Special Contribution

No Longer Only Human:
Language in the Age of AI
Tom GALLY 1

Language educators teach based on theories of language.
Whether choosing materials, preparing lesson plans, conductingclasses, or planning curriculums, each teacher decides what to
do and how to teach based in part on an abstract framework ofconcepts, relationships, hypotheses, and claims about the subject
they teach—in other words, a theory of language. That theory oflanguage might be formal—explicated in scholarly books andresearch papers, taught to novice teachers—or it might be
informal, a collection of ideas and beliefs that the teacher has
acquired through experience.
Different theories, whether formal or informal, approach
language in different ways. Some theories, and consequently
some teachers, regard language as primarily a cognitive phenomenon, as a system of sounds, words, grammar, and meaning
processed in the language user’s brain and mind. Others focus
more on language as a medium of one-to-one communication, of
conversation and dialog. And still others are concerned mostwith the role of language in society: its dialects and variations;its associations with identity, class, gender, ideology, and power;
its role as a transmitter and shaper of culture. But regardless of
their formality and focus, all theories of natural language until
now have, explicitly or implicitly, been grounded on one
assumption: that language is something only human beings possess and that we use language only among ourselves.

1

Emeritus Professor, The University of Tokyo. Formerly affiliated with the Department of English Language and Department of Language and Information Sciences.
Currently Project Professor, Center for Global Education.
KJEE vol 15 (2024)

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That assumption is challenged by recent advances in artificial intelligence. Now we can use natural language to interact
not only with each other but with computers as well. Using thesame words and sentences we would use with another person,
we can ask computers questions, engage them in conversation,tell them to draw pictures and write computer programs—even
have them teach us other languages. These advances in AI compel
us to reconsider our traditional views about human-only conceptions of language and about how natural languages can betaught and learned. In this new age of AI, language educatorsneed new theories of language.

A Turning Point
On November 30, 2022, the American company OpenAI released
to the public an interactive chatbot called ChatGPT. As I wrote a
few weeks later, in an essay published in this journal in March
2023, ChatGPT amazed me with its language ability. It could
respond appropriately to written prompts, correctly identify themeanings of words in context, compose well-organized essays,
and, especially, converse with me in a back-and-forth dialogue
almost as if it were human. Unlike programming languages, it
was forgiving of sloppiness: it responded appropriately toprompts that contained typos, grammar mistakes, and nonstandard expressions, and it could guess correctly the intended
meaning of ambiguous statements. Asking it questions, telling it
things, and reading its responses felt like interacting withanother person.
Interest in ChatGPT and other forms of generative AI spreadquickly beyond early adopters like me. Within a few months of
its release, schools and universities became aware that their students could use it to do homework, to write papers, and to cheat,and they began issuing guidelines for students’ use of AI. Mainstream media ran many reports and commentary on this new
development, and social media filled with examples of ChatGPT’s
output and discussions of whether it was really intelligent or not.
Warned of looming dangers from superhuman AI, governments
began to discuss the regulation of AI technology.
Technological progress also sped up. In March 2023, OpenAI
released a more powerful and reliable model called GPT-4. Later
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in the year, that model would be upgraded again, acquiring
multimodal capabilities, including a spoken interface and image
recognition and creation functions. Competing models from
Google, Anthropic, Meta, Amazon, Baidu, and other commercial
and open-source developers were also announced. Auxiliary
technology to support language use with computers improved
as well: speech recognition became more accurate, and synthetic
voices went from sounding robotic to being indistinguishable
from humans’. While some people predicted that AI progress
was about to plateau, many observers—including me—felt that aturning point had been reached, and that the coming months
and years would see even faster improvements in artificial intelligence of all types.
In my previous essay, I predicted that a new era was arriving for education, and my prediction seems to have been largely
correct. Hundreds of books, videos, and online guides about
how to use ChatGPT for learning languages and other subjects
have appeared, and smartphone app stores have filled up with
AI-powered conversation tools. School and university educators
have been more cautious about the technology, but by late 2023
reports of teachers incorporating generative AI in their lessons
were becoming common. Even educational administrators andgovernment policymakers—whom I had predicted would beslow to grasp and respond to this challenge—recognized thepotential and dangers of AI, and initiatives to integrate AI withschool and university education were under way in many countries.

Challenges
While AI’s impact began to be felt in all areas of teaching andlearning, it was perhaps in language education where the possibilities and threats were strongest. After all, the breakthroughsystems were large language models, and their greatest strength,
especially at first, was interacting with people using natural language. And the biggest weakness of the first LLMs—their tendency toward hallucination or confabulation, that is, the blithe
production of falsehoods—was not so serious for language education. While people trying to acquire explicit knowledge abouta subject need to be cautious about what LLMs tell them, the29KOMABA JOURNAL OF ENGLISH EDUCATION

goal of language learning is often more to acquire familiarityand fluency with linguistic forms. In such cases, occasional falsehoods may not be a serious problem. After all, when practicing
using a second language with a human partner, one might beannoyed if the other person makes up facts occasionally, but
those confabulations may not interfere much with the language
practice itself.
I should note that the first publicly available LLMs did
exhibit a quirk that was worrisome for language learning: aweakness at describing language itself. The models were trainedon texts that had been converted into units called tokens, which
did not always correspond one-on-one to letters, characters, morphemes, words, or other units that humans normally use. While thefirst release of ChatGPT was very good at explaining the meaningsof texts, because it couldn’t “see” the individual components ofsentences it sometimes gave false explanations of grammar andother linguistic structures. GPT-4 was more accurate though still
not perfect, while other LLMs could not even count reliably the
number of words in a sentence. LLMs exhibited similar weaknesses with logic, mathematics, and everyday common sense.
In that first year, there were other barriers to the immediate
adoption of generative AI for language learning. One was thecost. While a free version of ChatGPT was available, access to
OpenAI’s best model required a subscription of 20 U.S. dollars amonth—more than many schools and universities could ask
their students to pay. Another was that the techniques for using
the models—sometimes called “prompt engineering”—were stillin their infancy, and new users were often unable to use LLMs
effectively. There were also concerns about privacy. Generative
AI models that could be securely hosted on a personal computerwere significantly weaker than ChatGPT; the only way to interact
with the strongest models was to use cloud-based services. And
many teachers, and some students, were uncomfortable aboutincorporating AI into language learning. They felt that language
education was fundamentally a human endeavor and that allowing
AI to play a central role in learning might harm students’ language
acquisition and motivation to continue.
Despite the drawbacks and resistance and despite uncertainty
about AI’s future, the performance, flexibility, and ease of use ofgenerative AI seem to ensure that it will play an increasingly30

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important role in language education in the years to come. But
this prospect raises the profound issue mentioned earlier. Neverbefore in human history has it been possible to communicate
using natural language with anything other than humans. What
does it mean for our conceptions of language—our linguistic
theories—and for our ways of teaching and learning languages
that humans now also use our languages to interact with computers?

SoundsFor some aspects of language, the impact of AI may be slight.
One example is articulatory phonetics, that is, the study of how
people make speech sounds by passing air from their lungs
through their throats, vocal cords, oral and nasal cavities, andlips. Students learning how to produce the sounds of anotherlanguage will still need to imitate the physical movements ofhuman speakers of that language. Because computers makesounds by transmitting electrical currents through speakers,
there would be no point in trying to imitate their speech production process.
Another area not yet challenged by AI is gestures—handmotions, facial expressions, and head shaking, which can be different for different languages. Current AI models recognize only
what is typed or spoken by humans and cannot perceive or display gestures. Until onscreen characters or humanoid robots thatrecognize and produce gestures when interacting with humans
become available, we may not need to readjust how we think
about gestures as a part of language.
The theoretical implications are a bit more significant for
acoustic phonetics, that is, the study of the physical properties ofspeech sounds transmitted through the air. When two humans
have a spoken conversation, the sounds of one person’s speechreach the other person’s ears, where they are processed by thelistener’s auditory nerves and brain. In contrast, when a language-capable computer receives speech sounds, a microphone
converts the sounds into analog signals, which are then converted to digital form for processing—a method very different
from the human one. Previously, language students trying to
speak a second language had to think only about the impression
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their speech sounds made on other people and whether their listeners understood them. Now, however, a student practicing
pronunciation with a computer will get feedback based on how
the AI-equipped computer receives and processes the sounds.
Approaches to teaching and learning pronunciation will need tobe rethought.
According to modern theories of language, humans perceive
the sounds of speech not in terms of the air vibrations that reachour ears. Rather, we interpret those sounds as higher-level linguistic units called phonemes, which correspond only partiallyto actual sonic waves. The teaching of second-language pronunciation and listening comprehension thus focuses on phonemes.
Unlike the physical sounds of speech, which vary greatly witheach utterance, phonemes are largely constant from utterance toutterance and from speaker to speaker. While phoneme-based
speech recognition systems have been developed, today’s more
successful systems use deep learning and neural networks toconvert audio waveforms into a numerical transcription that
does not have an explicit phonemic representation. A theory ofphonology that includes interaction between humans and AI
must therefore consider the very different ways humans andcomputers process the sounds of language. Both humans’ recognition of AI-produced speech and the AI’s recognition of humanspeech are likely to be significantly different from what happensin human-to-human conversation.

Grammar
When we move from the sounds of language to the more
abstract level of grammar, one might think that LLMs process
language similarly to human beings. After all, theories ofgrammar have often been based on fairly rigid concepts—partsof speech, inflectional categories, syntactic functions, etc.—andon deterministic, almost mathematical rules. Some theories ofgenerative grammar have even resembled computer programs.
But LLMs acquire and process the grammar of natural languages in a way fundamentally different from human beings.
According to the theories of Noam Chomsky and others,
humans are thought to have an innate general language functionthat becomes specialized on a specific language through limited
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exposure to and use of that language during early childhood. In
contrast, LLMs begin their training without any preconceptions
about linguistic structure, and they require vastly more linguistic
input than any child receives in order to become able to interact
in a language convincingly. Through a method known as unsupervised learning, LLMs are fed large corpora of text, fromwhich they derive numerical representations that can, after
much processing, produce linguistic patterns and structures
similar to humans’. An LLM’s grammatical “understanding” isthus not rule-based but statistical and probabilistic. While a student of a language might be taught that a past-tense verb must
agree with its subject in number, person, and gender and while achild acquiring that language can apply that rule after exposure
to perhaps a few dozen examples, an LLM becomes able to conjugate the verb correctly only after processing thousands ofexamples of such agreement. How these wide differences inmethods of acquiring and processing grammar might affecthuman-AI interaction is not yet clear. But the ability of LLMs to
produce grammatically correct sentences without anything that
corresponds to the human predisposition to language acquisition suggests that some current theories of grammar need to berethought.

NativenessOne area where the different approaches to grammar by humans
and AI might be especially important is native language competence. While some aspects of native language acquisition and ofsocial attitudes about native speakers are controversial, it islargely agreed that, in general, people who acquire a language aschildren have a qualitatively different ability from those who
learn the language as adults. People who grow up speaking alanguage can process certain aspects of grammar—tense andaspect, subordination, pronoun reference, definiteness andindefiniteness—correctly, fluently, and unconsciously, while
those who start later usually never acquire full competence.
Whatever mechanism in the human brain might cause thischange during the transition from childhood to adulthood, it
seems to have no counterpart in LLMs. Statistical processing oftext data does not change as the computer’s components age,33

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and all that the models need to imitate native grammatical competence convincingly is a sufficiently large corpus of text thatreflects the performance of native speakers and sufficient processing capacity. To the extent that theories of language and language education have incorporated the concept of nativeness,they will now need to reflect the fact that nativeness does not
apply to the LLMs that people are increasingly interacting with.

Meaning
While theories of the sounds and grammar of language will needsome revision for the age of AI, perhaps the two aspects of language that will require the most rethinking are semantics andpragmatics, that is, the representation and conveyance of
meaning by language and the use of language for practical purposes.
Linguistic meaning has been theorized in various ways.
Informally, the meanings of words or phrases are often seen as
corresponding directly to concrete or abstract entities. Thisdenotative approach is common in second-language education,in which words may be taught and learned as though they had
simple, fixed meanings, like synonyms in a glossary. For beginning learners, that simplistic approach to lexical meaning might
be unavoidable, but it soon becomes untenable when learners try
to grasp the meanings of words in actual use. In real-life situations, the associative and emotional connotations of words andthe subtle, context-dependent changes in meaning make a purely
denotative approach to meaning inadequate.
Perhaps the most important—and also most complex—
frameworks for thinking about meaning focus on how language
is used in interpersonal interactions and other social contexts.
Philosophers of language with a social bent have theorized
meaning in terms of what people do using language, how people
co-create meaning when talking with each other, and how social
status, gender, ideologies, and power relations affect how meaning forms and changes. In such approaches, language is not a
symbolic signaling system comparable to a semaphore, telegraph, or HTTP connection. Rather, it is a dynamic, fluid, multifaceted medium that is created by and dependent on the people
who speak it and the situations in which it is used. And, once
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again, a fundamental assumption of all such approaches is thatthe users of language are people and only people.
In social theories of language, each language user has various statuses and roles in society: as a person with a name, age,gender identity, nationality, family, cultural background, andoccupation, as someone with likes and dislikes and desires andgoals. All of those factors affect how people use language tointeract with each other. And what we say is influenced not only
by our own personal characteristics; we also change how we talkbased on what we believe about the people we are talking with.
One of the most challenging parts of becoming proficient in asecond language is learning what linguistic forms and conversational strategies to use when addressing others, when asking
questions and making requests, when talking about oneself andothers. One must not only be able to choose the correct words
and sentence patterns; one must also infer what kind of person
the listener is and how they might react. We can ignore such factors when interacting with AI, for it has no social status, no personal identity, no individual will, and it will not get annoyed orangered by a learner’s ignorance or rudeness. Our conceptions oflanguage as a social instrument must therefore change now thatthese new participants in our linguistic interactions are notmembers of our human society.
A related issue is psychological: When people speak witheach other, they try to infer, consciously or unconsciously, themental and emotional state of their interlocutor. Does the otherperson know what I am referring to? Is the other person interested in what I am saying? What does the other person want? Is
the other person pleased, angry, or bored? Our inferences affect
how we use language in very concrete ways. In English, forexample, we use pronouns, demonstratives, and the definite
article when we assume that the other person knows what weare referring to, and we vary the forms of requests based on how
we think the listener will react. How must the theorizing andteaching of such linguistic forms change when people are
increasingly using them to interact with entities that have noconsciousness, emotions, or motivations?

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CultureIn recent decades, the teaching of English as a foreign language
in Japan has gradually de-emphasized the role of culture in language. Awareness of English’s growing importance as a widely
spoken second language and international lingua franca has
made English education’s traditional focus on learning about
British and American culture seem misguided. However, forother languages—Korean, German, Mandarin Chinese—theassociation between learning the language and learning about
the culture of the people who speak it is still strong. And while
culture plays less of a role in school and university English education, individual learners still find value in studying the language
through the cultures of English-speaking countries, especially ifthey plan to visit or live in those countries or they are interested
in some aspect of their particular cultures.
The language models now available are largely acultural.
Although they have been developed by U.S. companies, have
ethical constraints imposed on them that reflect a certain subsetof American values, and use American English as their default
output, the bots do not really embody American or any othercultural background. Much discussion has focused on how
biases in their training data can lead to biases in their output, but
an LLM’s biases are very different from any individual human’s.
An important part of learning to interact with people in a foreign
language is becoming aware of how their knowledge, assumptions, and predilections are different from one’s own. The
knowledge, assumptions, and predilections exhibited by LLMs
are the result of the massive data they were trained on, not of
having been raised and educated as a human member of a particular human culture.

New Theories
A theory does not need to be a rigorous academic frameworkwith explicit definitions, claims, and methodologies. Informal
theories, personal systems of understanding, are also useful—indeed, they are unavoidable. Given the complexities of theworld in which we live, we have no choice but to use abstract
concepts and principles to make sense of what we experience.
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Language in particular would be difficult to think about, andprobably impossible to teach, without at least an informal
theory. Even a language teacher with no academic training
thinks in terms of abstract linguistic concepts: word, sentence,
pronunciation, accent, politeness, clarity, correctness. Thoughperhaps not fully rigorous or internally consistent, those concepts and the teacher’s understanding of them constitute thatteacher’s theory of language. Without such a theory, the teacher
would be unable to teach.
At some point, human interaction with AI will have to beincorporated into formal theories of human language, theories
that could form the frameworks for rigorous academic research.
But technology is evolving too rapidly to make that feasible now.
Any full-fledged theory proposed today could easily go out ofdate as the AI becomes more capable and multimodal and as it
acquires more human-like characteristics, such as consciousness,embodiment, personality, and active roles in society.
But language educators cannot wait for the technology tomature. Our students are already using AI to learn and practicelanguage, and teachers need to figure out how best to guidethem. We need new theories of language now, new ways ofunderstanding how people use human language while interactingwith today’s intelligent-seeming, linguistically fluent, but nevertheless unhuman bots. Because language teachers usually draw
on their own experience learning languages, they need to interact
with the latest bots themselves to gain a sense of what it is like tolearn a language from them. They should also observe their students using the AI and ask them for their impressions. They
should try to figure out how interacting with AI will shape theirstudents’ learning and language acquisition and how it might
motivate or demotivate them. Most of all, they should be willingto modify their long-held beliefs about what language is andhow it is learned—that is, their theories of language. Since thearrival of these amazing new language models, these fluent,engaging, and sometimes frightening nonhuman conversation
partners, our old ways of thinking about language need tochange.

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