AI and new forms of knowledge work: How writing education canhelp equip students for an uncertain future1
Chad Nilep
Nagoya University
One way in which disruptive technologies have historically resulted in social and economicchange is by creating new jobs while displacing or reducing the value of existing jobs. While
it is easy to predict that current developments under the label of “artificial intelligence” willtransform work, it is exceedingly difficult to predict just what skills will best serve in the future.
Perhaps the best tools that educators can provide today come from classical liberal arts,
especially logic, rhetoric, and critical thinking. Questions of how to improve job skills, inaddition to being difficult to answer, may be counter-productive to ask. As Ekbia and Nardi(2017) note, 21st century innovations from social media to self-service grocery checkout so far
have served not to reduce human labor but to transfer its value to the corporations that control
the technology. Thus, important questions arise regarding how best to govern and to organize
society in ways that allow for continued gains without sacrificing equity. Obviously, writing
education will not answer these questions on its own, but our students will need to grapple withthem. The changes wrought by earlier disruptive communication technologies took more thana decade to resolve and are in many ways still being worked out. Therefore, the most beneficial
contributions of writing education at present are to develop skills in critical inquiry, rhetoricalreading and writing, and logical argumentation with which today’s learners can facetomorrow’s crises.
1. Disruption, winners, and losers
Generative AI is described as a “disruptive technology”, one that promises to remake theeconomy, labor, and society. Historically, disruptive technologies from steam power to themicroprocessor have enriched societies by reducing labor costs, increasing productivity, andsometimes introducing new categories of products. At the same time, however, thesetechnologies have tended to displace some workers or to reduce the value of their existing jobs.
Industrial and social progress usually – perhaps inevitably – produces winners and losers. Some
individuals gain a great deal from changes, while others gain only marginally. Likewise, someindividuals stand to lose a great deal more than they gain, while for others such progress isindeed a boon.
One familiar story about the Industrial Revolution of the 19th century in England involves
the Luddites, individuals who feared that the new machine age would somehow destroy theworld. According to one version of the story, the Luddites resisted progress by smashingengines and burning down factories in a futile attempt to stop the arrival of the industrial futurein which we would all enjoy a higher standard of living through modern technology. The epithet
luddite, still used to disparage technophobes whose excessive conservatism is thought to slow
This manuscript is based on “Equipping students for an uncertain future”, an oral presentation at the 5 thInternational Symposium on Academic Writing and Critical Thinking, held at Nagoya University 16 February
2024, and responses from participants in the symposium.
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progress, comes from this telling. This version of the story is, however, wrong in most of its
details. In fact, the Friends of Ned Ludd, as they styled themselves, were not a band of English
rustics but rather a labor movement with specific concerns about how their work would bevalued in the machine age. They were mainly highly skilled, highly paid workers in the textile
industry, such as carters, weavers, and knitters. While the Luddites did smash factory machines,
among other forms of labor action, this was not because they believed that the machines would
destroy the world in some unforeseeable manner.2 Rather, it was a rational fear of how comingchanges in the market would affect their own standard of living. Skilled artisans worried thatthe availability of inexpensive, factory-made cloth would reduce the need for their productive
labor and cost them their high standard of living. With the coming of industrialization, this isindeed what happened. By 1850 hand-made cloth was essentially gone from the English
marketplace, and with it the high wages of the people who had made it. While the Industrial
Revolution created new jobs and a generally rising standard of living, it destroyed the standardof living of some artisans. Average pay for members of the working class was essentially flatthrough most of the 19th century (Kestenbaum and Goldstein 2015). This average, however,masks the inequality among those who gained and those who lost income during the period.
Nicolas Crafts (2010), synthesizing work by Robert Allen (2009) and Joel Mokyr (2009),
suggests that less than five percent of Britain’s population accounted for about one third of thegains during the Industrial Revolution. Incomes for the Friends of Ned Ludd dropped sharply
and remained lower for the next generation of workers than had been the case for these highly
paid artisans. It would not be until their grandchildren’s generation that factory workers’
standards of living rose to the level that had been enjoyed by the Friends of Ned Ludd
(Kestenbaum and Goldstein 2015).
Similar to the ways that factory machines and fossil fuels disrupted laborers inmanufacturing, some contemporary commentators predict that generative AI will remake
knowledge work. In fields from education to law, from journalism to management and far
beyond, generative AI may replace many of the tasks today performed by educated workers. It
is difficult to predict whether these changes will reduce the value (and the compensation) of
human workers, free them to work in more productive ways, or increase demand for their labor,
perhaps through the introduction of new categories of product or services. What we can expect,
though, is that there will likely be changes in how we labor in the future, and that this willproduce economic winners and losers.
The anthropologist Ilana Gershon compared her recent studies of AI in the workplace withher earlier work on New Media (e.g. Gershon 2011). Gershon (2023) said that when she started
studying New Media in the 2000s she was “constantly surprised” during fieldwork. Of course,
it is common to be surprised during fieldwork; that surprise is a major reason to do it. Scholars
go to the field to discover new things and then analyze them. But what was unusual about
studying New Media, Gershon suggests, was the constant surprise. Every time she returned toa field site or re-interviewed subjects, she would find people using internet or cellularcommunication in new and unexpected ways. This constant surprise continued for about ten
years until people gradually settled on new norms for communication in the workplace andtheir private lives. She says that recent swings in public discourse over AI, from technoGiven what we now know about links between fossil fuel emissions and climate change, such a prediction, had
it been made, might seem prescient now.
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optimism to moral panic to resignation, remind her of that constant surprise. And like norms
around New Media, it may take a decade or so to settle on norms for the use of AI at work,school, and anywhere else we end up using it.
Since it is currently difficult to predict what knowledge work will look like in just a few
years, educators today struggle to decide how to deal with AI in education. What should we beteaching students about the nature and usefulness of current and coming applications? How
and to what degree should we guard against students’ use of AI as a form of academic
dishonesty, for example by submitting computer-generated term papers? Should we, as
educators, embrace it as a useful tool for our own work, or should we fear it as a potential threat
to our own positions? My own struggles with these questions may serve to illustrate some of
the challenges involved, as well as my reason for looking past specific answers and focusing
instead on rhetoric and logic in writing education.
In April 2023, the start of the first school term after the release of GPT-3.5 and ChatGPT(OpenAI 2022), I spent a considerable amount of time developing and explaining an AI policy
for my undergraduate writing courses. I illustrated ways in which AI can produce
grammatically correct texts that were nonsensical or factually wrong. During the first classmeeting we watched excerpts from a YouTube video (Cocktail Chemistry 2023) in which abartender asks ChatGPT to invent a new cocktail recipe, then mixes and tastes the cocktail.
One image captured from the video shows a quite plausible-looking recipe in the ChatGPT
display screen. The recipe shows measurements of ingredients, instructions for mixing themappropriately, and a description of the cocktail. Later in the video, the bartender samples the
cocktail that he mixed using the recipe and declares it too sweet; apparently the algorithm did
not know that both grapes and syrup are sweeteners. I concluded the lesson by pointing out thatalgorithms do not think in the same way that humans do or know things that are common
knowledge among people. Therefore, the texts that they produce tend to be grammaticallycorrect but not necessarily meaningful. Instead of expecting AI output to make sense, I
cautioned the students, they need to bring meaning to their writing and rely on the algorithms
solely for assistance.
My course policy in April 2023 regarding the use of AI in class had four points. First,
students were encouraged to use AI or other software to check for errors of grammar or spelling.
Second, students were allowed (though not necessarily encouraged) to use some amount oftranslation, especially at the level of individual sentences or phrases. I warned them, however,not to translate entire paragraphs or essays, as the results tend to sound stilted and aresometimes nonsensical. Third, I warned students that they should not write paragraphs or
essays in Japanese and use machine translation to render it into English text. As describedabove, the results of such practice are less than ideal. Moreover, the point of the course was tolearn methods for communicating to various audiences using appropriate styles and patterns(read: rhetoric, argumentation, and grammar) in English. Finally, I declared that students mustnever submit work produced by AI without explaining what tools were used and how. Students
who violate this rule would receive an F in the course.
Notwithstanding the prohibition on AI-produced work, I received some suspicious papers.
In each of these cases I put a sample of the paper into GPTZero (Tian, Cui, and Mimi 2023)
and received a numerical estimate of the likelihood that GPT or similar algorithms had
produced it. I then contacted the student, confronted them with my suspicion and the numericalevidence, and asked them to provide evidence that they had drafted earlier versions of the paper.

In every case, the students I approached admitted that they had submitted a paper produced by
ChatGPT or a similar product.3
In contrast to the detailed but not completely effective policy described in April 2023, mycourses in October 2023 used a much less comprehensive policy. I did not dedicate the firstcourse meeting to illustrating AI’s foibles. I merely told students that such tools are useful but
fallible, and that I would not accept assignments that had been created with generative AI thatwas not disclosed. As in the spring term, I received a small number of suspicious papers. Again,
I confronted students with my suspicions and with numerical evidence suggesting that the work
was computer-generated, and again each student confessed to the violation. In both cases – thedetailed policy description of the spring courses and the brief mention in the autumn term – I
received about the same number of suspicious papers, and in each case the number of such
papers was small – somewhat less than 5% of all papers received.4 That suggests either thatmost students do not attempt to cheat, or (less probably) that most cheating is not detected.
Studies of plagiarism (e.g. Comas and Surenda 2010, Park 2010) suggest that cheating inwriting courses is most common when students lack academic experience or feel alienated fromthe course content and practices. The writing courses described above are required courses forfirst-year undergraduates. Students therefore must enroll and earn a passing grade even if they
have no interest in the topics. The relatively low rate of attempted cheating in courses where it
may be expected suggest (albeit anecdotally) that education policy and teachers’ attention may
be better focused elsewhere. Furthermore, given that the students who feel tempted to submit
AI-generated papers are usually the least confident English users, it is not hard to guess that
their essays filled with complex grammar and obscure vocabulary are not their own work. In
such courses, carefully crafted and enforced AI policies may not be strictly necessary.
Even if AI is not a problem to be defended against, however, questions of how best to
structure writing courses for a future in which its use is commonplace are not easy to answer.
Experiences with then-new information and communication technologies at the beginning of
this century suggest that answers may not be forthcoming for several years. But a first step in
adapting writing education should involve thinking about the nature of AI and thinking about
the nature and goals of writing education.
2. On the nature of AI
In the early 2000s I worked briefly with interdisciplinary teams in natural language
processing. Our goals at that time included making systems to read texts and parse theirsyntactic structure; to transcribe speech into written words; and to understand and respond
appropriately to spoken requests over the telephone, among others. Today, computer systems
can do most of these tasks – not perfectly, but far beyond what was possible a generation ago.
There was a common joke in interdisciplinary labs at that time: “The more linguists that wefire from the team, the better the algorithm works.” The first time I, as a linguist, heard thisjoke, I understood it to be a form of good-natured ribbing at my expense. In addition, though,Despite the course policy stating that such behavior would result in the student failing the course, I instead gave
no credit for that single paper and required the offending student to file a report detailing what they had done.
Future violations of the university’s AI policy, published subsequently, might result in loss of course credit or
expulsion.
4
This figure is avowedly inexact. To protect students’ privacy, precise data was not collected or analyzed. Figuresin this section should be understood merely as general descriptions and not the result of any quantitative analysis.
3the joke provides a useful window into how the teams approached these tasks, and into thenature of AI systems. Linguists are interested in – among other things – how the human mind
processes language. As a result, linguists on the teams tended to pursue computational models
that work the way we think that the human mind works. Engineers and computer scientists, on
the other hand, were interested in designing systems that give the correct output. This can be amuch simpler problem. “Firing linguists”, therefore, may simplify the process by concentratingon the output without excessive concern for how it comes about.
The output of language-based generative AI systems resembles human communication, but
it may not be correct to think of these as the same thing. Elena Esposito (2017, 2022) reframes
natural language processing systems not as “artificial intelligence” but as “artificial
communication”. When human beings interact with AI-based systems, we experience a formof communication that is different from interaction with other humans – perhaps so different
that we should not regard the algorithms as participants in communication. Although thealgorithm produces text or sound that appears to be the product of intelligence, it is difficult to
say that the algorithm has knowledge or communicative intent in the way that a human being
would in the same situation. It may be nearer the mark to say that the actual intelligence present
is in the human being’s interpretation of the text more than the algorithm’s production of it.
Christoph Purschke and colleagues (2023) further limit communication between humans
and algorithms from “artificial communication” to “artefactual communication”. Large
language models are artefacts, built from the texts they are trained on. And these texts arethemselves artefacts of human intelligence. Thus, what we call AI – large, pre-trained models
built from corpora of texts, images, music, or what have you – are not intelligent in the same
way that humans or animals may be. They are good at producing output that resembles thekinds of material they were trained on, but the resemblance to the human intelligence that
produced that training input is at best imperfect, and perhaps of a different type.
3. Looking back to move forward
How should we think about the relationship between writing education now and theknowledge work of the future? As described in section 1, new technologies tend to havewinners and losers. The Industrial Revolution displaced artisans, but it created factory jobs –and for those who controlled the new means of production, it created the modern industrialist.
New media displaced corporate in-house graphics departments but created web developers andsearch engine optimizers. It is a relatively safe bet that generative AI and the applications thatcome from it will create new ways of working and will displace certain tasks from theworkplace. It is, at the same time, extremely difficult to predict exactly what those changes will
look like. Although I will not here venture specific predictions, I do want to reflect on worries
about what may result from currently ongoing disruptions.
Hamid Ekbia and Bonnie Nardi (2017) note that work in which human labor combines withartificial systems is not just coming: it is already here. They call this computer-mediated work
heteromation. Unlike automation, in which industrial machines do work in place of humanlaborers, heteromation relies on both human and computer inputs. This includes creative work,
such as social media “influencers” or YouTubers who create works which computer algorithms
then deliver to audiences; service providers, such as ride-sharing drivers whose work is sold
via apps; and self-service systems such as grocery check-out, in which customers or users arethemselves the laborers. In heteromated systems, Ekbia and Nardi point out, human labor isessential, yet the bulk of the monetary value – in some cases, its entirety – goes to thecorporations or businesses that control the computer systems. In a system where human userlaborers contribute most of the value, they argue, those users should share more fully indecision-making and economic gains.
We are beginning to grapple with new technology that will affect – and is affecting – how
we work and live. Some changes will have major implications, and some only minor
consequences. Society will need to balance various demands such as productivity versus
equality, and the needs of workers and producers against the desires of consumers and users
and the hopes of owners and investors. These changes are new in their particulars (heteromationof human and machine labor; novel products and jobs), but they are familiar in their broad
contours. There will be winners and losers, and groups and individuals will try to do what isbest for themselves, their group, and the world more broadly. In the face of this specificallyuncertain but broadly predictable future, the best thing that I can do for my students and theinstitutions I consider my in-group is to pattern my teaching on classical liberal arts.
From Europe’s Classical period, training in logic and rhetoric has been considered
fundamental to liberal education. In medieval Europe these subjects, along with grammar, wereidentified as the Trivium, the elementary subjects of liberal arts education. Looking back tothese long-practiced subjects may be the best way – or at a minimum, one way to prepare
today’s students for tomorrow’s challenges. In my own teaching I emphasize logic and rhetoric
more than grammar, for two reasons.
The first reason that I do not feel the need to emphasize grammar in my writing courses isthat the students I mainly work with, scholars writing in a language that is not their dominantspoken language, are well versed and sometimes overly focused on grammar. It takes a lot oftime and effort to master the form of a foreign language, and in that process we convince
ourselves that the form of that language is what is most important. Of course grammar isimportant, but in concentrating too much on linguistic form we may forget that it isfundamentally a tool for the communication of thought. Language – grammar, along withrhetoric and logic – allows us to analyze ideas and to communicate our own ideas to otherpeople. It is those ideas, rather than the tools used for analyzing and communicating them, thatare central to the process.
The second reason to de-emphasize it, is that AI powered applications are already relatively
good at producing good grammar. In fact, I encourage my students to use grammar checking
applications to help them spot errors or suggest appropriate vocabulary. But I caution them not
to trust what the applications suggest. While AI is relatively good at producing sentences likethe ones it was trained on, it does not understand the ideas that those sentences represent. As aresult, AI can “hallucinate” untrue ideas. Grammar checking systems, likewise, sometimes
suggest changes in the name of eliminating grammatical errors that have the unwanted effectof changing the meaning that the writer intended. Therefore, I advise students to use software
solutions for the tasks they are good at, such as spotting errors or providing first-passtranslations of text from one language to another, but I caution them to be wary of the output
and to take care that the corrected or translated texts accurately communicate the writer’s ideas.
Relying on medieval and classical education may seem perverse as a response to newtechnologies. Perhaps, like the historical Luddites, I seem to be rejecting new technology inorder to preserve an occupation that is becoming outmoded. I do not believe that is the case,though. The situation we find ourselves in today around emerging technologies built on
generative AI is new in its specific details. In its broad outlines, however, the situation isfamiliar and at least somewhat predictable. New styles of communication, new jobs and work
styles, and new social norms are emerging all the time. What is true now and will be true in the
future is that people will need to agree on ways to integrate these novelties into their lives andsocieties. Rhetoric, logic, and the forms of critical thinking that flow from them offer the mosteffective tools for people to effect such change.
Logical argument – informal as well as formal argument forms – allow us to test our own
knowledge and intuition, and to try to approach understandings of “truth” to the greatest extent
that we can. It is important not only to recognize valid and invalid types of arguments, but also
to be able to make valid arguments in order to produce clear and convincing texts. Rhetoricalappeals, likewise, allow us to share our understanding with others, to convince them of thevalue of particular contributions to knowledge, and in so doing to build social structures andcoalitions to act in common. Not only is the crafting of rhetorical appeals necessary, but theability to read rhetorically (Peterson 2017) also allows us to respond most effectively, resisting
shallow or manipulative appeals and embracing and joining those that further our best interests
and what we believe to be best for society.
Generative artificial intelligence, like other new technologies, promises to disrupt labor,
education, and society over the next several years. Like most disruptive moments, this one islikely to produce both positive and negative effects, which will almost certainly be unevenly
distributed among people and groups. Although the specific changes are difficult to predict, it
is easy to foresee that today’s students will need appropriate tools and training to deal fairly
and effectively with whatever comes. The greatest contributions that writing education canmake toward this future do not depend on embracing or resisting the technology, but on keeping
our and our students’ attention on the fundamentals of communication, argument, and rhetoric,
and the social goods that can flow from them.
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