Close Enough to Human:
Artificial Intelligenceand the Future of English Education
Tom GALLY

The application of new technology to language education has
always been hit or miss—perhaps more often miss than hit.
As a language learner and teacher, I have seen many technologies introduced, hyped, and adopted with great fanfare, only
to be marginalized or abandoned. When I was a child in the 1960s,
my parents owned sets of LP records for learning Spanish andFrench. I put them on the turntable once or twice and listened toa few lessons, but I don’t remember anybody in my family everstudying from them seriously. At that time, records and reel-toreel tapes seemed to revolutionize the learning of languages.
Before audio recording and radio, the only way to hear thesounds of a foreign language had been to be in the presence of a
speaker of the language. Recording and broadcast technology
seemed to be a great advance: We could be in our home inSouthern California and hear the sounds of Spanish as spoken in
Madrid or French as spoken in Paris. Presumably those LPs
would enable us to learn the language better and more easily. At
least in the case of my family, though, they didn’t.
When I entered college in 1975, the audiolingual approachto language learning and teaching was still dominant. I enrolled
in introductory Chinese. In addition to our five fifty-minuteclasses per week taught by teachers from Taiwan, we also had to
go to a language laboratory for several hours a week. There wewould sit with headsets on, listening to and repeating reel-to-reelrecordings of the consonants, vowels, and tones of Mandarin
and of sentences being read aloud from our textbook.
All those hours in the language laboratory did, I think,
improve my Chinese pronunciation, though they did not prevent me from abandoning the study of Chinese two years later.
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And eventually that audiolingual approach to teaching foreign
languages fell out of fashion altogether. I am not aware of any
universities in Japan, at least, where English students still go to alanguage lab and listen to and repeat recordings. One reason isthat theories about language teaching have changed. But anotheris that the attraction of that shiny new technology has long since
faded. Students grew bored with it, and educators and administrators learned that audio recording was not a silver bullet that
could replace human language teachers.
I saw another example in college of the premature adoptionof technology. In several buildings on campus that had been
built in the late 1960s, every classroom had one or two large television monitors hanging from the ceiling. That was before video
could be recorded on cassettes, so the televisions were connectedby cables to bulky reel-to-reel videotape decks in a central control
room. The idea had been that students, instead of listening to ateacher’s lecture, would sit in a classroom and watch prerecorded
lessons on the monitors.
Like language labs, those television monitors had been seen
as a way not only to apply a supposedly superior pedagogicalmethod, that is, audiovisual media; they were also intended tosave money on human teachers. The novelty and ease of recording
images and sound on magnetic tape rather than film had also
been an attraction. But by the time I enrolled at the university,
the equipment was just collecting dust. In my three years on
campus, I never saw or heard of any classes in which those televisions were used. The excitement about video as an education
tool that could replace the teacher had been misplaced, and theappeal of the shiny new technology had been fleeting. It turned
out that students learned better from a human teacher in theclassroom.
Since the early 2000s, when I returned to academia as ateacher, I have seen more cases of the overeager adoption of newtechnology. In 2007, I was asked to teach several undergraduate
writing classes in which the students would use laptop computers
in class. A fancy special classroom, paid for with a large government grant and private donations, was built for the experimental
classes, in which students would use expensive computers withtouchscreens and styluses. For a couple of years, with constanthelp from technical assistants, I taught my English academic
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writing classes in that classroom using those tablet computers.
At the time, few students brought their own laptops to school,
and they seemed to enjoy the opportunity to try out the novel
touchscreen interfaces. Writing and revising on the computers
did seem more convenient than with a pencil and paper. And
since the computers were connected to WiFi, students could
search the Internet to gather information for their papers andshare files of their drafts with each other during class. Althoughstudents were often frustrated by the slow startup times of thecomputers and other technical problems, I thought the classes
went reasonably well. And it was fun for me to be able to use thelatest technology in my classes.
After my role in that pilot project ended, I moved back to aregular classroom and taught once again with only a blackboard,
chalk, and handouts printed on an old-fashioned copy machine.
The students did their in-class writing by hand. After that high-techclassroom, it all seemed quaint and inefficient at first. But not amonth had gone by before I noticed that the students were moreengaged with the class than in the previous classroom. Without
the distractions of the flashy gizmos, they were able to concentrateon the language, the arguments, and the content of their writing
better. Students learned more without the technology.
In recent decades, many other technological innovations havebeen adopted with much enthusiasm but ultimately abandoned.
These include language lessons taught using computer software,classes managed and conducted through websites, and students
practicing language use through e-mail, text chat, and social
media. Often the reason for the failure was the technology’s
immaturity. The tablet computers we used in 2007 had beenslow and difficult to operate, with a too-steep learning curve forthe students; such classes would go much more smoothly now
using smartphones or iPads. The video technology that had
gained no traction at my university in California in the mid-1970s
was expensive and difficult to use, a far cry from the ease ofvideo recording and sharing today.
But the biggest reason for the failure of most new educational
technology was not its cost or complexity. Rather, it was its
inability to provide the human interaction that is essential tomost learning. Records and videotapes, computer software andwebsites, had attracted attention and investment largely because
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they seemed to reduce or eliminate the need for human teaching.
Teachers saw them as ways to cut down on their hours standing
in front of classrooms, and educational administrators saw themas ways to save on teachers’ salaries. But those new technologies
turned out to be poor substitutes for human teachers. Youngpeople just seemed to learn better, and to be more motivated tocontinue studying, when they had a person in front of them to
instruct, advise, encourage, nag, and entertain them.
After observing so many failures of technology adoption
over the years, and despite being an early adopter of digitaltechnology myself, I became jaded. I did not think any new technology would significantly reduce the need for human teachers,
especially in foreign-language education. When I helped establish
a large undergraduate English program in 2008, I made sure that
we used technology only as an aid for teachers, never a replacement. Fifteen years later, classes in that program continued to besmall, with an average of only fifteen students each, taught inperson by a team of nearly thirty full-time faculty. Fortunately,
our university administration understood the value of human
instruction. Despite the expense of teacher salaries, we were
never pressured to try to replace some of those teachers with,say, interactive software or computerized assessment.
* * *My skepticism about the potential of technology to replace
human language teachers began to weaken in late 2016. That waswhen advances in machine learning technology enabled, for the
first time, reasonably good translation by computers. I had long
believed that translation could be done only by human beings,
because only we were able to grasp the intended meaning of atext and express that meaning appropriately in another language. I had been wrong. Even though computers still did not
grasp meaning in the cognitive way that humans do, when trained
deeply on large bilingual corpora, electronic neural networks
were in fact able to imitate the translation skill reasonably well.
Language—and, by extension, language teaching—no longer
seemed like a uniquely human ability.
My skepticism about the potential for computer language use
vanished in late 2022, when a further leap in artificial intelligence
technology suggested that computer software might, finally, beable to replace human language teachers. On November 30, the38

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U.S. company OpenAI released a public research preview of its
interactive language model ChatGPT. Soon the Internet andnews media were filled with reports and examples of its amazingly human-like, sometimes superhuman ability to respond to
text prompts naturally and, it seemed, intelligently. When asked
to write an essay on, say, the influence of the French Revolution
on English literature in the Nineteenth Century or the depictionof people with disabilities in Hollywood movies of the 1930s,
within seconds it would produce an original, well-organized,
multiparagraph essay that would receive a good grade frommany high school and university teachers. It could also compose
original stories on nearly any topic—not the most creative stories, perhaps, but well written and with a pleasing narrative arc.
And it could explain physics and paleontology, discuss surfing,
gardening, and yoga, write computer code, compose rap lyrics,
and much, much more.
In my own testing of ChatGPT, I tried to see how well it
seemed to understand language. I gave it sentences containing aword with multiple meanings—such as “critical” or “oversight”—
and asked it to identify the sense in which the word was used ineach sentence. It performed flawlessly. I gave it a list of words andasked it to compose illustrative example sentences for each; in a few
seconds, it produced natural, well-formed sentences as good as any
that I could write. I gave it the first paragraph of a recent shortstory from The New Yorker magazine and asked it to annotate thetext for learners of English. It provided a list of the relatively hardwords and phrases that appeared in the paragraph—antecedents,
plagues, dire weather—with correct glosses in English and Japanese.
Many years before, when studying Russian, I had found suchannotated texts useful for improving my reading skills. But I
could use only texts that someone else had already annotated.
With ChatGPT, it seemed, learners could obtain annotations forany text they wanted to read—within seconds.
I also tried conversing with ChatGPT. Although during the
research preview it could be accessed only through a text interface, I found it quite enjoyable to chat with. Playing the role of aJapanese college student, I told it to ask me how I had spent mysummer vacation. It responded with questions like “Did you goon any trips or vacations during the summer?” and “Did you haveany memorable experiences or moments from your summer
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vacation?” When I answered “I spent most of my vacation helping
my parents in their shop. But on days when the shop was closed,
I usually went to the beach with some friends,” it responded“Thank you for sharing! It sounds like you had a mix of workand relaxation during your summer vacation. It’s always good tofind a balance between the two.” Occasionally it responded withcanned-sounding answers—when I said that I had gone to Hokkaido, it responded with “Hokkaido is a beautiful place to visit!
It’s the northernmost of Japan’s main islands and is known for its
natural beauty, including mountains, forests, and lakes.”—overall
the responses were friendly and appropriate reactions to what I had
said. ChatGPT sounded like an experienced English conversationteacher who knows how to carry on an interesting conversation
with a student without getting too personal or intimate.
ChatGPT was not flawless, though. As many people noted
during the first weeks after its release, it was often terrible atquantitative reasoning. When I asked whether the sentence“December 31st is the penultimate day of both the month andyear” was correct, it said it was. When challenged, it insisted that“[t]he word ‘penultimate’ helps to convey the idea that December
31 is the next-to-last day in both the month and the year, whichis accurate.” No, it isn’t. Compared to some other large language
models, ChatGPT did not seem as prone to “hallucinations”—
that is, plausible-sounding but false assertions of fact—but itwould occasionally give me information as flatly incorrect as its
statement that the 31st is the next-to-last day in December.
Its explanations of grammar could not be trusted, either.
When told to correct typical learners’ mistakes in English, it didso flawlessly. But when asked why it had made those corrections,
its explanations were often wrong. It misidentified singularverbs as plurals and the infinitive particle to as a preposition.
And while it could translate between Japanese and English aswell as the best dedicated machine translation systems thenavailable, its explanations in English of Japanese grammar or ofEnglish grammar in Japanese were often wrong. It could converse in Japanese as well as English, but its Japanese seemed lesspolished and natural than its English—presumably because it
had been trained on a much larger volume of English text.
Despite those flaws, many of which seemed likely to be
fixed eventually, for application to language learning ChatGPT40

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was clearly already useful. Its deep and wide knowledge of English
vocabulary, its ability to grasp the meanings of words, sentences,and entire texts, its readiness to discuss nearly any topic, and its
flawless production of English—in weeks of testing, I never saw
it make a single grammatical or vocabulary error in English—
showed that it was as competent linguistically as any professional
English teacher.
In the weeks after the research preview began, OpenAI’s
future plans for ChatGPT were unclear. But it seemed likely that,after having refined and improved its performance, the company
would release a commercial version, presumably with application
programming interfaces, or APIs, that would allow other software
companies to integrate its functions into their apps. That would
enable developers of language-learning software to tailor ChatGPT’s responses to the needs of learners. A spoken interface wassure to come as well. Talking digital assistants were available forsmart speakers and other applications, and it wouldn’t be long
before people would be able to have conversations with virtual
characters driven by ChatGPT.
I also knew that OpenAI would not have a monopoly on
ChatGPT’s level of performance. Earlier in 2022, the company
had created shockwaves similar to ChatGPT’s when it released
DALL-E 2, a system for creating images and art from text
prompts. Within months, several comparable programs, bothcommercial and open-source, had appeared as well. While training large language models required advanced expertise andexpensive computer resources, the potential payoff was so largethat competitors were sure to release software at least as powerful as OpenAI’s before long.
* * *In 1950, the British mathematician and computer scientist Alan
Turing proposed a test for evaluating whether a computer program
appeared genuinely intelligent. People would interact with boththe program and a real human being through a text interface. Ifthey could not tell the difference, then the program could be said
to exhibit artificial intelligence. In its initial release, ChatGPT
probably could not pass the Turing test yet, nor did it seem
designed to do so. Its responses were too quick, too complete,
too flawless linguistically for any human to produce. Its errors at
quantitative reasoning, including simple arithmetic, would seem
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unlikely for a human with such fluent language skills. And,
when asked about itself, it would reply that it was an artificial
intelligence assistant trained by OpenAI and without personal
experiences or feelings. Only an intentionally deceptive humanwould give such replies.
In other ways, however, ChatGPT was frighteningly humanlike. Not only could it produce original English sentences accurately and naturally. It also understood the prompts I fed it
exceedingly well and nearly always responded appropriately.
Particularly impressive was its understanding of deixis—that is,context-dependent meaning. In several long exchanges, I was able
to ask questions like “What about the other one?” in reference tosomething much earlier in the conversation, and it told me about
the correct “one.” The grammar of pronoun reference in English
is complex. Despite many years of explaining English texts tostudents, I am not confident that I could come up with a definitive set of rules that would correctly match each “it” and “they”
and “first one” and “other one” to the corresponding referent
earlier in a text. The grammar, though intuitive to me as a native
speaker, seemed to depend on complex interactions among syntax,
semantics, and knowledge of the world that I could not fully
explain. As with its vast knowledge about many subjects, ChatGPT
seemed almost superhuman in its grasp of deixis.
But what struck me most was how conversing with ChatGPT
felt like talking with a real person. Although I knew it was only acomputer, circuits without consciousness or feelings, I foundmyself beginning conversations with “hello” or “good morning,”
prefacing requests with “please,” and finishing with “thank you.”
My unconscious politeness was partly due to its friendly tone.
But I think I was swayed more by its apparent understanding ofnearly everything I input. Even though I knew better, there
seemed to be a conscious mind at the other end of our text
exchanges. And while I was careful to refer to ChatGPT as “it,”other people I talked to called it “he” or “she.”Though it perhaps could not yet pass the Turing test, ChatGPT’s ability to interact with users in an almost human way, as
well as its outstanding linguistic ability, convinced me that it
could be applied much more effectively to language learning
than had LP records, reel-to-reel videotapes, tablet computers, or
any other previous new technology I had tried. While attractive
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when they first appeared, those technologies had all lacked what
kept so many human teachers employed: the ability to interact with
their students as individuals. ChatGPT showed that language
models could also have that ability. Once they were linked toaudio and video interfaces, and once their memories, responses,
and personalities were tailored to language teaching, they would
be effective, enjoyable, inexpensive, and convenient tools forpeople learning second languages—not human, of course, butclose enough.
What would happen to language teaching? One possibility
was much less need for human-led instruction. Highly motivatedlanguage learners—those already driven to study a language outof interest or need—would be increasingly dissatisfied withclassroom instruction. While a flesh-and-blood teacher might
continue to be more compelling as a role model, learners would
realize that they learned more from the digital avatars, which,with their vast knowledge, memory, and adaptability, would be
able to tailor their instruction better to the individual’s ability,interests, and needs. Less motivated learners, including those
who didn’t want to study another language at all, could be motivated by language-teaching bots integrated into online games. In
both cases, the ability of the new AI-driven characters to interact
with learners in a fun, interesting, and personal way would
make it difficult for classroom teachers to compete.
The role for humans in language education would not, I
thought, disappear completely. No matter how human-like thesoftware became, it seemed likely that young people would continue to seek role models in other humans, especially those theyknew in person: their parents, their siblings, their peers, theirteachers. While the AI might be better than a human teacher at
helping students learn and use another language, the questionsof why they should learn the language, of what role that language
might play in their lives, would be answered better through theexperience and advice and example of flesh-and-blood people.
But the need for language teachers to do the day-to-day tasks oflanguage education—instructing students from textbooks, explaining grammar and vocabulary, demonstrating how a language isused—could very well end. Teachers would be less like instructors
and more like guides or coaches.
Although I was sure that large language models would have
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KOMABA JOURNAL OF ENGLISH EDUCATIONa powerful impact on language learning, I was much less confident about the ability of educational systems to adapt, especiallyin Japan. The response among educators and administrators to
the challenges raised by improved machine translation had beenvery slow. While some teachers, especially at the university
level, had started to incorporate machine translation into theteaching of English and other languages, English education at
the elementary and secondary levels—the national curriculum,
the government-approved textbooks, the entrance examinations
for high schools and universities, the training and certification ofteachers—continued as before, with little recognition that people
throughout the world were now using software to communicate
across language barriers. ChatGPT represented an even greater
challenge to long-held assumptions about how and why people
use and learn other languages. I was pessimistic that the Japanese
educational system would be able to adapt any time soon.
When I wrote this essay, just a few weeks after ChatGPT
was released, I was keenly aware that the performance andapplication both of it and of other large language models mightevolve in unanticipated ways even in the few months before theessay was published. I also knew that, a few years into the future,
I might appear to have been ridiculously naive in my predictions
about how this new AI software would affect language learningand education. But the huge step forward in computer language
use shown by ChatGPT, and the steadily accelerating pace ofadvances in other areas of artificial intelligence, convinced methat a new era in language learning and education had indeed
arrived—for better or for worse.
Three videos about this topic made by the author in December 2022 canbe viewed at the Internet Archive: “ChatGPT and Language Education” (https://archive.org/details/chat-gpt-and-language-education),“Using ChatGPT for Language Learning” (https://archive.org/details/
using-chatgpt-for-language-learning), and “ChatGPT and the Future of
Language Learning” (https://archive.org/details/chatgpt-and-thefuture-of-language-learning).

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