques pour être plus respectueuses de l’environnement. Il est important que nous COLLABORIONS tous ensemble afin de préserver notre planète pour les générations futures. Il est temps que chacun CONTRIBUE à un avenir durable.

Whatever theory of grammar one espouses, it is clear that the chatbot has the capacity to focus on specific needs and produce relevant output. As such, it has great value in grammar teaching, given that it can compare NL and FL grammatical points so as to bring out similarities and differences. When asked (by myself) to explain the reasons why the subjunctive was required in the paragraph above, ChatGPT put forth explanations that indicated how a verb or expression in the main clause triggered the subjunctive in the dependent clause. When asked to explain the difference between the two clauses, it gave a truly concrete explanation with further examples. It indicated in its responses that the main clause must have the relative pronoun que or qui to introduce the dependent clause (“Il est essential que nous agissions maintenant pour protéger l’environnmement”) and that the subject of the main clause and of the dependent clause must be different.

Words and Sentences One area that is highly relevant in FL-learning is word-structure recognition in the new language, that is, the ability to recognize how words are constructed with morphemes or lexemes—the former refer to any type of meaning-bearing unit with grammatical function, and the latter with lexical function, although the term morpheme often covers both. A way to conceive a lexeme is as a minimal free form—a form that can stand on its own. For example, the word logic is a minimal free form, because it conveys a single piece of meaning and cannot be broken down further. The form illogical is also a word, but it is

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composed of the same lexeme and two bound morphemes—the prefix il- and the suffix -al. The connection between words, how they are formed, and how they are used to create or insert into larger structures such as sentences was studied by philosophers already in antiquity. The Greek philosopher Plato wrote the following relevant comment in his Cratylus (Plato, Project Gutenberg 1999): “sentences are, I conceive, a combination of verbs and nouns.” Plato’s pupil, Aristotle, added another class of words to sentence construction, namely conjunctions. These are among the first ways to classify words in terms of syntactic categories, that is, in terms of how they function in sentences—thus laying the foundations for the notion of grammar, defined essentially as a set of rules that describe how words are organized in phrases and sentences. FL learners are faced with a daunting task, which Aristotle himself identified: how to organize words according to rules of combination into phrases and sentences. The term word class is used commonly to refer to the category in which a part of speech belongs. Word classes may be open or closed: open classes, which typically include nouns, verbs and adjectives, acquire new members constantly, while closed classes, such as pronouns and conjunctions, rarely do so, if at all. All languages have similar classes of nouns and verbs, with the same syntactic functions, but beyond these there are often challenging specific grammatical differences for learners to recognize: Japanese has three classes of adjectives; Chinese, Korean, Japanese and Vietnamese have a class of nominal classifiers (a word or affix that accompanies nouns classifying them according to referent); and so on. Grammar teaching and practice is an area in which chatbots can be used effectively, since they can easily segment words into their morphemes and lexemes and, with relevant prompts, can highlight which aspect of FL grammar requires more study and attention on the part of the learner. A chatbot can also easily explain what functions words play in sentences. The main pedagogical prompts that may be of utility in this area of FLT are the following: •

•

morphology: prompting the chatbot to identify FL lexemes and morphemes for students in terms of how they are used to construct FL words; syntax: prompting the chatbot to assign FL words to syntactic categories, so as to indicate to students how they function in FL sentences and larger structures (such as paragraphs);

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•

•

•

categorization: prompting the chatbot to identify for the students how a specific type of word is categorized according to its structure, such as the endings that might identify a word as a noun; semantics: prompting the chatbot to indicate to the students how meaning is assigned to words and larger structures, based on a large corpus of FL words in context; coreference: prompting the chatbot to explain coreference systems to students, such as how pronouns replace nouns in specific ways.

The latter point is an important one, since coreference is one of the hardest features to learn about the grammar of the FL. To get a sense of the task at hand, consider coreference in English, as realized with the following two types of grammatical devices: (1) anaphoric devices, which refer back to some word or phrase, as the use of she referring back to Sophia, in a sentence such as “Sophia went to the mall, where she wanted to do some shopping;” and (2) cataphoric devices, which anticipate some word or phrase, as for example, in the sentence “Even though she will deny it, I tell you that Mary did it.” Subject and object pronouns, locative particles, demonstratives, adverbs, and other kinds of grammatical classes, can function as anaphoric and cataphoric devices in conversations. There are other kinds of coreferential structures, which need not concern us here. The point is that an aspect of grammar such as coreference in the FL might take a lot of time and effort to teach and, on the part of the students, to acquire. The chatbot can be used in such areas to maximize teaching and learning time, as well as providing opportunities for practice and reinforcement, given that students can work with the chatbot to get relevant practice in this area and to receive concrete explanations of how coreference works in the FL.

Grammar and Usage As the foregoing discussion implied, ChatGPT is highly useful as a grammarteaching partner to both the teacher and student. Simon (2023), for instance, provides the following example of how a chatbot can provide appropriate and immediate feedback related to grammar in the case of someone trying to learn English: Prompt:

Provide feedback on my English: “Yesterday, I go to the pool and had fun swim with all the friends.”

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Here’s a revised version of your sentence with some corrections: “Yesterday, I went to the pool and had fun swimming with all my friends.” Overall, your sentence is understandable, but there are a few grammatical errors. “Go” should be changed to “went” to match the past tense of the sentence. Additionally, “swim” should be changed to “swimming” to use the correct verb form. Finally, it’s more common to say “all my friends” instead of “the friends.”

As this example shows, and as I myself confirmed with similar prompts, the chatbot is an expert grammarian. I asked it to evaluate the sentence “I goed to the store yesterday,” to which it responded: “The correct form is ‘went’ instead of ‘goed.’ The correct sentence is ‘I went to the store yesterday’.” It then informed me that “went” is an irregular form of the verb that breaks away from the normal verb conjugation paradigm. ChatGPT is clearly able to tackle any grammar query and provide personalized examples and drills responding to prompts such as: “What is the correct verb tense in this sentence?” “Why is this tense required?” or “How can I make this sentence more understandable and native-like?.” Here are a few more illustrative examples (from: fhttps://polyglotclub.com/wiki/Language/Multiple-languages/Culture/Howto-learn-a-language-with-ChatGPT): Prompt: ChatGPT: Prompt:

ChatGPT: Prompt:

ChatGPT:

What is the verb tense in this sentence? “She will be visiting her grandparents next week.” The verb tense used in this sentence is future continuous. How can I make this sentence more concise? “I am hoping that maybe in the future, I can possibly attend the university.” You can make this sentence more concise by saying “I hope to attend the university in the future.” I’m writing an email to a potential employer. Can you check if my sentence structure and grammar are correct?: “Dear hiring manager, I is excited to apply for the position at your company.” The correct form is “am” instead of “is.” The correct sentence is “I am excited to apply for the position at your company.”

ChatGPT’s responses are never abstruse, as can happen sometimes in traditional textbook explanations of grammar; they are pertinent to the user’s

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specific requests and learning requirements. To use another example, this time in the case of learners of Spanish as a FL (Caulfield 2023), let us suppose that a student does not grasp the reason why a pronoun subject may be omitted in Spanish sentences: Prompt: ChatGPT:

Prompt: ChatGPT:

In the sentence “Viajo a Mexico,” what is the subject? In the sentence “Viajo a Mexico,” the subject is the pronoun “yo” (I). The verb “viajo” (I travel) is conjugated to match the subject. Why is the subject not included in the sentence? I apologize for the confusion in my previous response. In the sentence “Viajo a México,” the subject is actually implied and not explicitly stated. The verb “viajo” (I travel) is conjugated in the first-person singular form, which indicates that the subject is “yo” (I). In Spanish, it is common to omit the subject pronoun when it can be inferred from the verb conjugation and the context. Therefore, even though the subject pronoun “yo” is not present in the sentence, it is understood that the speaker is referring to themselves as the subject.

Not only is this explanation accurate, but it is articulated in response to the specific prompts used, making it much more effective for the individual learner. Another important function of ChatGPT is that it can create personalized grammar exercises on a topic of one’s choice, as illustrated below (Caulfield 2023): Prompt:

Create a Spanish exercise with 5 incomplete Spanish sentences to practice subject-verb agreement. Example exercise sentence: Ella _______ como mecánica. Example solution: Ella trabaja como mecánica. Please don’t present the solutions in the exercise itself. Instead, I would like you to include a separate section below the exercise, which shows the correct solutions. ChatGPT: Exercises 1. Nosotros _______ al parque todos los domingos. 2. Tú _______ muy bien en el examen. 3. Ellos _______ libros en la biblioteca. 4. Mi hermana y yo _______ al cine esta noche. 5. La profesora _______ muy amable con sus alumnos.

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Correct Solutions: 1. Nosotros vamos al parque todos los domingos. 2. Tú te desempeñaste muy bien en el examen. 3. Ellos leen libros en la biblioteca. 4. Mi hermana y yo iremos al cine esta noche. 5. La profesora es muy amable con sus alumnos.

Such exercises can then be followed by translations into English and comparisons and contrasts made by the chatbot between the NL and FL, as prompted. Without going into further examples and details here, suffice it to say that chatbots can easily tailor the FL grammar to specific learner needs and can also provide teachers and students alike with analyses, examples, and review materials that cannot possibly be predicted by pre-fixed print materials or established curricular plans. All this raises the possibility of truly transforming traditional FLT, as Richard Campbell (2023) has aptly observed: Artificial intelligence has the potential to transform conventional teaching strategies in the field of language learning. Language learners can practice their language skills through interactive conversations in a more personalized and interesting way with AI-powered languages learning tools like chatbots and virtual assistants. These tools use algorithms for natural language processing to identify speech patterns and offer immediate feedback on pronunciation and grammar issues.

Needless to say, there are some caveats. While chatbots have a truly remarkable ability to detect and correct a large array of grammatical errors, they cannot cover all of a language’s grammar (at present), as Fang, Yang, Lan, Wong, Hu, Chao, and Zhang (2023) found, a fact that learners and teachers should take into account: Our experimental results and human evaluations demonstrate that ChatGPT has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent, possibly due to its over-correction tendencies and not adhering to the principle of minimal edits. Additionally, its performance in non-English and low-resource settings highlights its potential in multilingual tasks. However, further analysis of various types of errors at the document-level has shown that ChatGPT cannot effectively correct agreement, coreference, tense errors across sentences, and cross-sentence boundary errors.

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Vocabulary The type of pedagogical support that ChatGPT offers in the domains of pronunciation and grammar applies as well to the teaching and learning of FL vocabulary. The chatbot can generate a vocabulary list on a specific topic with just a few prompts, as discussed and illustrated at the start of this chapter. Once the vocabulary list has been produced, ChatGPT can assist in testing the knowledge gained by creating various types of related exercises and drills, including illustrations of how words are used with differing semantic nuances in sentences or within larger texts, thus providing to the student the possibility to form a deeper understanding of their meaning and usage. For example, ChatGPT can be asked to incorporate FL words into sentences, paragraphs, reading passages, or even dialogues, as Simon (2023) has shown in the case of an advanced French course: Prompt: ChatGPT:

Create a text using high frequency vocabulary on the topic of politics in French. Highlight the vocabulary words. La politique est un domaine complexe et crucial de la société. Les élections, partis politiques, et gouvernements sont des éléments centraux de ce système. Les citoyens jouent un rôle important dans la démocratie, où ils expriment leur voix par le biais du vote. Les politiciens, élus par le peuple, assument des responsabilités telles que légiférer, gouverner, et représenter les intérêts de leurs électeurs.

ChatGPT can also assist learners by responding concretely to a wide range of vocabulary questions, such as “What is the meaning of this word?,” “Can you give me synonyms for this word?” and so on. Follow-up practice material generated by the AI can then help learners practice different aspects of word and phrasal or idiomatic meaning.

Polysemy Note the differential meanings of cat in these four sentences: 1. “My cat has a long tail” 2. “My cat is a wonderful companion.”

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3. “Jazz musicians are cool cats.” 4. “They let the cat out of the bag.” The first sentence has literal, or more technically, denotative meaning, whereby the word cat refers to a specific kind of feline mammal. The second uses the word cat with a specific cultural connotative meaning, namely as a pet. The third sentence shows the use of cat in a figurative way, associating its perceived sleek movements with certain types of musicians (discussed further in chapter 4). And the meaning of cat in the fourth sentence is also figurative, but in a different way. In this case, it uses the animal as a metaphor for secrecy, likely associated with how cats are able to hide from danger. In effect, deciphering the meaning of words entails knowing the differences among denotative, connotative, and figurative dimensions of meaning and the kinds of relations that hold among words and cultural knowledge. The various meanings of a word such as cat are called polysemic. Can a chatbot make these distinctions? It is in this area that chatbots show some difficulty, although with neural networks and Deep Learning, they can still come up with an appropriate meaning by deciphering the contextual cues that are present in a prompt. Nevertheless, polysemy is tough to compute for a chatbot, hence the need for a human teacher in this domain of vocabulary learning. Chatbots rely on the programmed information and data on which they have been trained. If the chatbot’s training does not include the specific data on FL polysemic variability, it may not be able to provide a relevant response.

Thematic Vocabulary As in the case of pronunciation and grammar, a chatbot can be prompted to generate theme-specific vocabulary for learning or instruction. For instance, I asked ChatGPT the following: Prompt:

Create an Italian–English vocabulary list on the theme of cooking. ChatGPT: Sure! Here’s an Italian-English vocabulary list on the theme of cooking: 1. Cucinare: Cook 2. Ricetta: Recipe 3. Forno: Oven

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Marcel Danesi 4. Stufa/Fornello: Stove 5. Padella: Pan 6. Pentola: Pot 7. Coltello: Knife 8. Forchetta: Fork 9. Cucchiaio: Spoon

After that, I asked ChatGPT if the word padella can be used in idiomatic expressions, and it responded with one example, dalla padella nella brace (“from the pan into the fire”) and, as far as can be told, this is certainly the most frequent idiomatic use of the word, if not the only one. I then prompted ChatGPT to find at least one idiomatic expression for each of the other words, and it came up with accurate suggestions as well. Clearly, ChatGPT is a useful tool for thematic vocabulary learning or teaching. The chatbot can suggest words and phrases that are thematicallyfocused and explain their range of uses, helping learners construct sentences using those words. With an appropriate plugin it can also provide metaphorical ways of saying something—a topic to be discussed in chapter 5. As Halder (2023) has shown, with the appropriate plugin, ChatGPT can generate utterance-appropriate metaphors in English, such as the following: Prompt: ChatGPT: Prompt: ChatGPT: Prompt: ChatGPT: Prompt: ChatGPT:

Generate a metaphor for overcoming life’s obstacles. “steep hill to climb” (among others) Generate a metaphor for love. “love is magic” (again among others) Generate a metaphor for a strong friendship. “solid friendship” (once again among many others) Generate a metaphor for hope. “a journey with no end” (along with many other suggestions)

The prompts can be followed-up with requests for ChatGPT to use the metaphors in relevant examples and to indicate any synonymous expressions. Given its ability to navigate large amounts of data the chatbot can come up with all kinds of relevant conversation-appropriate thematic expressions. Here are some examples from Spanish (Caulfield 2023): Prompt:

How do I say the following sentence in Spanish? “I often travel to Mexico for work and go on vacation there.”

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To say “I often travel to Mexico for work and go on vacation there” in Spanish, you can say: ”Viajo a menudo a México por trabajo y también voy de vacaciones allí.” Please note that translations can vary depending on the context, and this translation assumes a general meaning. Are there other ways to express the same information in Spanish? Certainly! There are multiple ways to express the same information in Spanish. Here are a couple of alternative ways to convey the sentence: “Con frecuencia viajo a México por trabajo y también voy de vacaciones allá.” “Regularmente viajo a México por motivos de trabajo y también aprovecho para vacacionar allí.” “A menudo viajo a México por cuestiones laborales y también me tomo vacaciones allí.” These variations maintain the same meaning but present slight differences in wording and sentence structure.

As in other areas of the FL, ChatGPT can follow-up with interactive vocabulary games and exercises, such as word matching or fill-in-the-blank sentences. And it can be used by teachers to create flashcards with the required type of vocabulary words and phrases in the FL, along with their translations and pertinent illustrative images, so as to help language learners to memorize new words effectively.

Epilogue The ability of chatbots to analyze learner data, adapt to the learner’s needs, and provide personalized feedback and guidance in the primary areas of linguistic competence development (pronunciation, grammar, vocabulary) is what makes the AI highly useful as a complementary teaching-learning tool, at the very least. AI-generated content can also incorporate interactive features such as speech recognition and pronunciation feedback to help learners practice and improve even these psycho-motor skills. Overall, AI can greatly help students develop linguistic competence in the FL, in a way that is tailored to meet their specific needs and adapt to their learning styles. Traditional FL learning materials, technologies, and methodologies, including textbooks and audio CDs, and even digital language labs, are static

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to varying degrees, providing an established sequence of linguistic items to be taught according to a pre-established curricular script. Ever since the Direct Method, the underlying objective has been for FLT to provide a “one-size-fitsall” approach that consisted of limited opportunities for the student to interact with the material individually or receive personalized feedback without anxiety. On the other hand, AI systems such as ChatGPT use Machine Learning technologies that allow them to carry out specific or task-based analyses of learner data, which can then be used to make the teaching-learning experience a responsive one to actual needs. Another key difference between AI-assisted FLT and traditional FLT is the ability of chatbots to incorporate real-world examples and current events into the learning experience. AI algorithms can process vast amounts of data to identify relevant and up-todate examples of the FL used in practical situations. The primary benefits of an AI-shaped pedagogy of FL pronunciation, grammar, and vocabulary can be summarized as follows: •

•

•

•

•

•

Personalization: ChatGPT can identify learner problems and learner styles, and thus adapt the pedagogical materials and instructions it has the ability to generate according to their individual needs. Real-time feedback: As discussed throughout this chapter, chatbots can provide learners with real-time feedback on their pronunciation, grammar, and vocabulary usage. This can help them identify areas for improvement and allow them to make corrections as they learn. Affective filter: As Krashen emphasized (chapter 2), students often have an affective filter that prevents them from fully acquiring the input to which they are exposed, and they may uneasy as to what to say in a classroom environment. Interaction with a machine not only lowers the filter, but eventually may eliminate it completely. Interactivity: Chatbots can include interactive features such as speech recognition, thus providing learners with the opportunity to practice their language skills in a sophisticated language lab type context. Real-world examples: AI algorithms can analyze vast amounts of data to identify relevant and up-to-date examples to help learners understand how the FL language is used in (changing) real-world contexts. Gamification: AI has the ability to create games on the spot, and adapt them to the learning level and preferences of the learners.

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The last point requires some elaboration. Among the relevant findings related to the use of puzzles and games in FLT the following stand out (for a summary of the research, see Danesi 2022): •

• •

•

• •

Puzzles stimulate creative thinking, thus helping students unpack hidden information in them on their own via the problem-solving format. They motivate students to explore the new notions on their own. Most puzzles reflect content that is easily understandable, no matter what the student’s cultural background, which makes them useful as classroom devices. Since the chatbot can adapt to the student’s level of knowledge, it can produce puzzles in the FL that can greatly enhance the acquisition process as per the Input Hypothesis (chapter 2). Puzzles can be tailored to reinforce specific learning objectives and chatbots are clearly expert at doing so, creating puzzles in terms of level grammar and vocabulary achieved by a student, and according to the individual student needs. When combined with other pedagogical materials, puzzles thus complement the learning process in a supportive way. Games and puzzles that are found commonly in the FL culture can be adapted profitably for the enhancement of learning the language. Since chatbots can extract these from the Internet and adapt them to the learning situation at hand, they are particularly useful in creating such culturally-based ludic content.

As mentioned throughout this book, putting the enthusiasm for using chatbots aside, given their versatility and adaptability, there are caveats. The main one is that the best way to practice a new language is to speak it with another human, and hence the importance of a human teacher in facilitating and bolstering the learning process (discussed further in chapter 6). ChatGPT may not, in effect, always be the best conversation partner since it might not be able to pick up on contextual clues, as Richard Campbell (2023) has aptly pointed out: AI is a tool that is gaining popularity among language learners. The absence of interpersonal interaction is one of the main negatives, though. While AI can give students access to a variety of knowledge and

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Chapter 4

Promoting Communicative Competence Prologue A common technique in FLT, used in most pedagogical approaches, is the dialogue, which is typically designed to present new information in simulated conversational ways. Traditional print textbooks generally include a dialogue per chapter based on the new vocabulary and grammar introduced within it. Because print is static, the dialogue in the textbook cannot be immediately updated or adapted to specific needs, and thus has diminishing value as a pedagogical tool over time. With ChatGPT, on the other hand, dialogues can be constantly updated according to changing conversational styles in the FL and the content within them chosen according to changing lexical trends in the culture. Moreover, unlike the print dialogue, a chatbot-generated one can be tailored to meet specific learner and course needs. Consider the following example (from the website: https://sanako.com/how-will-chatgpt-influenceand-impact-language-education), whereby ChatGPT has been prompted to provide a beginner-level dialogue in Spanish. It presents thematically-relevant vocabulary and grammar, based on a specific communicative theme—making contact: Prompt:

Create a beginner-level dialogue in Spanish between two friends discussing their favorite hobbies. ChatGPT: Personaje 1: Ana Personaje 2: Luis Ana: ¡Hola, Luis! ¿Cómo estás? Luis: Hola, Ana. Estoy bien, ¿y tú? Ana: Estoy muy bien, gracias. Oye, ¿cuáles son tus pasatiempos favoritos? Luis: Me gusta mucho leer libros y escuchar música. ¿Y tú, Ana? ¿Qué te gusta hacer en tu tiempo libre? Ana: A mí me encanta pintar y cocinar. Además, disfruto mucho de ver películas. Luis: ¡Qué interesante! ¿Qué tipo de películas te gusta ver?

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Luis: Ana: Luis: Ana: Luis:

Me gustan las películas de comedia y aventuras. Siempre me divierten mucho. ¿Y a ti, qué tipo de música te gusta escuchar? Me gusta escuchar música pop y rock. Me ayuda a relajarme y a pasar un buen rato. ¡Eso suena genial! Tal vez algún día podríamos cocinar juntos y escuchar música mientras lo hacemos. Claro, me parece una idea estupenda. Será divertido compartir nuestros pasatiempos. ¡Perfecto! Hasta entonces, ¡nos vemos! ¡Hasta luego, Ana!

Note that ChatGPT created appropriate characters, with common Spanish names, who spoke in an informal style, with the relevant idiomatic expressions, conveying joviality and familiarity. Now, the details of the dialogue can be changed by different prompts, such as asking ChatGPT to utilize the topic of different hobbies that may be more interesting to a specific learner or even class, making it even more thematic or individualized according to specific student or course needs. In effect, AI is transforming the dialogue into a truly adaptive and constantly updated interactional form, ensuring that its conversational structure and content are always relevant to what is occurring in the FL culture. As such, it is possible to make the dialogue a non-static pliable technique designed to help students develop communicative competence in the FL. As discussed in previous chapters, communicative competence is a term that Dell Hymes coined originally in 1966 to complement the idea of linguistic competence, implying that there is a dynamic interaction between language structure and language use. This notion had immediate impacts on FLT, also as discussed, changing its focus away from the strict concept of method, as in the DM and ALM, to that of classroom interaction, so that students can learn by conversing with each other and with the instructor. Learners are expected to converse about personal experiences, and instructors to deal with conversational topics directly. However, even in such an open pedagogical environment, the dialogue concept was retained to shape derivative interactions, and it continues to be a significant pedagogical tool in the current AI age, as the above example shows. This chapter will deal with the topic of how AI can help promote communicative competence in the FL. It is remarkable that ChatGPT can understand and respond to human speech in a natural way, and can thus be

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used to create increasingly advanced dialogical-conversational materials. It is a “talking language lab,” so to speak, that teachers and students can take with them anywhere and at any time. It is also a “virtual assistant” to both learners and instructors, given its the ability to learn from its interactions with users, and to respond to inquiries in a relevant way.

Communicating in a Foreign Language As mentioned in chapter 2, it was during the 1970s that interest in converting psychological and linguistic theories into pedagogical practices, which started with the Direct Method, had waned considerably in FLT. Teachers started questioning the implicit goal of all the methods at the time—the development of FL skills systematically and sequentially, from listening and speaking to reading and writing. Rarely, they emphasized, did this view of FLT lead to learners acquiring the ability to use the FL by the end of a course of study to do things with it in the real world, as, for example, how to be polite, how to make social contact, how to express oneself in specific situations, etc. Imparting this kind of know-how was not taken into account in any systematic way by any of the methods, with a few exceptions. Language educators at the time started even to look at history to see if it could offer any insights into solving this dilemma. From this interest, works by educators such as the Czech teacher Comenius in the seventeenth century, came to the forefront of the new debate in FLT. Comenius had claimed that students learned best when they had to decipher and produce dialogues reflecting real life interactions. From these, he suggested, students could easily induce the appropriate linguistic and cultural rules on their own. The overall result of this new Zeitgeist was a focus on communication in the FL. It is interesting to note that chatbots are designed to carry out dialogues, as if they were themselves learners of the language spoken according to communicative-teaching principles. For this reason, among others, they constitute effective tools for promoting communicative competence, given their own design. Made-to-order dialogues such as the Spanish one above are arguably effective as communication-promoters because they are prompted by teachers and students alike, and they are based on the most updated language and conversational styles that are occurring in the FL culture.

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Communicative Competence To reiterate, it was the linguist Dell Hymes (1966, 1971) who indirectly brought about the end of the method notion in FLT when he argued convincingly that languages were hardly impervious to influences from realworld social interaction. He suggested that, on the contrary, verbal structures were shaped, and over time even modified, by language use. Moreover, he argued that the ability to apply language to specific social situations in a meaningful way constituted a different kind of competence, calling it communicative competence. Hymes’s term held an instant appeal to teachers, who at the time were also beginning to suspect that the method notion was a flawed one. The follow-up research on communicative competence led, as a consequence, to a paradigm shift in FLT—away from the focus on linguistic competence to a focus on communicative competence and, a little later, to an amalgam of both competencies, known by the mid-1980s as the Proficiency Movement, a standards-based system of instruction and assessment that requires learners to demonstrate competence in targeted knowledge, skills, and abilities before moving onto the next level or challenge, no matter what pedagogical approach was used to get them to that level. The first steps to develop communicative syllabi were taken by the Council of Europe (Van Ek 1975, Wilkins 1976). Their organizing principle was the concept of speech act, defined as a communicative strategy that native speakers employ unconsciously to carry out specific types of social functions—a notion that goes back to John Austin’s 1962 book, How to Do Things with Words. A simple social protocol such as saying hello or good-bye, for instance, constitutes a speech act, because it is based on an implicit detailed knowledge of the appropriate words, phrases, structures, and nonverbal cues that come together cohesively in a script-like fashion to enable a speaker to carry out a successful speech interaction with another speaker. An infringement of any of the procedural details of this script might lead to a breakdown in communication. Now, the question becomes: Can chatbots be used specifically for the development of communicative competence, assuming that they have the ability to differentiate between the functions of specific speech acts, as the example in Spanish above suggests? Leaving aside anomalies that I myself noted when prompting ChatGPT to create dialogues in different situations in Italian, the chatbot was able to generate acceptable dialogues used in typical social situations such as ordering a meal at a restaurant, carrying our affairs at a bank, or simply chatting with an acquaintance. When I compared these with

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textbook dialogues on the same themes, I found that the chatbot had updated the language and speech strategies considerably to reflect contemporary talks. The same pattern of findings are discerned in similar uses of chatbots in other languages. Simon (2023) provides an example of a restaurant-based interaction with ChatGPT in French: Prompt: ChatGPT:

Prompt: ChatGPT:

Je vais prendre la salade méditerranéenne. Parfait, la salade méditerranéenne est un excellent choix! Voulez-vous une vinaigrette spécifique avec votre salade? Nous avons une vinaigrette à l’huile d’olive et au citron, ou une vinaigrette balsamique. Ou peut-être préférez-vous une autre option? Je vais prendre une vinaigrette balsamique. Très bien, je vais vous apporter une salade méditerranéenne avec une vinaigrette balsamique. C’est un choix délicieux!

Not only did the chatbot simulate what an actual server would say in a French restaurant, providing the same kind of optional paths that a conversation might take, which led the prompter to respond according to the options, but it also used the appropriate formal register (with the relevant vous pronoun and related verb forms) as well as the formal social tone required in such a situation. Now, while a chatbot can provide cultural and communication-appropriate conversation practice, it is always important to keep in mind that it is an AI system and may not be able to fully replicate the experience of interacting with a human interlocutor, such as a waiter. Human conversation partners bring cultural, contextual, and bodily cues and nuances that an AI model does not possess—a topic discussed further in chapter 6.

Role of Context The role of context in promoting communicative-conceptual accuracy cannot be overestimated. Context shapes meaning and interpretation. The human mind always interprets information, not just processes it, in contextualized ways. For this reason, it can unpack any ambiguity in the use of words. Linguist Yehoshua Bar-Hillel (1960) was among the first to use an example of linguistic ambiguity that came to be known as the Bar-Hillel Paradox to show that the early versions of Machine Learning programs were incapable of deciphering contextualized constraints on meaning. As Bar-Hillel showed,

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humans use extra-linguistic (contextualized) information to make sense of messages that computers at the time (the late 1950s) were not be capable of accessing in the same way. In other words, context was identified as a determinant in how humans understand words and interpret their meanings. His example is as follows: 1. The pen is in the box (= the writing instrument is in the container) 2. The box is in the pen (= the container is inside another container [playpen]) Humans can distinguish meaningfully between the two utterances because they have access to outside information about the meaning of the word pen. As this example shows, and as discussed in the previous chapter, polysemy is an inbuilt feature of human language, which produces ambiguity that is resolved by real-world knowledge. Bar-Hillel’s Paradox led shortly thereafter to the serious study of extra-linguistic inferences in human discourse. Indeed, it can be argued that it was the starting point for the growth of pragmatics and conversation analysis as major branches of linguistics. Overall, Bar-Hillel’s Paradox brought out the importance of real-world context in determining the human meaning of forms such as words. In order for a fully-automatic Machine Learning system to resolve Bar-Hillel’s sentences correctly, it would have to have some rule subsystem that would indicate that: (a) if pens refers to writing instruments, then they are (typically) smaller than boxes; (b) if pens refers to large containers, then boxes can be put into them (if smaller); and (3) that it is impossible for a bigger object to be contained by a smaller one. Actually, chatbots can now resolve context-based ambiguity almost systematically, given the Deep Learning and Natural Language Programming systems on which they are based. These allow the chatbots to conduct both an “internal linguistic analysis” of the grammatical and lexical items requested by prompts and then an “external linguistic analysis” of the real world contexts that constrain the selection and concatenation of the items. Nevertheless, the guesswork involved in AI systems is vastly different than the one involved in human inferences. Humans make their hunches on real contextualized experiences in the world, algorithms involve probability models of such experiences. The two are isomorphic systems, not identical ones. In effect, Machine Learning has made many advances in this area that have led to AI systems today which deal with polysemy and other ambiguities to high degrees of accuracy. The algorithm starts by making inferences about the appropriate meaning of a word in terms of frequency distribution measures

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at the so-called “first layer” in the neural network. The algorithm then searches for contextualized options at the “second layer.” Frequency again determines the appropriate option for its output at the “third layer.” The details of how this is done are rather complex; and need not interest us here as such. Suffice it to say that the chatbot’s grasp of the correct meaning involves mining data from millions of texts on the Internet, analyzing the words within them statistically in terms of contextualized patterns, and then selecting the required meaning according to the prompt. The reason why the chatbot is so accurate is that the neural networks on which it is trained can access a billion to a trillion examples of real conversations. The chatbot then uses its knowledge about syntax in order to generate appropriate responses. A key aspect of human communication is the ability of interlocutors to make a conversation flow logically and coherently, taking contextual variables into account, such as social register and content shifts. ChatGPT approximates the flow by using a combination of techniques, which appear to mirror how humans keep conversations flowing in a meaningful way. These include the following: •

•

•

Dialogue state tracking: ChatGPT can keep track of the context and previous content of the conversation, including previous queries and responses, and then uses this information to understand the current state of the conversation and generate responses that are relevant to the flow. In the French restaurant scene above, the chatbot understood how to add to the content of the customer’s order, suggesting balsamic vinegar and then, remembering this, was able to connect the suggestion to the request. Response selection: ChatGPT can select the most appropriate response from a repertory of possible responses based on the context and content flow of the conversation, using advanced Machine Learning algorithms to do so, which enable it to take into account factors such as the relevance and coherence of the response. The whole restaurant scene above is grounded on the response selection system built into chatbots. Response generation: ChatGPT has the ability to generate natural language responses that are grammatically and semantically correct. It achieves this by using a neural network architecture that is capable of generating responses that are both relevant and verisimilar to real-

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life situations. Aspects of the architecture were described schematically above.

Conversations Conversations occur when people meet and interact in some (usually) purposeful way. They involve the ability to communicate coherently according to context. Teaching this in a FL involves keeping in mind the fact that even in promoting communicative competence there is bound to be NL transfer, whereby the conversational scripts between the NL and the FL could be isomorphic (positive transfer) or different (negative transfer). Passing on the ability to converse in the FL, however, involves a different type of Contrastive Analysis—one that looks at the speech act forms used in the NL and the FL in order to determine potential areas of negative transfer. If two people decide to have coffee together at a coffee bar, then it would be anomalous or bizarre if they did so in silence in American culture; rather, small talk (at the very least) is expected in this situation. But this is not so in other cultures, where small talk may be seen as anomalous or even insulting. The point to be emphasized is that a conversation is not realized necessarily in the same way across languages. Weather is a common topic in regions where the climate is unpredictable. Questions about the family are usual in many Asian and Middle Eastern countries. In some cultures, small talk between new acquaintances may feature exchanges based on the differential social class structure of the interlocutors. Interestingly, some countries, such as Finland and Sweden, utilize small talk sporadically, being more comfortable with silence. An overarching law of pragmatic interactional behavior is, in fact, that human conversation is highly variable along different cultural axes. ChatGPT has the ability to come up with variants that are culture-specific and even dialectal variants in a FL, if asked to do so, as can be seen, for instance, in the following example (from Caulfield 2023): Prompt:

ChatGPT:

I am learning Spanish because I am planning to spend some time in Peru. Can you give me a list of common idioms that are unique to Peruvian Spanish, with English translations? Certainly! Here are some common idioms that are unique to Peruvian Spanish, along with their English translations: “Estar en la luna”—To be on the moon (meaning to be

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absent-minded or daydreaming); “Estar en la papa”—To be in the potato (meaning to be in a good situation or feeling great); “Hablar hasta por los codos”—To talk through one’s elbows (meaning to talk a lot). These idioms are specific to Peruvian Spanish and may not be widely used in other Spanish-speaking countries. Learning these expressions will help you understand and communicate better with locals in Peru.

In normal social conversations, we expect speech to be relevant to the topic at hand, and often we expect it to be scripted, following some agreedupon “internal script” between interlocutors in specific situations, which routinizes speech acts in typical social situations. Script theory was described decades ago by computer scientist by Roger Schank (1984: 125) as follows: When we read a story, we try to evaluate the reasoning processes of the main character. We try to determine why he does what he does and what he will do next. We examine what we would do in a similar situation, and we try to make the same connections that the main character seems to be making. We ask ourselves, What is he trying to do? What’s his plan? Why did he do what he just did? Any understanding system has to be able to decipher the reasoning processes that actors in stories go through…[this means] understanding how people formulate goals and plans to achieve those goals. Sometimes people achieve their goals by resorting to a script. When a script is unavailable, that is, when the situation is in some way novel, people are able to make up new plans.

Conversational scripts exist across the social spectrum. Making contact with a stranger, for instance, requires access to both the appropriate speech protocol, its contextualized variants, and the specific category of verbal structures that encode it. The enactment of agreements, disagreements, flirtations, and so on can be seen to unfold in a script-like fashion, within conceptualized constrains such as the type of relation the interlocutors have with each other. Scripts are among the most common tools, in the form of dialogues, used to impart communicative competence in the FL. The most common ones can be generated by ChatGPT via prompts such as “How do you greet someone in the FL formally?” “How do you make small talk in the FL at a coffee bar”? and so on. These can be followed up by asking ChatGPT to explain the differences between the NL and the FL, so as to complete the pedagogical process.

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Needless to say, there is more to human interaction than script theory and routinized conversations. But the reliance on systematic ways to speak in different situations is still a common factor in human dialogical behavior. The speeches and interactions that typically take place during social rituals and rites, such as sermons, rallies, political debates, and other ceremonial types of speech, for example, show that speech involves different kinds of script-like behavior, either traditionally coded or specifically composed for the occasion. Speech in ritualistic situations is not intended to create new meanings, but to assert communal sense-making and to ensure cultural cohesion. Among the questions that are involved in training chatbots to learn how to carry out speech acts, the following are of special relevance: •

• • •

What are the objectives and effects of different types of speech in certain FL situations? What keywords characterize them and what styles are appropriate in the FL? What FL cultural rules and conventions are built into the speech patterns? How do these patterns reveal FL cultural values, beliefs, assumptions, and worldviews? What specific words indicate these? How does the speech used in the FL culture relate to social, political, cultural, and historical processes and systems?

Structure of Conversations The ability to carry out conversations in the FL is a core part of developing communicative competence. However, no generally accepted definition of conversation exists, beyond the fact that it is a communicative act that involves at least two people talking together. As Thornbury and Slade (2006: 15) characterize it: “Conversation is the kind of speech that happens informally, symmetrically, and for the purposes of establishing and maintaining social ties.” If someone asked an interlocutor, “How are you?,” and the interlocutor answered illogically, “Today is a fine day,” then there is no meaningful exchange possible, unless the interlocutors know each other and know as well that irony or humor is sometimes used in their interactions via such non-sequential responses. The expected answer is a formulaic one: “Not bad, and you.” Conversations reveal a lot about how we interact with

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each other, what our intents are, and even who we are or think we are. So far, chatbots seem able to differentiate between normal and ironic or conflictual speech, tending to answer anomalous communication in prompts with expressions such as “That does not make sense,” and similar responses. Among the most common types of conversations that chatbots can easily generate, the following are worth mentioning here: • • • • • •

Small talk, in which people exchange information, feelings, and thoughts in a perfunctory fashion in the FL. Discussion, in which the conversation in the FL is perceived to be about something important. Chat, which is an informal friendly conversation in the FL. Gossip, which is a conversation in which people talk about things they have heard, which may well not be true. Chitchat, which is polite friendly conversation about unimportant subjects, often for reasons of psychological appeasement. Banter, which is a friendly conversation in which people joke or make fun of each other.

The in-depth study of computer-generated conversation goes back to the work of Terry Winograd in the late 1960s. One of his computer experiments involved what he called SHRDLU—a name that reflects the moderately high frequency of the letters S, H, R, D, L, U in conversational texts (Winograd 1971). SHRDLU was capable of carrying out a seemingly natural conversation with a user who inputted commands on a keyboard. The machine would produce observations such as “I don’t understand which pyramid you mean.” The following stretch of dialogue between a person and SHRDLU has now become a classic one in the annals of Artificial Intelligence (http://hci. stanford.edu/winograd/shrdlu/): Person: Computer: Person: Computer: Person: Computer: Person:

Pick up a big red block. OK. Grasp the pyramid. I don’t understand which pyramid you mean. Find a block which is taller than the one you are holding and put it into the box. By “it,” I assume you mean the block which is taller than the one I am holding. OK. What does the box contain?

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The blue pyramid and the blue block. What is the pyramid supported by? The box. How many blocks are not in the box? Four of them. Is at least one of them narrower than the one which I told you to pick up? Yes, the red cube. Is it supported? Yes, by the table. Can the table pick up blocks? No.

Clearly, SHRDLU appears to know how to approximate how humans think when they talk. The dialogue contains, in fact, sequences that are logical sequiturs and sensitive to the relevance and quality of the content flow. The computer could also remember the names given to previously-mentioned objects, and to their arrangements, and it could answer questions about the blocks and their relevance to the situation at hand. In effect, the SHRDLU dialogue is an example of what a conversation is essentially about—a set of statements that cohere into a dialogical unity, containing features such as the following: •

•

Turn-taking: when people are involved in a conversation, they do not talk all at the same time (usually)—one person speaks, and then another person can follow. A turn becomes evident through certain cues that interlocutors recognize and count on, called turn construction units. These allow speakers to predict when someone is finished so that others can speak, thus minimizing both overlap and silence gaps. In the SHRDLU conversation, which consists mainly of a question-and-answer structure, the computer’s turn comes after a question. This is the simplest form of turn-taking, since it is logically expected. Sequence organization: The point above brings out another intrinsic feature of conversations—namely, they show sequence organization. The most common type is seen in question-and-answer sequences, known as adjacency pairs—sequences that tend to occur in pairs. However, the pairs may be split over various turns.

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Sequence expansion: This allows a conversation made up of more than a topic or theme to be constructed and understood as still relevant, given additional elements introduced within it. Repair: This describes how interlocutors deal with emerging problems in the flow of the conversation. Participants seek to correct any trouble source by initiating a repair, such as admitting that a wrong word was chosen. Phases: Interactions may involve various phases, including opening and closing a conversation, assessing something said, complaining about it, and so on.

Using ChatGPT as a conversation partner will allow students and teachers to zero in on these aspects of conversation in the FL, even if not explained explicitly at first. The following example, put forth by Álvarez-Morillo (2023), is a case-in-point of how a student can use the chatbot to engage in a meaningful and information-seeking Spanish conversation—an interaction in which turn-taking, sequence organization, phase structure, and other aspects are clearly evident in it: Prompt:

ChatGPT:

Prompt: ChatGPT: Prompt:

Let’s pretend you are a high school student from Uruguay. I’m going to ask you a series of questions in Spanish, and I’d like you to respond as if you were human. ¡Claro! Estoy listo para responder tus preguntas si fuera un estudiante de secundaria de Uruguay. Adelante, ipregúntame lo que quieras! ¿Cómo te llamas? ¡Hola! Me llamo Juan. ¿Y tú, cómo te llamas? Me llamo Natalia. Soy estudiante de 4to de secundaria. ¿Tú? ¿En qué grado estás?

As can be seen, the contact protocol between prompter and chatbot unfolds in a Spanish native-like way. After making “contact” with the machine, the interaction can become even more theme-specific, projecting the whole exercise into relevant semantic and cultural domains (from ÁlvarezMorillo 2023): Prompt: ChatGPT:

¿Te puedo hacer unas preguntas? ¡Claro, Natalia! Estoy aquí para ayudarte. Adelante, puedes hacerme las preguntas que desees.

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¿Cuál es tu materia favorita? Mi materia favorita es la historia. Me encanta aprender sobre eventos pasados y cómo han moldeado el mundo en el que vivimos hoy. Además, me fascina investigar diferentes culturas y civilizaciones.

The aspect that sets a chatbot-generated dialogue from static textbook ones is that it adapts personally to the prompter, using her name, and reacting directly to her questions—something that could not possibly be achieved in a pre-written print dialogue.

Conversation Analysis Perhaps in no other domain of research in pragmatics has the study of communicative competence borne such fertile fruit as in the study of actual conversations. Conversation Analysis (CA) has become a broad area of investigation within various branches of linguistics, anthropology, sociology, and psychology since at least the 1980s. A major finding of CA is that conversations are not random; rather, they are governed by social rules, as discussed and illustrated above. Even a simple protocol such as calling someone on the phone requires a detailed knowledge of the appropriate words to start and end to call. Note that the protocol changes if an email or text message is used in lieu of an oral phone conversation. In CA, words, phrases, and sentences are studied as units within conversational sequences, frames, or texts. Personal pronouns, for instance, are not viewed exclusively as parts of speech, but as anaphoric or cataphoric trace devices (chapter 3), serving conversational needs, such as maintaining the smooth flow of conversation by connecting its parts like an electric network of wires, and eliminating repetitive items. Consider the following two utterances, which have the same content but deliver it in different ways: 1. Mary is a friend. Mary likes to talk about Mary. But I still like Mary. 2. Mary is a friend. She likes to talk about herself. But I still like her. The first one is perceived normally as stilted because of its repetition of the noun Mary; the second one, on the other hand, comes across as more “conversationally natural,” given its use of pronouns as trace devices. In this

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view of language, pronouns are designed to keep the flow of the conversation smooth and economical. The choice of pronouns is thus not due to a rule of grammar; but to a rule of conversation. Conversational texts show, therefore, that we are sensitive to textual rules, more so than strictly grammatical ones— that is, we anticipate how the forms in a text or utterance relate to each other and cohere sequentially into a message-making system. Another major finding of CA concerns what is called the social framing of speech. Conversations can be framed to be aggressive or subdued, competitive or cooperative, depending on situation. In the case of competitive speech, the language used is typically adversarial, whereas in the case of cooperative speech, the language indicates that the speakers are inclined to work together to produce shared meanings. In other words, cooperative or competitive speech acts have identifiable characteristics. The former are marked by features such as the following: 1. Speakers build upon each other’s comments (“That’s true,” “I agree”). 2. They use hedges to indicate consent (“Uh-huh,” “Yeah,” “Sure,” “Right”). 3. When disagreement surfaces, it is negotiated with various formulas (“Yeah, but, maybe”). 4. Tag questions are used to ensure consent (“You agree, don’t you?”) This implicit script imparts a sense of togetherness among speakers. As Robin Lakoff (1975) found in her research, speakers regularly refrain from saying what they mean in many situations in the service of the higher goal of politeness or cooperation in its broadest sense; that is, to fulfill the function of collaboration. Competitive conversations are also marked by specific strategies, which stand in direct opposition to how they are used in cooperation: 1. Interlocutors tend to contradict one another’s comments (“That’s really not true,” “I wouldn’t say that”). 2. They use hedges to indicate dissent (“No-no,” “No way,” “Not true”). 3. Difference of opinion is indicated with expressions that convey doubt or uncertainty (“Sure, but, maybe”). 4. Tag questions are used as challenges (“You don’t mean that, do you?”).

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Clearly, knowing how to frame speech in conversations entails knowledge of how language, intent, social relations, and situation intersect. The sequences that occur in conversations follow a kind of implicit script that interlocutors in such settings agree upon—asking relevant questions, giving support or expressing opposition, discussing relevant topics in the given situation, and so on. As we saw with the ChatGPT dialogues above, it is clear that AI is able to mine from the huge network of texts in its database the appropriate conversation protocols and scripts that are relevant to the situation at hand in terms of the appropriate textual structures. With suitable prompts, teachers and students can in fact get the chatbot to generate correct up-to-date conversational content. Examples of very useful prompts are given by Álvarez-Morillo (2023), which can be paraphrased here for convenience, using Italian as the FL. From these, it is easy to imagine the kinds of conversational scripts that will be generated by ChatGPT: Prompt:

Prompt:

Prompt:

Prompt:

Prompt:

Let’s pretend you are a tour company in Italy called La Bella Italian. I will pretend to be a tourist. I will ask you a series of questions in Italian, and I’d like you to respond as if you were a human tour organizer. Let’s pretend you are at a restaurant in Pisa called Al Dente. I will pretend to be someone who wants to dine at the restaurant. I will ask you a series of questions in Italian. Pretend that you are my new Italian friend, Claudia. I will be myself. You will speak to me in Italian as if we were classmates. A minor traffic accident happened in the street named Via Appia. I’m a local news reporter and want to interview you about what you saw. I will be asking questions in Italian. Let’s pretend you are my Italian teacher, and you want to plan a school trip for the class. I will ask you questions about the trip in Italian.

Speech varies according to the age, occupation, socioeconomic status, and other variables of the interlocutors, being marked accordingly by so-called registers—modes of speaking or writing that are designed to match the situation, the medium used (face-to-face conversation, online interaction, or writing), the relations between the speakers, and the nature of the topic involved. Take, for example, saying good-bye to another person in English. This will vary typically as follows:

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Good-bye! Bye! See ya’!

The choice of one or the other expressions is not random or optional; it is a matter of the enactment of politeness criteria present in English society. It would be considered rude to address, say, a judge during a court proceeding with an informal mode of speech (unless the judge permits it); and it would be considered to be aberrant or strange to address a close friend with a highly formal register. This type of systematic speech variation is found throughout the world. For example, in the traditional, historical Javanese society of Indonesia, members of the different classes were expected to utilize a distinct register of speech. At the top of the social hierarchy were the aristocrats; in the middle the townsfolk; and at the bottom rural denizens. The formal register was used by aristocrats who did not know one another very well, but could also be used by a member of the townsfolk who happened to be addressing a high government official. The middle register was used by townsfolk who were not friends, and by rural people when addressing their social superiors. The informal register was used by the latter among themselves, or by an aristocrat or town person talking to a rural denizen, and among friends on any social level. It was also the register used to speak to children of any class. Now, in modern Javanese society some of these registers have been recalibrated, as the society has changed. But they have nonetheless left residues in how people address each other to this day (Harwati 2018). Relevant prompts for ChatGPT in the domain of register appropriateness might include the following in the case of, say, Korean as the FL: Prompt: Prompt: Prompt: Prompt:

Let’s pretend you are my Korean teacher, how would I say hello politely to a young person? Help me carry out an actual contact protocol in Korean with that person. What kinds of words will help me show politeness in a commercial store situation? How should I greet someone in authority in Korea?

ChatGPT can handle different registers of language use in response to user input, adjusting the tone, lexicon, grammar, and style of its responses accordingly, because it has been trained on a broad range of texts, based on formal and informal speech samples, and can thus generate responses in

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different registers. ChatGPT can thus use appropriate greetings and salutations, and incorporate polite language when necessary. And it can take into account the context and purpose of the conversation to determine the appropriate level of formality.

Interactional Verisimilitude One of the most difficult aspects of traditional classroom and print textbookbased FLT is its ability to recreate conversational activities in the classroom or in the language lab that are verisimilar to real-life conversations in the FL. With AI systems this has become a more achievable goal, as the conversations generated by ChatGPT in this and previous chapters clearly indicate. Using chatbots does not completely solve the problem of recreating real communication in FL situations, but it certainly expands the ability of previous technologies, such as language labs, and materials such as films and DVDs, to create more realistic conversational simulations. ChatGPT makes relevant pragmatic content creation effortless through its ability to respond in a communicatively natural way to prompts in the FL. It can thus help provide learners with personalized instruction and practice and teachers with updated conversational materials and interactions. In sum, the main value of ChatGPT in the domain of communicative competence teaching is that it is a realistic FL conversational partner, allowing learners to practice real-life dialogues in the FL in a continually updated way.

Focused Interactions The examples used in this (and previous) chapters of human-chatbot conversations showed the ability of ChatGPT to focus on specific themes, based on real-life situations. With suitable speech recognition and production plugins, the FL conversations can also be practiced orally. Moreover, the chatbot never tires of being asked to repeat or go over something. The algorithm has what is called a spaced repetition capacity, which allows it to move words in a conversation from its short-term memory to its long-term memory through time intervals set by users for purposes of repetition and review.

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Chatbots also allow pictures of different objects as prompts, for which they can provide the matching words as well as explanations as to their meaning or cultural relevance in the FL. There are now also virtual reality apps that can be incorporated into chatbots allowing students to interact in a simulated FL environment. With focused text, images, sounds, and simulated virtual reality (VR) functions and components chatbots offer a truly expanded multidimensional humanoid language lab experience that can be adapted constantly to specific learning objectives. VR apps are designed specifically for synchronous language teaching and learning that allows students to interact in different FL locations (a restaurant, a doctor’s office, the airport, in a schoolroom, etc.). Users can thus converse with a virtual passenger on an airplane, order cultural dishes in a restaurant, or check into a hotel. VR technology is an effective learning tool, providing the immersive experience that traditional FLT has been incapable of providing in the typical classroom; it clearly fits in with McLuhan’s classroom without walls concept (chapter 2). The question that has not been asked so far is whether or not students themselves favor or, at least, appreciate the use of chatbots in FL-learning. A study conducted in 2019 by Yin and Satar (2020) presents relevant results. The participants were Chinese-speaking learners of English, who differed in their level of linguistic and communicative knowledge (elementary versus advanced) and who were given the task of interacting with two types of chatbots: (1) the pedagogical chatbot “Tutor Mike” and (2) the conversational chatbot Mitsuku.” Tutor Mike would teach formal aspects of the English language (pronunciation, grammar, vocabulary), while Mitsuku was a chatbot with which learners could converse in English. The conclusions of the study are as follows: •

•

• •

Mitsuku was preferred by participants with advanced knowledge of English, since it offered an opportunity for conversation that was verisimilar to person-to-person interaction. The interactions with Mitsuku also proved relaxing for students with poor English skills, who said that they were not afraid of being laughed at during such interactions (as could occur in a classroom with an audience of peers), emphasizing that in this case they did not suffer from performance anxiety. Tutor Mike was most helpful for students with lower language levels. However, after some time, Mitsuku's speeches were perceived as predictable, redundant, and unemotional by many students.

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Despite a few critiques and shortcomings, such as the last one above, a commonly-perceived benefit by all the students was the immediate feedback that both chatbots offered, patiently repeating the information that students asked of them. The ability of both chatbots to learn from the interactions with users, adapting to different learning styles and utilize the appropriate registers of communication, was also seen as highly important. The study also showed that chatbot-based training can be made to focus on FL culture. To give a practical example, let us assume that a student of Italian is doing a project on Dante’s Divine Comedy. The learner can ask ChatGPT to clarify the meaning of some words and various passages in the text, since they come from a different era (the medieval ages). The learner can then ask for information about the author of the text as well as information on his other texts. The student could obviously get the same information through a search engine; but with ChatGPT, it gets more immediate and contextualized responses. Moreover, ChatGPT understands what students want to say, even if they do not say it explicitly, responding consistently to what is implied in a prompt, even if not stated overtly. In effect, chatbots can answer questions related to culture to which a teacher may not have immediate access, or may not even know, making realistic cultural training a more focused goal. Examples of relevant prompts that a chatbot can easily answer in culturespecific terms (paraphrased from Álvarez-Morillo 2023): Prompt: Prompt: Prompt: Prompt: Prompt:

Tell me about traditional celebrations and festivals in Mexico. What are some popular traditional dishes or cuisine in Japan? Can you explain the importance of family in Brazil? What are famous landmarks or historical sites in Germany? What customs or etiquette practices are essential in France?

On the whole, the interactional possibilities that in the past could only be realized with great effort outside the classroom through immersion or programs abroad in the foreign country are now within reach in the classroom with chatbots. Of course, virtual worlds cannot replace real worlds, but they certainly can help students grasp aspects of the FL culture that would have been difficult to grasp via traditional classroom pedagogy, the use of a print textbook, and the language lab of conventional FLT. As Alessandro Lenci (2022) states [translation mine]:

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Why is this “new AI” important for the study of language? Because it is closer to the way humans learn and use language. Cognition like communication is multimodal, and language learning takes place in a context that is inherently interactional. In addition, language is not learned in a supervised manner, using data explicitly labeled by others. Therefore, research on AI that focuses on multimodality, on learning models closer to human ones and able to learn from realistic and contained amounts of data, is certainly something that we linguists can and, I would add, must look at with interest.

Interactional Pedagogy In a comprehensive review of AI chatbot systems for EFL (English as a Foreign Language), Zhai and Wibowo (2022) found that they improved significantly the ability of students to use English in communicatively accurate ways. Similarly, Sabzalieva and Valentini (2023: 7) found that AI-assisted FLT made interactional pedagogy a reality, thus helping teachers enhance students’ overall communicative competence in the FL. The real-life accuracy of the dialogues that a chatbot creates is what makes them valuable tools for gaining access to cultural reality and ongoing shifts in FL culture. When ChatGPT is integrated with other classroom activities, the relevant research further indicates that the blend of classroom-based and personalized AI dialogue practice allows for the effective development of communicative competence in the FL. As Skjuve and Brandtzaeg (2019) observe: The increasing usage of chatbots is fundamentally changing the way people interact with new technology. Instead of clicking buttons to functionally navigate on a web page, people can access content and services by the use of natural language in interaction with an artificial agent (e.g., chatbot). This change toward human–chatbot interaction is typically manifested through a social and natural conversational style. This shift of how to interact with data and services has major repercussions for how to explore and measure conversational user experience with chatbots.

As I discovered with ChatGPT, the chatbot can be fine-tuned to understand and respond to specific inquiries in a way that feels natural in Italian (the FL in this case). This can clearly help to reduce the workload of

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the teacher by handling a large volume of simple queries by students, as a virtual native speaker. ChatGPT thus allows teachers to establish an interactional pedagogical system in the classroom that would have been a dream in the past. The chatbot can also schedule briefing sessions with individual students, set reminders as to when tests or other activities are scheduled, and even produce the course syllabus according to given criteria. There are, of course, some limitations and caveats that emerge with such chatbot-oriented pedagogy, some of which will be discussed in more detail in the final chapter. One of these is the potential for the chatbot to perpetuate or even amplify biases that exist in the data on which it is trained. This can lead to the generation or reinforcement of harmful stereotypes that are often associated with the FL culture and its people. Since chatbots now have the incredible ability to reprogram (recode) themselves, they can, however, be asked to eliminate prejudices and biases from their dialogues, even though it is hard for the chatbots to do so without substantial retraining.

Epilogue By asking ChatGPT to decompose and explain a simple script-like conversation into its pragmatic, linguistic, and conceptual components, it will do so in a generic way. If more pointed and focused questions are involved, then it can be asked more specific research questions. Some of these are listed below: 1. What aspects of FL dialogues are different from corresponding NL ones in terms of register and appropriate level of speech? 2. Which words in a FL dialogue refer to the same objects in the NL one differentially? How can this be resolved? 3. How can FL dialogues be classified in terms of their social function? Can you give examples of each one in the FL? 4. How can the FL parts of speech be tagged for social function? 5. How do FL speakers identify relationships among named entities in a dialogue? The main point of this chapter has been that conversations that take place between chatbots, students, and teachers can enhance the acquisition of communicative competence. As such, the chatbots are highly useful as pedagogical tools, both for the learner and the teacher, streamlining, at the

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very least, the process of FL-learning. They have made the language lab, print textbooks, and dictionaries virtually obsolete. Conversations with a chatbot can also be tailored according to personalized needs. For instance, one might chat about food and recipes, choosing to interact virtually in the FL with a “chef” bot; one can talk about healthcare by asking the chatbot to take the role of a FL “doctor;” and so on. The question becomes not when to integrate AI technologies into FLT but how—a question that will be taken up in more detail in the remaining two chapters.

Chapter 5

Enhancing Conceptual Competence Prologue A common persistent problem manifested by FL learners is the tendency to put together expressions or messages with FL words and phrases that reflect NL concepts, not FL concepts. In other words, they are conveying the thoughts they form in the NL through the FL structures they are learning. For example, the present author would often hear his beginning students of Italian say things such as “Io sono caduto in amore” which was their Italian version of the English concept “I fell in love.” The conceptually-appropriate expression in Italian would be “Mi sono innamorato.” The interference process in this case inheres in the transfer of a concept that is articulated in a specific way in the NL to the FL via a literal word-for-word mental translation; as a result the phrase “Io sono caduto in amore” is grammatically correct in Italian, but it reflects English, not Italian, meaning. The ability to form appropriate FL concepts with the new language is a type of competence that is different from purely linguistic or communicative competence, since it involves the ability to think about something directly in the FL without recourse to the ways in which the student thinks about it in the NL. It can be called conceptual competence. When the conceptual systems between the NL and FL overlap, the students’ FL utterance is accurate conceptually; when they do not, it is anomalous. As argued in previous work, aberrant utterances such as the one above lack “conceptual fluency” (Danesi 2017). Psychologically, they result from a process that can be called “conceptual calquing.” This is the tendency to assume that the structures of the FL bear the same meanings and grammatical features that the concepts carried by the structures in the NL do. Conceptual fluency is intertwined with the related notion of metaphorical competence, put forward for the first time in the mid-1980s (Danesi 1986) as a pedagogical response to the research findings and insights that were crystallizing at the time in the fledgling field of cognitive linguistics, and specifically, in the field of Conceptual Metaphor Theory (Lakoff and Johnson 1980, Lakoff 1987, Johnson 1987). The premise behind the coinage of the term was as follows: if metaphorical concepts formed the backbone of abstract

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thinking, as the relevant research was revealing, then metaphor could no longer be ignored within FLT. This chapter deals with conceptual competence—an ability that AI now seems to be have, as can be gleaned by the various ChatGPT responses and dialogues in previous chapters. As I also found out in the context of an AI research project in text analysis (Neuman, Danesi, and Vilenchik (2023), the new Transformers do indeed appear to have the ability to negotiate metaphorical speech because of extensive training on large datasets in which metaphor is abundantly present. Given that Conceptual Metaphor Theory has shown that metaphor use is systematic, not just idiomatic, then it is a small step to see why AI can tap into the relevant system via inductive learning (chapter 1). Chatbots can thus engage students in helping them enhance conceptual competence in the FL, in ways that are consistent with their use in helping learners develop linguistic and communicative competencies. Evidence that ChatGPT is now adept at processing metaphorical language is its use as a tool in literary translation (chapter 1), showing an ability to preserve the nuances and the verbal images that literary works contain, especially poetic ones. The task of literary translation has always been a complex and challenging one, entailing not only a profound understanding of the source and target languages, but also an appreciation of the cultural context in which a work is created and the stylistic idiosyncrasies of authors. Given the chatbot’s training on a vast amount of linguistic-literary data, it develops a nuanced understanding of the source language, and is thus able to produce translations that are not only highly accurate but also faithful in style and tone to the original work. But it should always be kept in mind that ChatGPT is an AI tool trained to recognize patterns in large quantities of text, and occasionally will come up with anomalies that are conceptually aberrant.

Conceptual Fluency Conceptual fluency theory, as mentioned, was motivated by the common observation that, although classroom-based student discourse often manifests a high degree of linguistic and even communicative fluency—that is, the ability to manipulate the formal grammatical and communicative structures of the FL—it seems to lack the expressive (conceptually-based) fluency that characterizes the discourse of native speakers. Students tend to use FL structures as “carriers” of their native language concepts. When the NL and FL conceptual systems coincide in some area of discourse, then the student

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utterance is serendipitously native-like; when they do not, it manifests anomalous conceptual content. What student discourse often lacks, in other words, is conceptual fluency. From the extensive research in cognitive linguistics since the late 1970s, it has become obvious that many concepts reveal metaphoric, metonymic, and ironic forms, and it is now an established principle that these are hardly subsidiary or decorative forms of speech. They are at the core of how we conceptualize the world (Danesi 2022). The pedagogical implications in this area cannot be ignored. Indeed, it can be claimed that the incorporation of conceptual competence training into an integrated pedagogical approach to FLT will likely make true proficiency in the FL a more realizable outcome of the student experience. The question of how to teach literal concepts in the FL has always had a fairly straightforward answer, from the Direct Method onwards. It has been normally assumed that they are best taught directly—namely, by “demonstrating” them through ancillary devices that allow their meanings to be illustrated in some concrete way (through pictures, simple dialogues, etc.). By and large, this type of pedagogy has always produced, and will continue to produce, good results in all kinds of classroom situations. The dilemma, it would seem, has always inhered in how to teach non-literal concepts. How does one teach students to use the concept of “love” appropriately when discussing it in the FL, given its abstract nature and many non-literal referential points? As discussed above, it is in such domains of meaning that NL conceptual transfer can be seen to occur. It is in this rea that ChatGPT presents itself as a potentially useful tool, because it tends to locate and use the conceptually-appropriate FL form and then explain how and why it is different from the NL form. So, when English-speaking students of Italian produce a phrase such as cadere in amore for “falling in love” (above) they are using their NL image schema of love as a trap, as it is called in cognitive linguistics, and transferring it to the conceptualization of Italian amore (“love”), which is based on a different image schema. This type of “conceptual interference” is more destructive of meaning flow than other kinds of errors present in student interlanguages, since it impugns meaning, not only grammatical or communicative structure. With advanced versions of ChatGPT, and as I myself discovered by prompting the chatbot to generate and explain metaphors of love in Italian, it is potentially a useful enhancer of conceptual competence, as can be ascertained in the translations it is able to cope up with, as Frąckiewicz (2023) has noted:

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Marcel Danesi The secret behind ChatGPT-4’s success lies in its use of deep learning techniques, which enable it to analyze vast amounts of text data and learn patterns and structures that are common across different languages. This allows the model to develop a sophisticated understanding of the nuances and subtleties that are unique to each language, making it better equipped to handle the challenges of literary translation. Moreover, ChatGPT-4’s ability to generate multiple translations for a given input allows translators to choose the option that best captures the spirit and tone of the original text, ensuring that the final result is both accurate and engaging.

Learning a language goes beyond grammar, vocabulary, and communicative scripts; it involves understanding the conceptual-cultural nuances that are imprinted in the FL, many of which are revealed via metaphor and other figurative forms. ChatGPT is of great significance in this area because it exposes learners to authentic language patterns and expressions, enhancing students’ ability to use the FL conceptually in real-world situations.

Cultural Meanings Proficiency in the FL cannot be pinned down simply to developing high levels of linguistic and communicative competence, as discussed. The presence of figurative language in common discourse, not as a decorative communicative tool, but as a basic form of conceptualization, makes the notion of conceptual competence a central one in any modeling of FLT. The gist of the research in Conceptual Metaphor Theory indicates that metaphor in particular is the vehicle that embeds and then expresses social and cultural phenomena of all kinds; in other words, the so-called conceptual metaphors that undergird a language, systematically reflect social emphases, perceptions, and are often a key to understanding specific social symbols and rituals. A conceptual metaphor is a thought formula that is imprinted and delivered via specific linguistic metaphors. For instance, individual metaphorical expressions such as “She’s a tiger,” “He’s an eagle,” and so on, are not random creative verbal artifacts; rather they are all based on the same conceptual metaphor—people are animals, which is used to depict variable human personalities via this formula. The same concept is also part of cultural representations, including names given to sports teams (Chicago Bears,

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Toronto Blue Jays), the use of animal stories to impart moral and ethical notions to children, and so on. Take, for instance, the love is a sweet taste conceptual metaphor in English, which can be seen in such common expressions as “She’s my sweetheart,” “They went on a honeymoon,” and so on. The instantiations of this conceptual metaphor do not stop at the level of language. Giving sweets to a loved one on Valentine’s day, symbolizing matrimonial love at a wedding ceremony with a cake, sweetening the breath with candy before kissing a paramour, and so on are all symbolic-ritualistic correlates of the same conceptual metaphor. Incidentally, in Chagga, a Bantu language of Tanzania, similar cultural practices exist. It is no coincidence that the language possesses the same conceptual metaphor. In Chagga the man is perceived to be the eater and the woman his sweet food, as can be detected in expressions that mean, in translated form, “Does she taste sweet?” “She tastes sweet as sugar honey” (Emantian 1995: 168). The research has shown, overall, that material, symbolic, and ritualistic culture is itself a mirror of metaphorical thinking. Conceptual metaphors are also a trace to the historical processes that characterize a society’s fund of unconscious knowledge. A common proverb like “He has fallen from grace” refers historically to early religious narratives. Today, we continue to use it with only a dim awareness (if any) of its origins. Conceptual metaphors that portray life as a journey—“I’m still a long way from my goal,” “There is no end in sight”—are similarly rooted in such narratives. As the Canadian literary critic Northrop Frye (1981) pointed out, one cannot penetrate such expressions without having been exposed, directly or indirectly, to the original stories. These are the source domains for many of the conceptual metaphors we use today in English for judging human actions and offering advice, bestowing upon everyday life an unconscious metaphysical meaning and value. When we say “An eye for an eye and a tooth for a tooth” we are invoking imagery that reverberates with religious meaning in a largely unconscious way. Every culture has similar proverbs, aphorisms, and sayings. They constitute a remarkable code of ethics and of practical knowledge that anthropologists call “folk wisdom.” Indeed, the very concept of wisdom implies the ability to apply proverbial language insightfully to a situation. Clearly, the kind of competence that involves thought patterns based on embedded cultural ideas and traditions goes beyond the rules of grammar and communication. The intent of the notion of conceptual competence is to stress how cultural knowledge is expressed through metaphor and other forms of verbal figuration. In any interaction, conceptual competence provides the

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means (words, phrases, nonverbal cues, etc.) through which interlocutors can feel a shared sense of cultural meanings, while communicative competence provides the means (protocols, scripts, speech acts, etc.) for concretely realizing these meanings in a specific context. Proficiency can thus be defined as the ability to control the relevant conceptual and communicative systems of a language. As Andreou and Galantomos (2009: 2) aptly observe: “Conceptual competence is neither an isolated nor a simplified phenomenon. Rather, it is an indispensable component of the wider notion of communicative competence and a complex set of specific sub-competencies.” To further explore what conceptual competence entails, even at a superficial lexical level, consider the following utterances: 1. 2. 3. 4. 5.

I apologize for not coming to the party yesterday. I promise I will make it up to you later. I thank you for understanding. I’ll bet you that there will be another occasion. I warn you to beware of their advice.

These utterances illustrate the ways in which distinct kinds of speech acts—apologizing, promising, thanking, betting, and warning—are expressed in English. Now, an interlocutor must know not only the actual expressive forms, but also their different conceptual nuances. Apologizing for not attending a party is different from apologizing for stepping on someone’s foot. Similarly, promising that you will make something up later is quite different from promising that you will never have another glass of beer. Thanking someone for understanding is different from thanking a server for providing a cup of coffee that has been requested. Betting that there will be another occasion is vastly different from betting one’s entire wages on a horse race. And, finally, warning someone about the danger of someone’s advice is rather different from warning someone that they are in trouble with the law. In effect, the pragmatic dimension in utterances subsumes the conceptual-cultural dimension and, in its implicature for joint action. The founder of speech act theory, John Austin (1962), observed that, when used in the first person of the present indicative, verbs like promise, apologize, thank, bet and warn can be used performatively to realize a speech act. Languages make available a vast array of performative devices for attaining joint actions in verbal communication. For instance, in English (1) below is a more appropriate way of making a hospitable offer than the blunt utterance in (2):

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1. How about a beer? 2. Have a beer! But in other cultures this may not be the case. Wierzbicka (2003: 62) points out that those who may not be acquainted with the conceptual-semanticpragmatic conventions of English speaking-cultures, might well interpret (1) as a genuine question rather than an invitation, and would certainly consider (2) a more appropriate type of offer expression. Now, this may seem to be a problem in lexical choice and it pragmatic consequences, but a closer consideration will show that each type of expression has a conceptual history of usage within a culture that entails different modes of understanding the world (which need not concern us here, see Danesi 2017). Using the concepts of a different culture through the learning of its language involves what psychologists call a Theory of Mind, the capacity to understand other people by ascribing mental states to them and, by extension, the meaning of their culture-specific expressions. This includes the knowledge that others’ beliefs and thoughts may be different from one’s own. This comes out in the fact that the appropriate use of metaphor in a language, which is largely unconscious, is based on knowing how the other person sees the world. As Tonini, Bischetti, Del Sette, Tosi, Lecce, and Bambini (2023) have noted, “metaphor resolution across tasks” implies a Theory of Mind, since it provides better access to the psychological lexicon (i.e., terms referring to mental states) and better context processing, serving as a springboard to achieve sophisticated pragmatic skills.” Now does ChatGPT have a Theory of Mind (Holterman and Deemter 2023)? Of course is does not, in any human sense. But the research shows that the chatbot can arrive at correct answers to a conceptual dilemma more often than would be expected based on chance: “although correct answers were often arrived at on the basis of false assumptions or invalid reasoning.” Whatever the case, the process of guessing based on informed metaphorical knowledge, via inductive training, brings the usefulness of the chatbot tool closer to the task of training students to develop conceptual competence through exposure to the system of conceptual metaphors present in the FL. On the technical side, Wachowiak and Gromann (2023) have shown how language models, such as GPT-3, have the ability to “detect metaphoric language and predict the metaphor’s source domain without any pre-set domains,” which provides the technical reason why a chatbot can converse in conceptually-fluent ways in a FL. Significantly, the researchers found that “GPT-3 generates the correct source domain for a new sample with an

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accuracy of 65.15% in English and 34.65% in Spanish.” While this may seem to be a low rate for conceptual competence training, it is actually rather high when compared to the kinds of errors that students make in this area. However, the caveat issued above must be reiterated here—AI thinking is not identical to human thinking and thus will come up with anomalies, of which teachers and students must be constantly on the lookout for. As Memisevic (2022) aptly observes: Neural networks lack the kind of body and grounding that human concepts rely on. A neural network’s representation of concepts like “pain,” “embarrassment,” or “joy” will not bear even the slightest resemblance to our human representations of those concepts. A neural network’s representation of concepts like “and,” “seven,” or “up” will be more aligned albeit still vastly different in many ways. Nevertheless, one crucial aspect of human cognition, which neural networks seem to master increasingly well, is the ability to uncover deep and hidden connections between seemingly unrelated concepts and to leverage these in creative and original ways. As the level of abstraction rises at which we train our networks, so does the level of capability they surprise and amaze us with.

Calquing The transfer of NL concepts to FL forms can be called calquing (Danesi 2017). In the case of a positive transfer, the conceptual calque from the NL onto the FL produces an isomorphism. So, the English sentence, “They are going to school” can be calqued onto the Italian corresponding sentence, word-perword; “Loro vanno alla scuola.” So, “going to school” in English coincides conceptually with andare a scuola in Italian and thus the two utterances coincide in meaning and syntactic structure. Now, the concept of “skipping school” requires a conceptual adjustment, given the tendency of English speakers to calque this NL concept onto Italian as saltare la scuola, whereas the actual concept in the FL is formulated with the expression marinare la scuola. There have been attempts to identify these particular types of errors outside of a conceptual competence framework, as, for example, simple NL lexical transfers (Laufer and Waldman 2011, Peters 2016). While the end result is indeed a lexical error, the process may well involve different conceptualizations, which involve different image schemas (“skipping” versus

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“marinating”). Conceptual competence is not a synonym for semantic and lexical knowledge. It involves the ability to navigate figurative referential source domains that are critical in a conceptual-cultural sense. An example of how ChatGPT can be used in this area is provided by Caulfield (2023). Prompt:

ChatGPT:

Prompt: ChatGPT:

Prompt: ChatGPT:

Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: la cultura argentina. ¡Claro! Estoy listo para hablar contigo en español. Comencemos hablando sobre la cultura argentina. ¿Cuál es tu aspecto favorito de la cultura argentina? ¿Te gusta el tango, el fútbol o la comida? Disfruto de la música de tango, pero no soy muy buen bailarín. ¡Qué genial que te gusta la música de tango! No es necesario ser un buen bailarín para disfrutarla. ¿Tienes alguna canción de tango favorita o algún intérprete que te gusta escuchar? No soy un experto ¿Tienes sugerencias de músicos de tango populares? ¡Claro! Aquí tienes algunas sugerencias de músicos de tango populares para que puedas explorar su música: Carlos Gardel, Astor Piazzolla, Aníbal Troilo, Osvaldo Pugliese y Juan D’Arienzo. Estos son solo algunos, ¡hay muchos más para descubrir!

The conversation was, clearly, conceptually fluent, with no calques from negative transfer evident in it. One could explain the use of tener (“to have”) in tienes algunas sugerencias as part of an idiomatic expression, but even so, its selection in terms of the pragmatic context is remarkably accurate conceptually. In fact, throughout the conversation tener became a metaphorical fulcrum on which the whole dialogue revolved conceptually. So too were conceptualizations verbalized with disfrutar. It is interesting to note that the notion of calquing in FL-learning goes as far back as the nineteenth century. In an 1894 article, linguist Louis Duvau made the following revealing comment in this regard:

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Marcel Danesi Another phenomenon of hybridization is the creation in a language of a new word, derived or composed with the help of elements already existing in that language, and which is not distinguished in any way by the external aspect of the older words, but which, in fact, is only the copy (calque) of a word existing in the mother tongue of the one who tries out a new language.

Metaphorical Competence Conceptual Metaphor Theory has been the target of extensive research since the publication of George Lakoff and Mark Johnson’s book, Metaphors We Live By. In it, the two scholars differentiate among metaphor, metonymy, and irony correctly. However. As Sebeok and Danesi (2000) have suggested these can be subsumed under a general conceptual competence, whereby meaning is encoded by different mapping processes: (1) from one domain to another (metaphor), (2) from an element in the domain onto the whole domain (metonymy), and (3) from a contrastive domain onto another so as to highlight it (irony). In this way, abstract concepts can be treated as based on mappings of different kinds: in metaphor the mapping occurs from a source domain (animals) to a target domain (people) to produce a conceptual metaphor (people are animals); in metonymy, a part of the domain (White House) is mapped onto the entire domain (American government); and in irony, a contrast (nice) is mapped onto a real world event (hurricane) so as to highlight its negative impact (“Nice hurricane, isn’t it?”). Together, these three forms of meaning-making have been called part of metaphorical competence (Danesi 1986), although a better term might be mapping competence. Metaphorical competence makes up a large chunk of conceptual competence, and is a major factor in the development of conceptual fluency in the FL. Take, for example the metaphorical statement “The professor is a snake,” which is an instantiation of the conceptual metaphor people are animals. In this case, perceived animal features are mapped onto the concept of human personality, and this is the reason why specific linguistic metaphors such as “He is a snake,” “She is a tiger,” My friend is a puppy,” and so on, will make sense. The meaning of snake that the statement encodes, however, is not its concrete, denotative one. Rather, it encodes the culture-specific connotations perceived in snakes; namely “slyness,” “danger,” “slipperiness,” etc. It is this complex of connotations that is implied in the utterance “The professor is a snake.” Each different instantiation of the conceptual metaphor

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changes the view we get of the person: for example, in “The professor is a gorilla,” the professor is portrayed instead as someone “aggressive,” “combative,” “rude,” etc.—a complex of connotations that are implicit in the new mapping of the animal source domain (gorilla). Now, if the learner of English speaks a NL that does not have this conceptual metaphor, such expressions will literally make no sense. Once a conceptual metaphor has been introduced into the system of concepts that makes up a culture, then it becomes itself a source for providing further descriptive detail to evaluations of, say, human personality, if such a need should arise. Thus, for instance, the specific utilization of snake as the source domain can itself become a subdomain (made up of types of snakes), allowing one to zero in on specific details of the personality being described: 1. He’s a cobra. 2. She’s a viper. 3. Your friend is a boa constrictor. Within each source domain, therefore, there are subdomains that provide the user with an array of connotative nuances that can be utilized to project subtle detail onto the description of a certain personality. This type of knowledge is what metaphorical competence is essentially about.

Conceptual Metaphor Conceptual fluency theory has led to two key research findings that are highly relevant to FLT with implications for the use of AI for the development of FL conceptual competence. First, metaphorically-trained groups of students have no difficulty accessing the FL conceptual system; second, students show a remarkable ability to apply conceptual metaphors to novel communicative tasks in native-like ways. The ChatGPT dialogues in this and previous chapters show a high degree of conceptual fluency, if examined carefully. In the dialogue above the use of tener as a metaphor of “having” or “possessing” something as if it were a substance is a key image schema in Spanish. By simply interacting with ChatGPT the student can thus see how this metaphor can be used literally in “communicative action.” This implies that input factors are critical to the unconscious acquisition of concepts, as Krashen and others have shown outside of AI-based training (chapter 2). In a relevant study worth mentioning here, Shirazi and Talebinezhad (2013) exposed a group of Iranian

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learners of English to metaphorical utterances systematically and routinely, rather than exceptionally (as idioms). At the end of the instruction period they found statistical significance in the learners’ use of metaphor in a native-like fashion. To reiterate, the importance of metaphor to language and thought came to the forefront in the 1980s, after the publication of Lakoff and Johnson’s groundbreaking book (above), which documented the fact that abstract meaning emerges typically via mappings from concrete source domains to abstract target domains. The relevant research has shown that the human mind seeks to understand reality by blending domains of meaning through experiential-imaginative processes. For example, by linking animals to human personality, we are seeking to understand the latter in terms of the former. This is why we interpret sentences such as “He’s a fox,” “She’s an eagle,” and so on, as personality constructs. It is essential to clarify what the notion of metaphor implies in this framework. In traditional rhetoric there are many kinds of figures of speech, and metaphor is considered to be simply one of them. But within Conceptual Metaphor Theory many of these are seen as manifestations of the same type of reasoning (mapping) that is exemplified by conceptual metaphors. Thus, for example, personification (“My cat speaks Italian,” “Mystery resides here,” etc.) is viewed as having the reverse mapping structure of the usual conceptual metaphor—namely, mapping human qualities onto the animal target domain. However, as discussed, metonymy and irony are viewed as separate cognitive phenomena and thus treated separately (see below). The psychological source of conceptual metaphors is traced to a mental mechanism called the image schema, mentioned several times above (Lakoff 1987, Johnson 1987, Lakoff and Johnson 1999). This is a mental percept that extracts common elements of an action, object, event, etc., becoming the guide to the metaphorical mapping. Image schemas such as up-versus-down, back-versus-front, near-versus-far, etc., are seen as undergirding the formation of concepts such as happiness (“Lately my spirits are up”), responsibility (“You have to face up to your problems”), life (“You have a long life ahead of you”), love (“They are very close to each other”), among many others. An image schema is something that gives mental outline form to experience. For instance, the common experience of how containers work and what they allow us to do underlies such concepts as mind (“My mind is full of good memories”), emotions (“My heart is filled with hope”), and so on. Obviously, it is impossible to determine which came first—the metaphor or the schema. Perhaps this is a moot question, since the occurrence of a metaphor implies a specific image schema and vice versa.

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Now, the point is that this kind of thinking, as expressed through conceptual metaphors, is not random, irregular, or something derivative of more basic literal speech. It is the core of abstract cognition, and is thus systematic. This implies that metaphorical competence can be taught as can any other competence in FL-learning environments, even if just via exposure to metaphorically-rich input. An extensive research study by Norafkan (2013) investigated the effect of exposure to authentic English language materials on learners’ metaphorical competence on the part of Iranian students. She divided 53 learners into experimental and control groups. Standard textbook materials were used with the control group while culture-based, metaphorically-rich materials and instruction by trained native speakers were employed with the experimental group. Both groups improved in their English language proficiency, but the results of the study at the post-test stage showed that the metaphorically-trained learners showed a significantly higher level of conceptual competence. The pedagogical problem comes down to setting up input and devising instruction that takes conceptual metaphors into account, which is something that ChatGPT can do instantly, as I myself found out. For instance, the chatbot can be prompted to do the following: • •

•

select source domains (sweetness, animals, colors, and so on) and then provide primary utterances in which these are used in Italian; select target domains (emotions, personality, perception, and so on) that are representative of specific types of speech acts or conversational themes based on the relevant source domains; search for common texts with these expressions on the Internet and then organize them according to image schema categories so as to make them ready for teaching.

Problems did emerge with ChatGPT’s responses to these prompts, but they were not intractable, with the chatbot easily revising its responses on the basis of further prompts. The main problem was that it was difficult for the chatbot to come up with the themes required for a specific teaching unit. But then these can be easily devised by a human teacher.

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Metonymy and Irony As mentioned, there are two figures of speech that are treated differentially from metaphor within Conceptual Metaphor Theory—metonymy and irony. Metonymy reveals a part-whole reasoning process, whereby an element in a domain is used to stand for the entire domain: 1. 2. 3. 4. 5. 6. 7.

She loves Austen (= the writings of Jane Austen). There are many new faces around here (= people). My dad doesn’t like nose rings (= people with nose rings). They bought a FIAT (= car named FIAT). The buses are on strike (= bus drivers). The Church does not condone infidelity (= theologians, priests, etc.). The White House made another announcement today (= the president, the American government).

Irony constitutes a discourse strategy whereby words are used to convey a meaning contrary to their literal sense; it is realized by mapping source domains (love, enjoyment) onto target domains (torture, torment) incongruously, so as to emphasize meaning by contrast: 1. I love being tortured. 2. They love getting hurt. 3. He enjoys torment. There is, of course, much more to irony than this assessment. But the discussion would be beyond the present objective. As I found out with the use of ChatGPT for Italian pedagogy, AI does indeed detect patterns and relationships between words and phrases based on irony or sarcasm, but finds it challenging, which actually mirrors the human decipherment of such language. That said, ChatGPT is programmed to learn from its interactions with users, and, as I kept getting it to detect irony, it seemed to improve its ability to do so.

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Contrastive Conceptual Analysis Within the framework of conceptual competence, it is a straightforward pedagogical task to identify the relevant image schemas and source domains that are central to its development in the FL. This can be done initially by getting the chatbot to identify the isomorphic and contrastive source domains between the NL and the FL. If NL and FL concepts use identical source domains (in part or in whole), and similar image schematic mappings, then this would normally entail positive transfer. For example, friendship and love tend to be delivered in terms of identical (or similar) source domains, based on specific image schemas, in English and Italian, as corroborated, incidentally, by the chatbot I used: depth English: Italian:

Theirs is a profound friendship/Theirs is a profound love. La loro amicizia è profonda/Il loro amore è profondo.

duration English: Italian:

Theirs was a brief friendship/Theirs was a brief love affair. La loro amicizia fu breve/La loro storia fu breve.

directionality English: Their friendship is continuing on/Their love affair is continuing on. Italian: La loro amicizia sta continuando/La loro storia sta continuando. taste English: Italian:

Theirs is a sweet friendship/Their love is sweet. La loro amicizia è dolce/Il loro amore è dolce.

Contrasts in conceptualization between the NL and the FL, however, can lead to negative transfer, as in other areas of FL-learning. Consider the use of avere (“to have”), fare (“to do, make”), and essere (“to be”) in expressions that involve the states of “coldness” and “hotness” in Italian (the FL) and English (the NL): Fa freddo/caldo fuori. = It is cold/hot outside. Lui ha freddo/caldo. = He is cold/hot.

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Marcel Danesi Il caffè è freddo/caldo. = The coffee is cold/hot.

The different verbal selections, together with the underlying conceptual patterns that occasioned them are common sources of negative transfer. In Italian, the verb fare is used with respect to a weather situation—fa freddo (literally) “it makes cold,” fa caldo (literally) “it makes hot.” The physical state of “coldness” or “hotness” is conveyed instead by the verb essere when referring to objects (è freddo “it is cold,” è caldo “it is hot”); and by avere “to have” when referring to people: lui ha freddo (literally) “he has cold,” lei ha caldo (literally) “she has hot”. The use of one verb or the other—fare, essere, or avere—is motivated by an underlying image schematic conceptualization of bodies, objects, and the environment as containers and the states of “coldness” and “hotness” as entities. The container and entity schemas are the sources of the differential verbal selections. If the container is the environment, then the entities (“coldness” and “hotness”) are made by Nature (fa freddo, fa caldo); if it is a human being, then the individual’s body has them (ha freddo, ha caldo); and if it is an object, then the object is their container (è freddo, è caldo). No such conceptual distinctions are required in English. This type of conceptual contrastive analysis can thus be employed to explain an error of the type Lui è freddo, which can be traced to a negative conceptual transfer (“He is cold”). Suffice it to say here that ChatGPT produced conversations about hot and cold states in Italian that involved the correct image schemas and their relevant verbs. When asked to compare these with English expressions, it came up with an explanation that was based on idiomaticity rather than on metaphorical conceptualization, which was anticipated. It is up to the teacher to continue from where the chatbot left off, as will be discussed in the next chapter.

Translation Analysis Perhaps the best way to unravel if ChatGPT is able to negotiate and process conceptual systems in the FL is to see how it translates texts from one language to another, which is an indirect form of contrastive conceptual analysis. A case-in-point is the following Spanish-English translation task asked of ChatGPT (from https://www.translateplus.com/blog/chatgpt-translatingcontent/):

Enhancing Conceptual Competence Prompt:

ChatGPT:

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Translate “Descubre todas las actuaciones musicales, horarios, estrellas que entregan galardones, favoritos y un concurso: una guía de la 37ª edición de los Goya” into English. “Discover all the musical performances, schedules, stars that award prizes, favorites, and a contest: a guide to the 37th edition of the Goya Awards.”

The response was conceptually correct, since ChatGPT translated the verb entregar as “to award” rather than as “to win” or “to receive.” The text that was translated into English by ChatGPT was a tweet by the newspaper El País, which was posted before the event so that the winners were not known at that point in time, as ChatGPT correctly interpreted. So, the translation “awardwinning stars” would not have been conceptually correct. In an excellent study of translation with ChatGPT, Claire Frances and Giovanni Zimotti (2023) looked at how the chatbot handles queries regarding translation, as for example the following Spanish example, based on an actual course (Módulo 1): Prompt:

ChatGPT:

Escribe una reflexión sobre tu experiencia con el español en tu vida. Para el primer proyecto, que entregarás al final del Módulo 1, vas a reflexionar sobre tu experiencia con el español. Mi experiencia con el español ha sido muy importante en mi vida. Desde pequeña, he estado rodeada de la lengua española ya que mis padres son de América Latina y siempre hablamos español en casa. Además, tomé clases de español en la escuela durante toda mi educación primaria y secundaria. Aunque he tenido la oportunidad de hablar español con frecuencia, siempre he sentido que hay mucho más que puedo aprender y mejorar. He tenido la oportunidad de viajar a algunos países de habla hispana y siempre me ha sorprendido la riqueza y la diversidad de la lengua española. Cada vez que viajo a un nuevo lugar, me encanta escuchar cómo se habla el español de manera diferente y aprender nuevas palabras y expresiones.

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Marcel Danesi En general, me siento muy afortunada de haber crecido rodeada del español y estoy emocionada de seguir aprendiendo y mejorando mi habilidad en esta lengua. Creo que el español es un idioma muy importante en el mundo de hoy y estoy agradecida de tener la oportunidad de practicarlo y mejorarlo constantemente.

In order to assess the conceptual correctness of the translation, the two researchers asked a Spanish instructor for feedback. The teacher graded it as perfect, grammatically, lexically, and conceptually. Another instructor stated that the essay was written with a native-like command of the language. Clearly, the chatbot passed the translation test with flying colors. Similar experiments using translation as a means for contrastive analysis have shown a consistency of results. Of primary importance, pedagogically, is that the chatbot provides a model to the learner of how to negotiate a type of writing that is conceptually fluent, which in itself is a remarkable feat.

Metaphor Plugin Significantly, ChatGPT offers the possibility of installing a so-called “metaphor plugin” (Halder 2023), a software component that adds metaphorical abilities to the chatbot’s repertory. As I discovered in the case of Italian, the plugin was able to generate appropriate conceptual metaphors, based on relevant image schemas, such as those discussed above in this chapter. The plugin also allowed me to customize the generated metaphors according to specific requirements, such as when I prompted ChatGPT to speak about love in Italian, which it did successfully. Suffice it to say that for the teacher of a FL, the chatbot with a metaphor plugin makes conceptual language teaching much more concrete and systematic, given the ability of the chatbot to navigate the Internet and come up with the image schemas that are closely tied to a pedagogical request. Some metaphor-based prompts are provided by Halder (2023), and are worth repeating here for the sake of illustration, since they can be easily adapted to FL pedagogy more generally: Prompt: Prompt:

Generate a metaphor for a difficult journey in Spanish. I need a metaphor to describe a peaceful morning in Italian.

Enhancing Conceptual Competence Prompt: Prompt: Prompt: Prompt: Prompt: Prompt: Prompt:

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Can you provide a metaphor for an intense competition in French? Generate a metaphor for love in Farsi. I need a metaphor to describe a chaotic situation in Mandarin. Can you provide a metaphor for growing old in Arabic? Generate a metaphor for a strong friendship in Greek. Generate a metaphor for hope in Polish. Can you provide a metaphor for the passage of time in Hungarian?

Continuous research on chatbots is showing more and more how their specific type of training contributes to making them achieve a relatively high level of conceptual fluency. A study by Zheng, Song, Hu, Fu, and Zhou (2020) showed how work in this area is expanding and leading to significant results; they describe their research as follows: Our work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences. By embedding the framework into a chatbot system, we then enable the chatbot to communicate with users using figurative language. Human annotators validate the novelty and properness of the generated metaphors. More importantly, we evaluate the effects of employing metaphors in humanchatbot conversations. Experiments indicate that our system effectively arouses user interests in communicating with our chatbot, resulting in significantly longer human-chatbot conversations.

Epilogue Because ChatGPT has the ability to access conceptually fluent language on the Internet, it is a truly useful pedagogical tool for imparting conceptual competence via specific input prompting. As discussed various times, a chatbot cannot replace human teachers, for the simple reason that the latter are ultimately the interpreters of what is conceptually correct in the FL (discussed further in the next chapter). The development of conceptual competence in the FL is not something that comes about only spontaneously; it requires exposure and pedagogical treatment in the classroom, as any other FL skill also requires. Linguist Ronald

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Langacker (1987, 1990, 1999) has argued that even the parts of speech themselves are the result of image schemas working unconsciously below the surface of grammatical structure. Nouns, for instance, encode the image schema of a region. Thus, a count noun such as baseball is envisioned as referring to something that involves a bounded region, and a mass noun such as rice, a non-bounded region. Now, this difference in image schematic structure induces grammatical distinctions. So, because bounded referents can be counted, the form baseball has a corresponding plural form baseballs, but rice does not. Moreover, baseball can be preceded by an indefinite article (a baseball), rice cannot. The gist of the research suggests that literal meaning occurs when words are used in isolation; but when they are combined into utterances, they tend to accrue metaphorical meanings, and that these influence grammar and communication. As Christiansen and Kallens (2022) note, unlike the carefully scripted dialogues found in textbooks, the language of everyday conversation tends to be based on variable figurative meanings. As the two scholars state, chatbots have the capacity to tap into these meanings and, as they remark, “astonishingly, they all do it without the help of grammar.” However, grammar does play a role in conceptualization, as Langacker showed. A chatbot is trained to predict the next word in a sentence given what came before across hundreds of billions of words from the Internet, print books, and Wikis (such as Wikipedia). When it makes a wrong prediction, it has the ability to adjust its parameters with an automatic learning algorithm. So, it does have its own grammar—one based on network connectivity rather than any sequencing of words in terms of parts of speech. We hardly realize how prevalent metaphorical language is in everyday speech. When asked how we feel about something, we might say that we are “cool about it,” or that we are “lukewarm about it.” These expressions reveal that we perceive feeling in terms of the sensations we experience in response to physical conditions that we have named cool and lukewarm. As psycholinguist Roger Brown (1958: 154) aptly puts it: The quality is first detected in one sense modality and is named at that stage. Afterward the quality is detected in many other phenomena that register with other senses. The original name tends to be extended to any experience manifesting the criterial quality.

Now, the chatbot does not have a body, so it cannot register anything via the senses. But it simulates (artificially) how this might occur via a process

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called conceptual blending in the human brain (Fauconnier and Turner 2002, Turner 2015). A blend is formed when the brain identifies distinct entities in different neural regions as the same entity in another neural region. Together they constitute the blend. In the metaphor fighting a war on cancer, the two distinct entities, located in different regions, are “cancer” and “fighting.” The blending process is guided by the inference that disease is a war, constituting the final touch to the blend—a touch that keeps the two entities distinct in different neural regions, while identifying them simultaneously as a single entity in the third region. Getting the brain to activate blending in the FL is the ultimate objective of conceptual fluency theory.

Chapter 6

Blended Foreign Language Teaching Prologue In the previous chapter, a relevant article by Frances and Zimotti (2023) was cited in reference to a “translation test” aimed at assessing the level of conceptual fluency of ChatGPT in Spanish. In the same article, the two scholars ask ChatGPT a question, which zeroes in on the main question addressed throughout this book—namely, what is the role of chatbots in FL? ChatGPT’s answer is remarkably noteworthy, as can be seen below: Prompt:

ChatGPT:

Write an introduction to the debate surrounding the use of ChatGPT in the foreign language classroom, citing academic articles. The use of ChatGPT (Generative Pre-trained Transformer) in foreign language teaching has been a topic of debate in recent years. Some proponents argue that ChatGPT can provide language learners with personalized, conversational practice that is more engaging and immersive than traditional methods. They claim that ChatGPT can help learners improve their speaking and listening skills, as well as their grammar and vocabulary. However, critics argue that ChatGPT may not be able to fully replicate the complexity and nuance of human communication, and that it may not adequately prepare learners for real-world interactions in the target language. Additionally, some have raised concerns about the ethical implications of using ChatGPT in foreign language teaching, such as the potential for biased language output or the potential for ChatGPT to replace human language teachers. (Citations: “Chatbots in Language Learning: A Review,” “The Ethics of Chatbot Language Teaching,” “The Role of Chatbots in Language Learning: Opportunities and Challenges”).

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As the two researchers note: “The fact that ChatGPT could generate this text and cite three articles on this topic in less than forty seconds was nothing short of astounding.” What is similarly amazing is the fact the chatbot did not repeat robotically what it found on the Internet, as a search that I myself carried out indicated, but that it put together a technical answer as would a human, in academic style, as if it were a linguist or language teaching theorist. The two researchers then followed this up with a more specific question, that is also of obvious relevance to the present discussion: Prompt: ChatGPT:

Can AI trick a foreign language professor into thinking that a response was written by a student? It is possible that AI could produce responses in a foreign language that are convincing enough to trick a professor into thinking that they were written by a student. AI is a broad term that encompasses a wide range of technologies and techniques, including natural language processing (NLP), machine learning, and deep learning. These technologies can be used to generate human-like text based on a given prompt or input, and it is possible that a professor could be deceived by the responses if they are not paying close attention or if the responses are particularly sophisticated. However, it is worth noting that AI is not capable of fully replicating the complexity and nuance of human communication, and it is likely that a professor who is familiar with the language and the abilities of their students would be able to recognize the responses as being generated by a machine.

What is again remarkable is how the chatbot imitated the style of a professional researcher in language pedagogy, using academic explanations appropriately. Overall, ChatGPT’s own answers to the two prompts summarize, in a nutshell, the main points put forward and illustrated practically in this book—namely that chatbots have come forward to signal the third major technological revolution in FLT, after the language lab and CALL, emerging as virtual assistants to both learners and teachers, constituting a portable “intelligent language lab.” This chapter will tie together the themes discussed in previous chapters, integrating them into a blended model of FLT, implying a form of classroom pedagogy that involves humans in collaboration with an AI. It will specifically assess the role and effects of this partnership on learners, teachers, and courses.

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Needless to say, this revolution raises many serious questions, which will need to be addressed, especially since, as McLuhan often commented about technology, once a new technology comes into wide use, there is no turning back the clock. As Rose Luckin (2020) has aptly remarked, echoing McLuhan’s ideas indirectly, “The real power that AI brings to education is connecting our learning intelligently to make us smarter in the way we understand ourselves, the world and how we teach and learn.” As AI installs itself into education, including FLT, the result will not be easy or uncontroversial. But, to cite McLuhan (1962: 47) again, change is inevitable: “When technology extends one of our senses [capacities], a new translation of culture occurs as swiftly as the new technology is interiorized.”

The Learner One of the aspects of human learning that is prized above all others is its inbuilt creativity, a feature that is related to so-called autopoiesis (Maturana and Varela 1973). For the purposes of the present discussion, this implies that human thought organizes itself creatively on the basis of the input received through the body and the brain’s perceptual filters. The result is a mode of thought that is highly imaginative and referentially purposeful. As Michael Mumford (2003: 110) has pointed out insightfully, creativity in humans “involves the production of novel, useful products.” Every act of human learning is creative in some way, and to some degree, new products of the mind result from the act of learning. Is AI creative (autopoietic) in the human sense? As discussed throughout this book, the new AI technologies seem to have attained a high degree of creativity, as illustrated with the responses by ChatGPT throughout this book. Now, it is clear that the chatbot is not aware of what its thoughts are about, as are humans. This is beside the point in the context of the present book, since what is of relevance is the fact that the intellectual products of chatbots are useful in relation to what learners and teachers require. In a blended teachinglearning system, there will be phases during which the learner receives input and interacts with a teacher creatively, and others with a machine, just as creatively. The point is, as Marr (2023) aptly points out, “Tools like ChatGPT and Dall-E give the appearance of being able to carry out creative tasks—such as writing a poem or painting a picture—in a way that’s often indistinguishable from what we can do ourselves.” It does not matter that the AI is sentient or conscious of its creativity; just that it can produce creative products (as

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interpreted to be so by humans themselves). As Marr (2023) goes on to observe, “without humans to create the data that’s been used to train seemingly highly creative machine intelligence like Dall-E and GPT-4, they wouldn’t be capable of ‘creating’ anything more impressive than random word-soup or a toddler’s scribblings.” The point here is that, for the FL-learner, the blended system is a very effective one, provided that there is motivation to engage meaningfully with it. As psychologist-semiotician Yair Neuman (2014: 61) has argued, the use of Machine Translation, for instance, is powerful cognitively, because it allows us to truly understand our culture-based peculiarities of meaningmaking: The reason for using Machine Translation is twofold. First, there is no better way to understand the loss accompanying translation than by examining the most structured and formal attempt of translation known today. Second, instead of pointing at the problems and errors of Machine Translation, I suggest using it in order to better understand cultural peculiarities and discrepancies. The second suggestion is somewhat counterintuitive as we positively think of eliminating errors and solving problems. Sometimes, however, errors can be used for the better.

The Main Competencies As Alissa Ludanskaa (2023) has shown with regard to teaching Spanish in terms of the different competencies (linguistic, communicative, conceptual), ChatGPT can become a true virtual assistant, allowing individual learners to develop the competencies at their own pace and according to their specific learning styles, by providing theme-specific conversational practice, grammar assistance, vocabulary training, and cultural-conceptual insights. Below are relevant examples: Pronunciation Practice Prompt: ¿Puedes pronunciar la palabra ‘reloj'’en español? ChatGPT: [Audio Pronunciation] Conversational Practice (Saying hello) Prompt: ¡Hola!, ¿cómo estás hoy? ChatGPT: ¡Hola! Estoy bien, ¿y tú? ¿En qué puedo ayudarte hoy?

Blended Foreign Language Teaching Grammar Assistance Prompt: ¿Cuál es la diferencia entre “ser” y “estar”? ChatGPT: “Ser” y “estar” son dos verbos en español que se usan para expresar diferentes estados o características. “Ser” se utiliza para describir características permanentes o esenciales, mientras que “estar” se utiliza para estados o condiciones temporales. Por ejemplo, “soy alto” (I am tall) usa “ser” porque es una característica permanente, mientras que “estoy cansado” (I am tired) usa “estar” porque es una condición temporal. Prompt: ¿Cómo puedo formar una pregunta en pasado? ChatGPT: Para formar una pregunta en pasado, puedes invertir el orden del sujeto y el verbo auxiliar. Por ejemplo, “¿Hiciste la tarea?” o “¿Fuiste al cine ayer?” Vocabulary Help Prompt: ¿Cuál es la traducción de “amigo” en inglés? ChatGPT: La traducción de “amigo” en inglés es “friend.” Prompt: ¿Qué significa “echar una mano”? ChatGPT: Echar una mano es una expresión que significa “ayudar” o “colaborar.” Por ejemplo, “Voy a echarte una mano con la mudanza” significa “Voy a ayudarte con la mudanza.” Cultural Insights Prompt: Cuéntame sobre la celebración de “Día de los Muertos” en México. ChatGPT: El “Día de los Muertos” es una festividad mexicana que honra y recuerda a los seres queridos que han fallecido. Se celebra el 1 y 2 de noviembre, y las familias construyen altares en sus hogares con fotos, comida, flores y objetos que solían pertenecer a los difuntos. Es una ocasión para recordar y celebrar la vida de los seres queridos que ya no están con nosotros. Prompt: ¿Cuál es la forma apropiada de saludar en España? ChatGPT: En España, es común saludar con dos besos en las mejillas cuando te encuentras con amigos o conocidos. También puedes usar un apretón de manos en contextos más formales.

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These examples showcase how students can interact with ChatGPT in a concrete way, in order to seek grammar explanations, expand vocabulary, gain cultural insights, and get conversational practice on demand. Perhaps the key aspect of using chatbots in the FL classroom is, in fact, their ability to be tailored to meet individual learning needs and thus to enhance motivation to learn (chapter 2). However, it could be claimed that the opposite is true— namely, that students who rely on chatbots may be actually less motivated than others. Interestingly, this very issue was posed by Frances and Zimotti (2023) to ChatGPT itself, which gave the remarkable following assessment, going contrary to many of the positions taken in this book: ChatGPT’s responses 1. Decreased motivation: Students who rely on ChatGPT to complete writing assignments may be less motivated to learn the language and improve their writing skills. This could lead to a lack of progress and a decrease in overall language proficiency. 2. Inaccurate translations: ChatGPT may not always produce accurate translations, particularly when it comes to idiomatic expressions, slang, or more complex language structures. This could result in misunderstandings or incorrect language usage in writing assignments. 3. Reduced cultural competency: Students who rely on ChatGPT for writing assignments may have a reduced understanding of cultural context and may be more likely to make cultural mistakes in their writing. This could have negative consequences for their ability to communicate effectively with native speakers. 4. Decreased critical thinking skills: ChatGPT does not require students to engage in critical thinking or analysis, as it simply provides prewritten responses. This could result in a decrease in critical thinking skills and a lack of depth in written assignments. ChatGPT’s responses about being used by students constituted an unexpected caveat for using a chatbot. Now, as if it were a sentient creature, aware of the implications of its own criticism, a week after generating this negative response Frances and Zimotti (2023) prompted ChatGPT again. This time, the AI provided reasons for using it to the benefit of the learner, which it stipulated as follows:

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ChatGPT’s responses 1. Increased efficiency: Chatbots can provide immediate feedback to students on their writing, potentially allowing for a faster learning process. 2. Personalization: Chatbots can be customized to provide specific feedback to individual students based on their needs and learning goals. 3. Availability: Chatbots can be available to students at any time, potentially allowing for more flexibility in terms of when students can receive feedback on their writing. 4. Scaling: Chatbots can potentially be used to provide feedback to a large number of students at once, making it easier for teachers to manage a large class. 5. Reduced workload: Chatbots can help to reduce the workload of teachers by automating the process of providing feedback on writing assignments. 6. Mixed results: However, it is important to note that the effectiveness of chatbots in teaching second language writing may vary, as some students may prefer more traditional methods of instruction and feedback. The irony here is that the AI outlined both the advantages and potential disadvantages of using AI in the classroom. Albeit “artificial,” the responses by ChatGPT are truly remarkable. Without belaboring the point, the chatbot is truly a valuable assistant to the learner and the teacher, at the same time that it is apparently aware of the caveats of relying totally on itself. As such, the responses above constitute an overall argument in favor of blended FLlearning, in which humans and machines are interactive entities.

Learner-Chatbot Interactions As discussed in chapter 2, Krashen’s distinction between acquisition and learning implies essentially that one or the other process is activated by relevant input materials and activities (Krashen 1985). Acquisition is triggered by providing input that stimulates the unconscious aspects of the process; learning occurs when students work formally with the categories of the FL that they have acquired. Acquisition is clearly crucial in getting the whole process into motion. But it can be blocked by fear of making errors or by the inability

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to interact socially in a classroom environment, which Krashen called the affective filter (as discussed)—a mental block that prevents students from acquiring the input to which they are exposed because of anxiety of not performing adequately in the FL in front of the teacher or classmates. When the filter is “up,” students may understand what they hear and read, but they will not be able to acquire it. When the filter is “down,” however, they are not hampered emotionally by the possibility of failure. Krashen suggests that the filter will go down on its own when acquisition is set in motion, after which learning the rules of the language will either occur via what he calls the student’s inner “Monitor” or else, if necessary, by instruction. He put it as follows (1985: 1-2): Acquired competence comes from our subconscious knowledge. Learning, or conscious knowledge, serves only as an editor, or Monitor. We appeal to learning to make corrections, to change the output of the acquired system before we speak or write (or sometimes we speak or write, as in self-correction).

As discussed throughout this book, one of the main reasons for incorporating learner-chatbot interactions in the FL classroom is the fact that these tend to lower the affective filter, facilitating the absorption of new material much more readily and effectively, and thus enhancing the motivation to learn. This was corroborated by a key study conducted by Yin, Goh, Yang, and Xiaobin (2020) with regard to a course in computer science, whose findings can be seen to apply generally to any educational context. The study found that “students in the chatbot learning group attained significantly higher intrinsic motivation than the traditional learning group with perceived choice and perceived value as core predictors of intrinsic motivation.” In another relevant study, Holmes, Persson, Chounta, Wasson, and Dimitrova (2022: 6) summarize this whole situation aptly as follows: Exploratory learning environments [involve] AI-supported tools in which learners are encouraged to actively construct their own knowledge by exploring and manipulating elements of the learning environment. Typically, these systems use AI to provide feedback to support what otherwise can be a challenging approach to learning.

Overall, as the research has been showing, and as discussed throughout this book, in addition to making the acquisition of new knowledge

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personalized and non-threatening, for the FL-learner the main advantages resulting from interactions with chatbots include the following: •

•

•

•

Accelerated learning. Language learners need varying time periods to learn different aspects of the FL. Because chatbots can attend to the needs of a learner immediately, they can help accelerate learning. Tailor-made learning. Unlike traditional approaches using prepared lessons and print materials, chatbots can easily tailor the teaching material to the specific level of the learner. Users can select the topics that matter most to them and work at a comfortable pace. Time efficiency. Chatbots allow students to focus on areas in which they need special help, saving valuable time in the classroom for other activities. Blending. As emphasized here, when used in combination with human teachers and even with traditional pedagogical methods and materials, AI can greatly help language learners overall.

The Teacher In addition to allowing students to learn at their own pace and to provide teachers with ideas as to how to present new material, AI-aided FLT presents a number of other benefits to the teacher regarding the usual routines and preparatory aspects of pedagogy, including grading efficiency, a reduced workload, the generation of appropriate syllabi, creating lesson plans, and composing lesson units. Some overly-enthusiastic educators now believe that AI will not just save teacher time, but at some point make teachers de facto redundant, or at least reconfigure their role to be classroom technology facilitators. But this might be the same type of miscalculation that saw the language lab as not only an aid to learners but a substitute for basic teaching (chapter 2). While AI technology is certainly much more sophisticated than past technologies, the human teacher is still crucial to the learner. The reason is actually common-sensical, as Kavika Roy (2022) has insightfully noted: Teaching is a one-on-one and many-to-one relationship that works in many unique ways. Not only does a teacher impart worldly knowledge to the students, but they also learn from them. Teachers and students form a symbiotic and synergistic ecosystem that helps in the mutual enhancement of knowledge (various flavors). Further, what makes

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While AI tools can do many things automatically, quickly, and accurately, such as assess learner assignments, they are not capable of the depth of interpretation or accuracy of analysis that a human teacher can bring to the tasks at hand. Of course, those who are better at learning by themselves may not gain as much from the human teacher; but this type of learner is actually less common than might seem. Overall, in a blended teaching system, both the human and computer work in tandem to help learners gain proficiency according to their varying needs and learning styles.

Learner-Centric Pedagogy As mentioned throughout this book, chatbots diminish the risk of students developing anxieties; as such, they make learner-centric pedagogy more viable than at any other time in the history of FLT. However, learner-centric content still needs to be shaped according to course goals, and this is where the teacher comes into play—helping establish the learning goals to be pursued systematically by the students. Given its ability to help students with self-learning activities according to need, AI can remove teachers from having to carry out repetitive teaching, since the AI can take over all kinds of repetitive tasks, allowing more time for teachers and students to interact meaningfully in the classroom. Rote teaching can be thus taken away and delegated to technology, as was the case with some uses of the language lab. With AI, teachers are thus in the process of transitioning from being general pedagogical practitioners to facilitators. They do not have to continue to be the sole purveyors of new knowledge, but can form a partnership with AI and the students to ensure that FL-learning is maximized. In effect, learner-centric approaches are now greatly facilitated by AI, with the teacher overseeing the overall pedagogical process from the outset, as Holmes, Persson, Chounta, Wasson, and Dimitrova (2020: 36) aptly point out: “Since Dewey, a learner-centric approach to teaching and learning has been a recurring theme in education research and practice. This approach gives children significant control over the learning processes, thereby maximising learner agency.” However, as the researchers also go on to emphasize, this

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does not exclude the teacher from the scenario, since, not having the same capacity as the human teacher to understand issues of bias and fairness, a chatbot cannot give “genuinely informed consent, or to understand or contest the effects of AI-based recommendations and predictions on their lives.”

A Teaching Assistant As Nghi, Phuc, and Tat (2019) found in a significant study, chatbots are not only “effective and useful for enhancing student performance and engagement in learning a specific point of a foreign language,” but also have the ability to generate “excitement and fun.” This combination makes AI an ideal teaching assistant, which can take over many of the routine and laborious aspects of pedagogy and tailor them according to learner needs and desires, and making even those “exciting and fun.” To reiterate, ChatGPT can serve as a companion to the student and valuable assistant (grader, supplementary exercise maker, and so on) to the teacher. It can provide learners with tailored opportunities for practicing conversations, receiving feedback on grammar and vocabulary usage, and exploring different semantic nuances involved in conceptually fluent discourse. In combination with other language learning resources, such as traditional textbooks and live classes, ChatGPT is the “assistant par excellence,” who will never complain or become tired, but always trudge on as requested to do. One of ChatGPT’s most valuable skills, as also discussed throughout this book, is giving immediate feedback to students on their speaking or writing, identifying and correcting grammatical, lexical, and conceptual errors, as well as suggesting alternative phrasing. As Alejandro del Carpio (2023) has aptly pointed out, there are several main ways in which ChatGPT can be used as the maximal assistant, which are worth paraphrasing here for the sake of illustration: •

•

Language practice. ChatGPT can help students practice grammar and vocabulary in context. It can provide examples of how to use words and grammatical structures in real-life situations, updating these situations constantly because of Internet updates. Conversation practice. ChatGPT can produce thematic conversations based on real-life situations (shopping, eating at a restaurant, etc.),

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•

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tailored to meet student needs and in response to teacher prompts for relevant conversational material at a specific point in the curricular sequence. Proofreading and editing. ChatGPT can proofread and edit FL writing expertly. It can catch common grammar mistakes and, if asked, break the writing down into its stylistic and conceptual components. Textual materials. ChatGPT can search out textual realia on the Internet according to teacher specifications, locating and extracting FL texts that are appropriate at different levels of competency, from elementary to advanced. Pedagogical materials. ChatGPT can help teachers create all kinds of teaching materials, from drills to exams, as well as grading them and providing feedback to students. Emotional intelligence. While ChatGPT can simulate real-life conversations, it lacks the emotional intelligence of a human conversation partner. This means that a human tutor or language exchange partner is always required to complete any pedagogical process.

The Course As mentioned, a chatbot can be prompted to generate an entire coursebook, based on specific curricular requirements and according to a given syllabus. Chatbots can also create an informal, collegial environment for sharing concerns and successes in a course, which can be rather helpful when asking for feedback about more delicate topics, such as the biases that a FL culture might present. In other words, chatbots are useful not only to learners and teachers, but to course layout and design. As Sabzalieva and Valentini, (2023: 14) observe, this entails assessing chatbot use according to specific pedagogical questions or requirements, such as the following: • •

Which aspects of course structure would benefit from using AI (student learning, assessment, planning, etc.)? Which AI technology should be chosen? As discussed throughout this book ChatGPT is a very useful system, but there are others that have different devices and plugins that may be required for a specific

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course. Some of these are described in the Appendix at the end of this book. What would be the criteria for choosing a specific chatbot? What value does the technology add? How effective is the AI technology in meeting the needs that an instructor or course designer has identified?

The traditional course, consisting of a main textbook (print or electronic), a set of practice materials (print or audio-oral as in a language lab), and a teacher at the front of the class, directing and instructing the entire FL-learning process at the same time may no longer be a viable model. The purpose of this book has not been to present an exhaustive manual of AI-based techniques, nor to be innovative in showing how chatbots and digital technologies can be incorporated into FLT. Its goal has been simply to highlight the adaptability and versatility of integrating chatbots into FLT and how they fit in with FLlearning models, in a world where technology has become a key component in people’s lives. Blended pedagogy in FLT can be used to kill the proverbial two birds with one stone—enhancing learning in the FL and motivating students to envision the FL classroom as a truly contemporary one and useful to them beyond just language learning. As Henry Widdowson (1978: 75) put it decades ago “effective teaching materials and classroom procedures depend on principles deriving from an understanding of what language is and how it is used.” These principles are found in a small-scale version in the case of an integrated interaction between learner, teacher, and computer. Nonetheless, no matter how scientific or theoretically sound a particular teaching proposal might appear to be, it is always susceptible to the vagaries of its human congener. Proposals such as the blended teaching one here is no different. It is not a solution to the dilemma of how languages are best learned in formal environments; it constitutes a suggestion for enhancing the learning process in the modern world today, where technology plays such a crucial role in everything we do.

Blended Pedagogy As Suchi Rudra (2021) has aptly noted, chatbots should be enlisted to provide tutoring on demand, not to replace human teachers. This is a key function, since it is becoming less and less possible to support every student having

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some learning need with a human tutor. As a result, AI-driven tutor chatbots are becoming an important addition to the classroom. One of the most fascinating ideas to come out of cognitive science is, as mentioned throughout this and the previous chapter, that of blending. The main manifestation of this neural process is figurative language, as discussed in chapter 5, and the claim can be made that blending is constantly at work in FL-learning. The claim here is that blending is not only applies to learning itself (Fauconnier and Turner 2002), but is also a broader one that involves the formation of social structures (including education) through the amalgamation of humans and their technologies, as McLuhan often pointed out, into unitary social systems. Specifically, the claim here is that the pedagogical model that best mirrors neural blending it itself a blended one. However, as Raymond Gibbs (2000) has argued, one cannot consider blending theory as a single theory, but rather as a framework. And that is how it is intended in this book. Unlike traditional views of FL-learning (chapter 2), the blending framework does not envision the process in a linear way, orchestrated through a single teacher and a textbook, but concentrically through activities and processes that blend student needs and teacher requisites as a course unfolds. As a psychological notion, blending refers to how language concepts are formed in the brain from linkages among various inputs (chapter 5). As a pedagogical notion, it refers to how different intelligences (human and artificial) along with human needs are linked together into a framework that goes back and forth between humans and machines. This entails a kind of continual navigation among the different contents, contexts, media, and technologies to carry out the same or different tasks the individual teacher and group of students in a classroom once did in a prescribed sequence. This means that no one individual can control the learning process entirely; the process organizes itself from the interactions between its human and artificial components.

A Paradigm Shift Technologies invariably bring about paradigm shifts in how people think, behave, and learn. The advent of AI as a learning tool is a major part in the paradigm shift in education today. It has changed how the roles the roles and expectations of learners and teachers are perceived. It has also made possible the type of “wall-less” classroom that McLuhan had predicted in the 1960s (chapter 1). The blended model of FLT described here is a product of the

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paradigm shift, exemplifying the following three main features: (1) it provides conditions for FL-learning through an apprenticeship format, given that students can see themselves as apprentices through chatbot interactions; (2) it promotes virtual connectivity to the ever-changing world of the FL culture, made possible by the chatbot’s ability to navigate the Internet instantly and in a thematically-focused way; and (3) it creates conditions whereby learning is felt to be concentric (learner-centric and interactive) rather than as linear. So, what do we language teachers do practically in this new age of Artificial Intelligence? What kinds of curricula or methodologies are suitable? The previous chapters have attempted to discuss the bits and pieces of a potential set of answers to these questions. The main advantages of AI-based pedagogy can now be summarized in point form here: •

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•

•

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The new technologies have not eliminated the need to use traditional materials and the need for students to gain print literacy in the FL. But the exclusive or primary use of a traditional textbook and written activities is no longer the hub of pedagogy because students (and many teachers) have not been reared in the previous age of printbased pedagogy, but rather in an electronic-digital age. AI technologies make a new language interesting and connected to the social-technological milieu in which we all live. They extend the classroom considerably, connecting the FL to the real world in which we live. Through the ability of ChatGPT to navigate large masses of information on the Internet, the contents of a course can be extended to the outside world. This does not obliterate the traditional materials; it connects them to the real world and its ongoing changes. AI extends the classroom considerably beyond its temporal and spatial limitations. It allows students and teachers to form a blended learning community with the technology itself. Chatbots have become a kind of “teacher-by-proxy” system, able to reinforce classroom teaching considerably. They are also teaching assistants to the human instructor. In the past, the classroom isolated itself from the outside world—a situation that McLuhan called the world of the “walled-in” classroom, as mentioned in chapter 1. Above all else, chatbots can bring this world of FL culture continually into the classroom.

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In 1958, McLuhan was invited to be the keynote speaker at the annual convention of the National Association of Educational Broadcasters in Omaha, Nebraska. It is reported that he gave a dazzling speech, and afterwards the Association invited him to prepare a syllabus for a year-long grade eleven course devoted entirely to the study of new media, which at the time meant basically television. They wanted American students to be conscious of the contradictory effects of media, as expressed by his own laws of media, believing fully that McLuhan was the appropriate person to explain them. McLuhan gladly accepted the task and set out immediately to work on it. But the syllabus and accompanying textbook that he devised shortly thereafter baffled the executive of the Association. They did see his materials as constituting an ambitious and intellectually challenging project, but they had no idea how they could be used in the classroom of that era. One exercise that McLuhan included was reflective of how he envisioned the whole project: “Speech as organized stutter is based on time. What does speech do to space?” It is now part of McLuhanian lore that McLuhan saw the rejection as inevitable because the times were not right. So, he went on to expand and modify his report into his classic 1964 book, Understanding Media: The Extensions of Man. But the public was also not ready for his ideas. Reviews of the book were generally negative. But the reviewers did notice the potential importance of the work and some were even enthusiastic. McLuhan had understood the power of new technologies perfectly, as bringers of paradigm shifts. The Reform Movement envisioned its pedagogical models as based on standardized curricula and materials that purportedly reflected a common denominator in learning, regardless of learner background. Education for “one-and-all” was an Industrial Age concept and the classroom was turned into a kind of factory where students “grew up along with the production line” (McLuhan and Leonard 1967: 23). Mass education came about at the historical moment when western society had mastered mass production technology, and education adapted this model. The first FLT methods were designed with a one-size-fits-all philosophy. Teaching called for very little student involvement. The teacher was the center of attraction. But the teacher was also controlled from above through imposed standardized models of teaching and testing. Evaluation through uniform testing was the main tool for determining learning outcomes. This model has broken down with the advent of AI. The blended model proposed here is meant to correspond to how the world has evolved since the Reform Movement and how much technology shapes everyday life today.

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Epilogue In their report to the Council of Europe regarding the impact of AI on education, Holmes, Persson, Chounta, Wasson, and Dimitrova (2022: 11) make the following relevant statement, paraphrasing the concerns of the Council: AI, like any other tool, offers many opportunities but also carries with it many threats, which make it necessary to take human rights principles into account in the early design of its application. Educators must be aware of the strengths and weaknesses of AI in learning, so as to be empowered—not overpowered—by technology in their digital citizenship education practices. AI, via machine learning and deep learning, can enrich education. By the same token, developments in the AI field can deeply impact interactions between educators and learners and among citizens at large, which may undermine the very core of education, that is, the fostering of free will and independent and critical thinking via learning opportunities.

The core argument that the report goes on to make is that an AI system cannot replace the human teacher entirely. Built upon a neural network architecture and trained with massive amounts of data, it operates through complex algorithms that guide its responses. Its focus lies more on performance and accuracy than on explaining the rationale behind its decisions, which is always needed in classrooms. There is little doubt that a paradigm shift has occurred in FLT and education generally with AI technologies, and fundamental questions regarding it are crucial, perhaps reflecting the same kind of questions that emerged during the Reform Movement in the late nineteenth century. As teachers, we need to truly recalibrate our understanding about who we are and rethink the role of our ever-broadening partnership with technology in the classroom. Where does the human teacher end and the artificial teacher begin? What is the role of the physical classroom today? Does it need to exist as it has for centuries and even millennia? Is McLuhan’s wall-less classroom already here? If a student spends time asking ChatGPT questions, and engaging with it on all matters involved in learning the FL, can we say that ChatGPT is fulfilling the role of a teacher? There really is no way to turn the back the clock, nor is it even useful to do so, to paraphrase McLuhan one more time. A tool such as ChatGPT in FLT

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is not unproblematic. It challenges us to consider what we do as teachers and what FL learning is fundamentally all about—psychologically, socially, and emotionally. Like the faith put into the language lab in the past, however, AI is not the magic pill that teachers have been looking for, nor is it the dangerous thing that will eliminate them from the classroom. The work required to adapt is never easy, but the potential is enormous if we choose to embrace the change, as discussed throughout this book. As writer J. G. Ballard so aptly put it decades ago (1984: vii): “Science and technology multiply around us. To an increasing extent they dictate the languages in which we speak and think. Either we use those languages, or we remain mute.”

Appendix Language Learning Apps For the purposes of this book, I have used or referred to uses of ChatGPT, which is, in my view, a truly powerful tool in the blended model of FLT proposed in this book. It enables users to refine and steer a conversation or a response according to specific needs and requirements. Successive prompts and replies form part of a conversational flow that imitates almost to perfection how a human verbal interaction might unfold. OpenAI operates on a freemium model, allowing users to access the GPT-3.5-based version without fees. In contrast, its more advanced GPT-4 based version involves payment. At the time of writing this book, an array of various useful language learning apps were available for use in the FL classroom, some of which can be accessed for a fee, and most of which have a chatbot option. Of these, the following seem to me to be useful either as part of blended teaching or as ancillary outside-the-course devices. Brief descriptions of unique features follow below. Note that these are selections; there are others that teachers may already be using.

Babbel Babbel is a language-learning platform that uses AI-powered chatbots to provide personalized language-learning recommendations to students. Lessons include polysemic variants of words or phrases, illustrated with pictures and video, as well as information on social register. The new material is used in common conversations, to which users can listen and then repeat, as well as interact with.

DALL-E DALL-E is developed by OpenAI, constituting a version of GPT-3 modified to generate realistic images and with the ability to combine concepts, formal structures, and styles into learning units. Its distinguishing feature is its ability to combine verbal and visual material. OpenAI makes a combination of

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ChatGPT and DALL-E available as a unitary system at the time of writing this book.

Duolingo This app has a so-called “streak” feature, intended to motivate users to keep going by tracking the number of days used to reach a learning goal. It also makes available short audio stories that allow users to check their comprehension skills. It is meant for beginners and thus its range of grammar, vocabulary, and conversation is constrained to this level. Duolingo also creates characters such as a chef, a police agent, or a taxi driver, which will interact with users, responding to their prompts in a contextually-appropriate way. When a user gives a wrong answer, the characters will correct the prompter so as to make the learning process more personalized.

Rosetta Stone This app uses primarily auditory material with images, which can be customized according to learning preferences. It also incorporates augmented (virtual) reality which enables users to point a phone camera at an object and get a word for it in the FL.

Memrise This is an app-chatbot that uses short videos to show how different phrases are used in real-life conversation in the FL. It allows for abundant pronunciation practice as well as conversational know-how. Above all else, it makes learners feel that they are interacting with a real teacher.

Busuu This app helps users determine how advanced they are, allowing them to set a daily study goal, according to a study plan it generates that presumably allows them to reach their learning goal by a set date. The app also provides

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reminders, such as the vocabulary or grammar that a user needs to review so as to improve in these areas. Each new word or phrase is paired with an image of its meaning.

Drops With this app, users can check out their personal learning statistics after completing a task (percentage of correct versus wrong answers). As such, Drops offers specialized training in FL words used in isolation as well as a system of review exercises to reinforce vocabulary.

Mondly The app uses images, translations, and auditory aids to help with specific learning requests. It also speaks the FL words and phrases in a melodic way, perhaps based on the view that musical style enhances memory. The app also has augmented and virtual reality features, allowing users to participate by simulation in informal conversations. It also has chatbots with whom a user can interact by either typing or speaking the prompts.

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Index

A acquisition, 10, 26, 29, 31, 32, 33, 48, 63, 86, 99, 117, 118, 136, 141 adjacency pairs, 76 affective filter, 32, 48, 62, 118 algorithm, 26, 30, 34, 39, 40, 70, 82, 108, 139 AlphaGo, vii, viii analogy, 28, 29, 33 anaphoric, 54, 78 Arabic, 51, 107 Aristotle, 53 Army Specialized Training Program, 47 artificial intelligence (AI), vii, viii, ix, 1, 2, 12, 15, 16, 17, 18, 19, 20, 23, 25, 26, 27, 30, 32, 36, 37, 38, 40, 41, 42, 43, 47, 48, 51, 57, 58, 61, 62, 63, 66, 69, 70, 75, 80, 82, 85, 87, 90, 96, 99, 102, 112, 113, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,127, 128, 129, 133, 134, 135, 137, 138, 139 audio-lingual method (ALM), 1, 4, 5, 6, 7, 8, 10, 11, 12, 20, 30, 37, 66 audio-visual method, 5 Austin, John, 68, 94, 133 autopoiesis, 113, 137

B Babbel, 129 Bantu, 93 behaviorism, 134 Black, Max, 43, 133 blended pedagogy, 15, 123

blends, 14 Busuu, 130

C calquing, 89, 96, 97 cataphoric, 54, 78 categorization, 54 Chagga, 93 chatbot, vii, viii, ix, 16, 19, 25, 26, 34, 36, 38, 41, 43, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 63, 65, 68, 69, 71, 77, 78, 80, 82, 83, 84, 85, 86, 87, 90, 91, 95, 101, 103, 104, 105, 106, 107, 108, 111, 112, 113, 116, 117, 118, 121, 122, 123, 125, 129, 130, 138, 140, 141 ChatGPT, viii, ix, 16, 17, 18, 19, 25, 30, 31, 32, 36, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 69, 71, 72, 73, 77, 78, 80, 81, 82, 84, 85, 86, 90, 91, 92, 95, 97, 99, 101, 102, 104, 105, 106, 107, 111, 112, 113, 114, 115, 116, 117, 121, 122, 125, 127, 129, 130, 133, 134, 135, 137, 139, 140 Chinese, 17, 19, 25, 50, 51, 53, 83, 136 Chomsky, Noam, 22, 33, 134 classroom without walls, 21, 22, 83 cognitive linguistics, 89, 91 collocation, 34 Comenius, 10, 67 communicative competence, 7, 35, 45, 66, 67, 68, 72, 73, 74, 78, 82, 85, 86, 89, 92, 94

144 computer-assisted-language-learning (CALL), vii, 1, 2, 12, 13, 14, 15, 23, 42, 78, 93, 95, 112, 135, 139 conceptual blending, 109 conceptual competence, 45, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 101, 103, 107 conceptual fluency, 89, 91, 98, 99, 107, 109, 111 conceptual interference, 91 conceptual metaphor, 92, 93, 95, 98, 99, 100, 101, 106 conceptual metaphor theory, 89, 90, 92, 98, 100, 102 conceptual transfer, 91, 104 conceptualization, 42, 91, 92, 103, 104, 108, 136 connotative, 59, 99 context, 8, 17, 25, 29, 32, 34, 40, 41, 42, 43, 54, 56, 61, 62, 69, 70, 71, 72, 82, 85, 90, 94, 95, 97, 113, 116, 118, 121, 133, 138 contrastive analysis (CA), 8, 10, 46, 47, 48, 72, 78, 79, 104, 106 contrastive conceptual analysis, 104 conversation analysis, 70 conversations, vii, 17, 35, 36, 41, 42, 47, 54, 57, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 86, 104, 107, 121, 122, 129, 131 coreference, 54, 57 Council of Europe, 68, 127, 135, 140 culture, ix, 5, 12, 13, 19, 21, 37, 42, 55, 63, 65, 66, 67, 72, 74, 84, 85, 86, 93, 95, 98, 99, 101, 113, 114, 122, 125, 137, 141

D DALL-E, 129 deep learning, ix, x, 40, 41, 48, 59, 70, 92, 112, 127 denotative, 59, 98 dialect, 28 dialogue, 2, 7, 9, 18, 21, 22, 29, 65, 66, 71, 75, 76, 78, 85, 86, 97, 99, 133 direct method (DM), 8, 9, 10, 26, 29, 30, 31, 37, 62, 66, 67, 91

Index drills, 4, 6, 9, 26, 29, 46, 47, 48, 55, 58, 122 drops, 131 Duolingo, 2, 130

E ELSA, 2 emotional needs, 26 English, 2, 6, 25, 27, 28, 42, 45, 46, 49, 50, 54, 57, 59, 60, 72, 80, 81, 83, 85, 89, 91, 93, 94, 95, 96, 99, 100, 101, 103, 104, 105, 133, 135, 136, 137, 138, 140

F Farsi, 107 feedback, 12, 17, 18, 22, 39, 41, 47, 50, 54, 57, 61, 62, 84, 106, 117, 118, 121, 122 figurative, 59, 92, 97, 107, 108, 124 folk wisdom, 93 foreign language learning (FL-learning), viii, ix, 4, 7, 8, 10, 11, 12, 13, 23, 25, 26, 27, 29, 30, 31, 34, 39, 43, 52, 83, 87, 97, 101, 103, 117, 120, 123, 124, 125, 134, 135, 136 foreign language teaching (FLT), viii, ix, 1, 2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 19, 22, 23, 26, 33, 35, 37, 38, 41, 47, 50, 53, 57, 62, 63, 65, 66, 67, 68, 82, 83, 84, 85, 87, 90, 91, 92, 99, 111, 112, 113, 119, 120, 123, 124, 126, 127, 129 French, 36, 51, 52, 58, 69, 71, 107 Fries, Charles, 6, 30, 135 Frye, Northrop, 93, 135

G games, vii, 11, 13, 41, 61, 62, 63, 134, 138 gamification, 62 Generative AI, 1, 2, 16, 32, 45, 139 German, 49 Gouin, François, 8, 135 grammar, 5, 8, 9, 16, 17, 21, 27, 29, 30, 32, 33, 34, 35, 36, 44, 45, 51, 52, 53, 54, 55,

Index

145

56, 57, 58, 59, 61, 62, 63, 65, 79, 81, 83, 92, 93, 108, 111, 114, 115, 116, 121, 122, 130, 131, 134, 136, 140 grammar-translation, 27, 29, 44 graphemics, 49 Greek, 53, 107

irony, 2, 74, 98, 100, 102, 117 Italian, 5, 18, 25, 27, 42, 46, 59, 68, 80, 84, 85, 89, 91, 96, 100, 101, 102, 103, 104, 106, 134

H

Japanese, 53 Johnson, Mark, 89, 98, 100, 136

habit-formation, 4, 5 hallucination, 30 Hungarian, 107 Hymes, Dell, 35, 66, 68, 136

K

I idiom, 25 image schema, 91, 96, 99, 100, 101, 103, 104, 106, 108 imitation, 5, 6, 7, 9, 10, 29, 33 individualized learning, 12 induction, 6, 30 inductive process, 30 industrial revolution, 9 information theory, 39 input, 7, 9, 11, 16, 27, 30, 31, 32, 33, 39, 40, 48, 49, 51, 62, 63, 81, 92, 99, 101, 107, 112, 113, 117, 136 input hypothesis, 31, 32, 63, 136 instruction, ix, 5, 6, 9, 12, 13, 18, 27, 29, 31, 33, 37, 51, 59, 68, 82, 100, 101, 117, 118, 139 interactional pedagogy, 85 interactivity, 11, 62 interference, 27, 28, 89 interlanguage, 27, 28, 29, 30, 31, 39, 51, 139 interlanguage theory, 27, 28, 29, 30 interlinguistic error, 27 International Phonetic Association, 9 internet, 11, 12, 15, 18, 20, 21, 22, 25, 33, 39, 63, 71, 101, 106, 107, 108, 112, 121, 122, 125, 139 intralinguistic error, 29 inuit, 42

J

Korean, 51, 53, 81 Krashen, Stephen, 29, 31, 32, 62, 99, 117, 134, 136

L Lado, Robert, 42, 136 Lakoff, George, 79, 89, 98, 100, 136 Langacker, Ronald, 108, 136 language acquisition device, 33 language laboratory, 1, 2, 4, 10, 12, 137 language learning apps, 129 learner-centric pedagogy, 120 learning style, 16, 18, 26, 37, 38, 39, 49, 61, 84, 114, 120 lesson plan, 16, 18, 119 lexeme, 52 linguistic competence, 32, 35, 45, 61, 66, 68 linguistic metaphor, 92, 98 linguistics, 4, 8, 27, 33, 48, 70, 78, 134, 135, 136, 139, 140 listening, 4, 9, 17, 20, 29, 41, 48, 49, 67, 111, 133, 137 literary translation, 90, 92

M machine learning, 16, 39, 40, 41, 43, 62, 69, 70, 71, 112, 127 Malinowski, Bronislaw, 35, 137 Mandarin, 17, 107 mapping, 98, 99, 100, 102

146 McLuhan, Marshall, 15, 20, 21, 22, 23, 83, 113, 124, 125, 126, 127, 137 Memrise, 130 metaphor, 33, 59, 60, 90, 92, 93, 95, 98, 99, 100, 102, 106, 107, 109, 134, 135, 140, 141 metaphorical competence, 89, 98, 99, 101 metonymy, 98, 100, 102 Modern Language Association of America, 8 Modern Language Association of Great Britain, 8 Mondly, 131 monitor, 4, 19, 118 monitoring, 12, 31, 32 morpheme, 52 morphology, 53 Morris, Charles, 34, 137 motivation, 16, 19, 37, 38, 39, 114, 116, 118, 133, 135, 136, 140 multimedia, 11, 135

N native language (NL), 2, 7, 8, 9, 10, 18, 21, 25, 27, 28, 29, 30, 31, 49, 51, 52, 57, 72, 73, 86, 89, 90, 91, 96, 99, 103 negative transfer, 8, 27, 30, 72, 97, 103, 104 neural network, x, 16, 59, 71, 96, 127 N-gram, 39

Index pedagogy, ix, 8, 9, 13, 14, 15, 23, 27, 37, 43, 62, 84, 85, 86, 91, 102, 106, 112, 119, 120, 121, 123, 125, 134, 139 personalization, 38, 41, 62, 117 phatic communion, 35 phonetic, 17, 49, 137 phonics, 50 phonology, 45, 46, 48, 49 pinyin, 17 Plato, 53, 138 plugin, 47, 60, 106, 135 Polish, 107 polysemy, 39, 58, 59, 70 positive transfer, 8, 30, 72, 96, 103 pragmatic model, 35, 36 pragmatics, 34, 36, 70, 78, 140 proficiency movement, 38, 68 pronunciation, 7, 8, 9, 30, 41, 45, 46, 47, 48, 49, 57, 58, 59, 61, 62, 83, 114, 130, 138 proverb, 93, 123 psychology, 4, 8, 10, 27, 78, 135, 136, 138 puzzles, 63, 134

Q quizzes, 18, 19, 20

R

OpenAI, viii, ix, 129 oral method, 35 oral speech, 17, 41 orthography, 49

reading, 5, 9, 17, 20, 29, 37, 41, 50, 51, 58, 67, 139 reform movement, 27, 43, 126, 127 register, 16, 69, 71, 81, 86, 108, 129 repetition, 5, 6, 10, 78, 82 role of the teacher, 14, 15 role-playing, 7, 18 Rosetta Stone, 130

P

S

Palmer, Harold, 10, 35, 138 pattern practice, 5, 6, 7, 9, 29, 47

scripts, 51, 72, 73, 80, 92, 94 second language teaching, 2 Selinker, Larry, 27, 139 semantics, 34, 54, 140

O

Index sequence organization, 76, 77 Shannon, Claude, 39, 139 SHRDLU, 75, 76 singularity, viii, 136 small talk, 72, 73, 75 social framing, 79 source domain, 93, 95, 97, 98, 99, 100, 101, 102, 103 Spanish, 25, 45, 46, 56, 60, 61, 65, 66, 67, 68, 72, 77, 96, 97, 99, 104, 105, 106, 111, 114, 133, 137 speaking, 3, 4, 9, 17, 27, 29, 41, 45, 46, 47, 48, 64, 67, 73, 80, 83, 91, 95, 111, 121, 131 speech act, 68, 72, 73, 74, 79, 94, 101 speech recognition, 48, 61, 62, 82 Sweet, Henry, 10, 93, 103, 139 syntax, 34, 53, 71, 134

T target domain, 98, 100, 101, 102 technology, viii, 1, 2, 5, 6, 11, 13, 14, 15, 20, 22, 23, 41, 42, 48, 83, 85, 113, 119, 120, 122, 123, 125, 126, 127, 128, 133, 134, 135, 138, 139, 140 tests, 18, 19, 20, 38, 41, 45, 86 textbook, 37, 45, 55, 65, 69, 78, 82, 84, 101, 123, 124, 125, 126, 139 theory of mind, 95, 135, 140 tracking, 71, 130 transfer theory, 7, 10, 27, 29, 30 transformer, ix, 16, 27, 111

147 translation, 18, 25, 29, 61, 84, 89, 90, 104, 105, 106, 111, 113, 114, 133, 134, 139 turn-taking, 76, 77

U Universal Grammar (UG), 30, 33 Universal Grammar Theory, 33

V Vietnamese, 53 Viëtor, Wilhelm, 8, 140 virtual assistant, 38, 57, 67, 112, 114 virtual reality (VR), 12, 41, 83, 131 vocabulary, 7, 9, 17, 29, 30, 36, 45, 48, 51, 58, 59, 60, 61, 62, 63, 65, 83, 92, 111, 114, 115, 116, 121, 130, 131 voice recognition, 41 Vygotsky, Lev, 32, 140

W Weizenbaum, Joseph, viii, 140 Winograd, Terry, 75, 140 word class, 53 writing, ix, 5, 9, 16, 17, 19, 23, 29, 41, 42, 49, 51, 55, 67, 70, 80, 106, 113, 116, 117, 121, 122, 129, 130, 133, 134, 137

Z zones of proximal development, 32