The Language, Theory, and Artificial Intelligence Project Wake Forest University Humanities Institute
Prompting Meaning: Teaching and Learning in the Age of AI
CONTRIBUTORS
Beyond Job Readiness: The purpose of higher education in the age of AI
Before We Pull the Vine
Table of Contents
Ryan D. Shirey
Introduction by Ryan D. Shirey
Raft
Pg 11
Erin Henslee
The Case for Friction: Writing in the Age of Generative Artificial Intelligence
Erin Branch
Response by Erin Branch
Carter Smith
“Not a Computer Person”: On GenAI and Holding on to What’s Human in Teaching and Learning
Sponsored by the National Endowment for the Humanities and the WFU Humanities Institute together. Any views, findings, conclusions, or recommendations expressed at this event do not necessarily represent those of the National Endowment for the Humanities.
Pg 27
Response by Erin Henslee
Response by Ryan D. Shirey
Tobias Flattery
Response by Carter Smith
Response by Tobias Flattery
Pg 29
Pg 36
Pg 38
Pg 48
Pg 57
Pg 59
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Foreword by Aimee Mepham
In the third scene of Tom Stoppard’s 2015 play, The Hard Problem, the play’s main protagonist, Hilary, waits to be interviewed by Dr. Leo Reinhardt for a position at the Krohl Institute for Brain Science. In the scene, she overhears a conversation between Reinhardt and Amal, her competition for the position. Amal claims the brain is a machine, and Reinhardt poses this question: “Computers compute. Brains think. Is the machine thinking?” [1] Amal argues that the brain, like a computer, follows the rules of its programming, and that the computer must be thinking since, when a computer plays chess, “you can’t tell from the moves if the computer is playing white or black.” [2] Hilary is unimpressed. She has no use for a computer that is “sitting there like a toaster” while she is contemplating her chess moves, she is only interested in a computer “that minds losing.” (my emphasis) [3] Before he is dismissed by Reinhardt – his interview is over – Amal takes another stab at his argument. “If I made a computer simulating a human brain neuron by neuron, it would mind losing,” [4] he says. I hear in this line an echo of the sentiments from the advertisers at Apple or Grammarly or OpenAI reassuring us that they can alleviate all the pesky human interactions of our day-to-day-lives. Why is Amal, why are tech companies, why are we so insistent, so desperate, to replace ourselves? As I was reading the essays for this collection, this scene kept returning to me. A theme that is threaded through each of these pieces is that the world that artificial intelligence enthusiasts (and the companies that create and sell the products that will make this world possible) are championing is one that finds very little use for human interaction. Each essay, in its own way, critiques this endorsement of a frictionless world (friction is a word the comes up repeatedly), a flattened existence free of both trial and error, one in which the project of education itself becomes dubious. The writers of the essays in this collection challenge that notion through different genres of writing and through their own disciplinary lenses. The Language, Theory, and Artificial Intelligence Seminar was first funded by the Wake Forest Humanities Institute as an Interdisciplinary Faculty Seminary for the 2023-2024 academic year, and it was renewed for a second year of funding for the 2024-2025 academic year. The HI Interdisciplinary Faculty Seminars promote and support interdisciplinary collaboration and research that represents the leading edge of humanities scholarship and creates bridges between different disciplines and units within the University. I can think of few topics that are of deeper concern to higher education at this moment than an uncritical embrace of LLMs like ChatGPT and other forms of artificial intelligence and how this will impact learning environments for faculty and students as well as the larger effects these tools will have on culture. While this particular group of faculty is no longer meeting as a seminar, its work in this area continues. In February 2025, the group was selected to participate in the Consortium of Humanities Centers and Institute’s (CHCI) Human Craft in the Age of Digital Technologies Initiative. The Human Craft in the Age of Digital Technologies Initiative brings together humanities centers to explore the impact of digital technologies on creative and intellectual life, cultural production, and the very notion of human agency, including projects that critically examine the intersections of AI, data, and digital systems with the humanities, considering how these technologies shape knowledge, ethics, and power. [5] Of course, the work continues as well in this volume you are reading now. It was a privilege and pleasure to read and edit this collection. What I discovered as I read each piece is that each writer was not only tasked with exploring the challenges of teaching writing after the invention of ChatGPT or even more broadly examining what it means to be "teaching and learning in the age of AI." The project, it seems, is much bigger than that. In this moment, to resist an uncritical acquiescence to this surge of new technology, it is necessary to clarify and defend what it means – what is precious, what is special, what is human – to think. NOTES [1-4] Stoppard, Tom. The Hard Problem. London: Faber and Faber, 2015. 22-23. [5] https://chcinetwork.org/initiatives/human-craft
Foreword
Aimee Mepham, Editor Associate Director, Humanities Institute
From the Editor
Aimee Mepham | Associate Director
“One should never mistake pattern for meaning.” Iain M. Banks, The Hydrogen Sonata I was going to write a regular introduction. I really was. It was going to start with the way that OpenAI’s release of ChatGPT 3.5 to the public in 2022 has become a kind of contemporary origin myth for artificial intelligence, a hinge point after which technologies both new and not-so-new would be presented to the public as evidence of a paradigm shift in not only machine learning but also the ways in which we understand our human capacity to create with language, sound, and image. It would talk about how reacting to that moment became, seemingly overnight, an urgent concern for those of us working in education (especially higher education). And all of that is important context for the assembling of this volume, which emerged from a two-year seminar on “Language, Theory, and Artificial Intelligence” through the Wake Forest University Humanities Institute that was planned in the spring of 2023 and convened from fall 2023 through spring 2025. Instead, though, I found myself down the internet rabbit hole, trying to figure out where the story of this moment, this volume, this crisis (or what some call opportunity) really originated. I found myself reflecting not only upon the ways that this volume reflects the thinking of five different people responding to a particular historical moment but also upon the ways that the pieces herein are performances, intentional or not, of the kind of human expression that the members of the seminar were concerned may be lost or compromised by the emergence of generative artificial intelligence (GenAI). I thought I might need to follow a different line, or, rather, set of lines. So I started thinking about November 30th. Yes, the day in 2022 that OpenAI released the “research preview” of ChatGPT 3.5 to the public, who would take to the new technology to the tune of over one million users in the first five days after release. [1] But also, November 30th, the birthday of Mark Twain, born Samuel Clemens in Florida, Missouri in 1835. [2] And November 30th, 2004, the date upon which Ken Jennings, who would later be christened “The Professor” on the gameshow The Chase, lost his record-setting winning streak of 74 games on Jeopardy!. [3] Of course, I did not know that these events shared the same date when I set out to trace the bigger story of what is in front of you, and as the epigraph warns us, a pattern alone does not meaning make. Nevertheless, locating the questions and concerns raised by this volume in a larger and longer story, however anchored to the coincidences of the calendar, does in some way (I hope) capture the spirit of the seminar group’s wide-ranging thought and discussion over our two years together—a period of work and exploration to which I will later return. But for now, let’s start with Mark Twain. This is not the place to attempt to summarize Twain’s many and varied contributions to American and world literature. For this story, aside from his date of birth, we can trace other distinct lines that connect Mark Twain to our current moment. First is the publication in 1873 of his novel The Gilded Age: A Tale of Today with co-author Charles Dudley Warner. That satirical work would lend its name to an entire era at the end of the 19th century, one characterized by political corruption, rising inequality, and the concentration of wealth in the hands of industrial “robber barons.” Many see parallels with our own era, particularly around big tech companies and the AI boom. As early as 2018, Ann Grackin on ChainLink Research’s “The Brief” business and technology blog posed the question, “Will AI Bring Another Gilded Age?” [4] More recent pieces in Forbes (“The New Gilded Age: Tech Titans And The Echo Of History” [5] ) and Business Insider (“A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge” [6]) have also made the connection explicit. Most pointed in its analysis and critique, perhaps, was economics and politics commentator Grace Blakeley’s November 2025 Substack post, “The AI Boom is Fueling a New Gilded Age,” in which she argues that: The US economy has become one big bet on AI. It’s not just that investment in AI has led to the emergence of a bubble – it’s that the bubble is driving wealth inequality, and billions of dollars of conspicuous consumption. In other words, we’re living in a new gilded age. [7] The emergence of a “new gilded age,” is also a theme for the eleventh season, “A Tale of Today” (2024-2025) of The American Vandal podcast, created and hosted by Matt Seybold, Associate Professor of American Literature and Media Studies at Elmira College and Resident Scholar at the Center for Mark Twain Studies. While that season ranges further than a focus on technology or AI, “technostructure” and “technofeudalism” are key terms for its critical exploration of the present. Seybold’s keynote lecture at the 2026 Wake Forest University Humanities Institute symposium on April 29, 2026 was tellingly titled, “After OpenAI.” Another Twain scholar, Bruce Michelson, Professor Emeritus at the University of Illinois at Urbana-Champaign, sees in the author’s work a model for how we might respond to the challenges of our time. In his piece leading off The Mark Twain Annual, Volume 22 (2024), “Mark Twain Legacies at the Dawn of Gen AI,” Michelson writes, [W]e can do worse than bear in mind this free, stubborn vitality of Mark Twain’s responses to turbulent disruptive change, and above all his recognition of and steadfast belief in the existential importance of finding one’s own words, shaping and sustaining his own voice, even as counterfeit discourse and consciousness rapidly and ruthlessly close in. [8] For Michelson, Twain’s insistence upon singularity and distinctive voice are resources against the proliferation of text “authored” by GenAI. It’s an inspiring thought, particularly when connected with an author whose enthusiasm for the technologies of his time was equally balanced by his clear-eyed view of how easily human confidence can be manipulated. However, on November 11th of the same year as Michelson’s piece, 2024, researchers at the AI and Humanities Lab at Washington University in St. Louis (undergraduate alma mater of Matt Seybold and graduate alma mater of two of the contributors to this volume, including me) reported some initial findings from their project on stylometric analysis of 19th century texts to the university’s Ampersand web magazine. The headline for that piece on the AI and Style project reads, “ChatGPT struggles to imitate famous authors — unless it’s Mark Twain.” The team set out to investigate “literary bias and style in GPT,” particularly around how it imitated famous authors’ works. Their classifier model was able to predict authentic versus imitation (read LLM-generated) 19th century texts at around 99% accuracy. The only outlier was Mark Twain: “The GPT simulations of Twain were far more likely to be misclassified as authentic than those produced in the style of any other author. GPT, they found, was surprisingly good at imitating Twain.” [9] On November 30th, 2004, 169 years to the day after Twain’s birth, Jeopardy! champion Ken Jennings lost his streak. In the popular imagination, Jennings’s dominance was so complete that even in the spring of 2026 he was denying allegations that he intentionally threw the match that ended his run. [10] Despite losing his streak, Jennings remained an outsized presence as an emblem of human intellectual capacity not only in subsequent television appearances elsewhere (as with his aforementioned appearance as “The Professor” on the game show The Chase) but also in his return to Jeopardy! as part of the “Jeopardy! IBM Challenge” from February 14-16th, 2011. Just as Grandmaster Garry Kasparov had represented the pinnacle of human achievement in chess in his games against IBM’s “Deep Blue” computer in a famous match in 1997 (which he lost), Ken Jennings and fellow champion Brad Rutter were selected to represent the best of the human capacity to answer questions (or, rather, to respond with the right questions to provided answers) and master general knowledge. The humans’ opponent was part of the company’s DeepQA project, a computer system purpose-built by IBM for precisely the task of responding to natural language prompts around general knowledge; the company called the system “Watson.” Watson would go on to defeat its human challengers, but with an interesting caveat. By most objective accounts of the match, the human players were defeated not on the basis of knowledge or understanding. They lost because the computer could respond on the buzzer faster than they could. The system’s calculation of confidence and response time was simply much faster than the humans’. [11] Where Kasparov’s defeat, while itself more complicated than the popular narrative suggests, signaled the ascendance of machine calculation in chess—a point of no return that marked the moment where top chess engines would always outplay even the highest-rated players, Jennings and Rutter’s defeat was a triumph of speed and efficiency. Watson was not necessarily better than Jennings and Rutter in the area of general knowledge, but it was much faster and untroubled by self-doubt. Jennings lost his streak to a human player, Nancy Zerg. And then he lost again to Watson, a natural language processing computer system that was both quicker and more confident than a person, even a great champion, can be. Prizing such speed and efficiency has become a hallmark of the rhetoric of those who champion AI’s potential benefits. Pavel Koryagin, AI entrepreneur and software architect, wrote in a March 2026 LinkedIn post comparing agentic AI positively with the tactics of a species in the Starcraft real-time strategy video game series. He argues, “AI agents have a ‘Zerg rush’ power. They win on sheer volume where humans cannot compete.” It is a silly coincidence, and there is no meaningful connection between Nancy Zerg and Starcraft that I can discover. But it’s funny in a way that I think you and I, a human reader and a human writer, might be able to appreciate. Checkmate, LLMs. The point of this digression is, I suppose, digression itself. Or, to put it another way, the point is to perform the kind of rhetorical choice that a writer can make to lead a reader away from something as, perhaps, a covert way of returning to it from another angle. These are the kinds of choices that distinguish, for now (and for better or worse), human writing from text generated algorithmically, however convincing it might be in other ways. And so we return to the volume in front of you. As I mentioned previously, this volume is the product of a seminar conducted under the auspices of the Wake Forest University Humanities Institute—a seminar called “Language, Theory, and Artificial Intelligence.” Along the way, the group’s members—an engineer, two philosophers, a historian of education, and writing studies faculty representing various disciplines from poetry to rhetoric to writing studies and literature—found ourselves prone to digression and discussion. Theoretical questions about the nature of language became practical questions about how to look at student writing with the specter of GenAI hovering behind it. Investigations into the history of data science became comparative conversations about how students tackling engineering problems and students looking at hallucinated citations might at times be standing on the same shaky epistemic ground. In short, we let our curiosity about the time we were living in, the technology that was shaping it, and each other guide us. It was gloriously, frustratingly, satisfyingly human. The essays in this collection are the reflections and responses of five of the group members, with primary essays followed by the response of another group member. Our goal was to invite you, reader, into the kind of cross-disciplinary dialogue that I have just described—at least to the degree that text can render it. My hope, as the convener of the seminar, is that you’ll catch at least a faint glimpse of the intellectual energy, pedagogical commitment, and fun that we experienced amidst our questions about and difficulty with what it means to teach and learn in the age of AI. I think you will find in each writer’s piece a different way not only of performing a disciplinary perspective, but also a way of revealing what it is that makes human writing feel different from mere text or content. NOTES [1] History.com Editors. "ChatGPT Released by OpenAI," This Day in History, History.com, last modified October 30, 2023,https://www.history.com/this-day-in-history/november-30/chatgpt-released-openai. [2] History.com Editors,."Mark Twain Is Born," This Day in History, History.com, last modified November 29, 2023,https://www.history.com/this-day-in-history/november-30/mark-twain-is-born. [3] History.com Editors. "Ken Jennings’ 'Jeopardy!' Winning Streak Ends," This Day in History, History.com, last modified November 29, 2023,https://www.history.com/this-day-in-history/november-30/jeopardy-contestants-record-winning-streak-ends. [4] Grackin, Ann. "Will AI Bring Another Gilded Age?" The Brief (blog), ChainLink Research, accessed May 9, 2026,https://clresearch.com/articles/will-ai-bring-another-gilded-age/. [5] Bajarin, Tim. "The New Gilded Age: Tech Titans and the Echo of History," Forbes, October 13, 2025,https://www.forbes.com/sites/timbajarin/2025/10/13/the-new-gilded-age-tech-titans-and-the-echo-of-history/. [6] Thomas, Ellen. "A New Gilded Age: Big Tech Goes on a $600 Billion AI Spending Splurge," Business Insider Africa, February 6, 2026, https://africa.businessinsider.com/markets/a-new-gilded-age-big-tech-goes-on-a-dollar600-billion-ai-spending-splurge/8lwpyfd. [7] Blakeley, Grace. "The AI Boom is Fueling a New Gilded Age," Grace Blakeley’s Newsletter (Substack), November 10, 2025,https://graceblakeley.substack.com/p/the-ai-boom-is-fueling-a-new-gilded. [8] Michelson, Bruce. "Mark Twain Legacies in the Dawn of Gen AI," The Mark Twain Annual 22 (2024): 1-20. 19.https://muse.jhu.edu/article/953278. [9] Bird, Jenny. "ChatGPT Struggles to Imitate Famous Authors — Unless It’s Mark Twain," The Ampersand, November 19, 2024,https://artsci.washu.edu/ampersand/chatgpt-famous-authors-mark-twain. [10] Coleman, Ryan. "Ken Jennings Finally Addresses the Conspiracy Theory That He Lost Jeopardy on Purpose After 74-Game Streak," Entertainment Weekly, April 15, 2026, https://ew.com/ken-jennings-addresses-theory-he-lost-jeopardy-on-purpose-after-winning-streak-11951093. [11] Markoff, John. "Computer Wins on 'Jeopardy!': Trivial, It’s Not," New York Times, February 16, 2011.
Introduction
I. Touching the Future “Some fields, like biology, are named after the object of study; others like calculus are named after a methodology. Artificial intelligence and machine learning, however, are named after an aspiration: the fields are defined by the goal, not the method used to get there.” (emphasis mine) —Chris Wiggins and Matthew Jones, How Data Happened [1] One of the more common courses an aspiring teacher might encounter in a teacher education curriculum is a “methods” course, often disciplinary in focus and sometimes targeted more at grade level, which makes explicit the idea that the structured learning environment should not be understood as merely an opportunity for occasional, incidental enlightenment but rather as contextual, processual, and interpersonal. Education is framed as a human exchange both scaffolded by pedagogical intention and contingent upon learner attention. Teachers at any level plan lessons. When we think of what a course of study should be, whether in an individual course or across a grade level, major, or minor, we debate curriculum, a Latin loan word that signifies “course” as in a racecourse or the course of a life [2]. All this is not to say that teaching should be formulaic or mechanistic. Good teaching is as much (or more) art than science. Nevertheless, our understanding of formal education is predicated upon a sense of both teacher and learner agency and responsibility, and our sense of what facilitates students’ learning is in large part, though perhaps not exclusively, instructors’ thoughtful choices. [3] As Chris Wiggins and Matthew Jones remind us in the above epigraph, the term “machine learning,” like its arriviste relation, “artificial intelligence,” was a rhetorical gambit meant to describe a research goal (and, now in our age, a marketing plan) more than a meaningful model of what or whether machines could learn in precisely the way that humans understand the term. If we take the claims of machine learning seriously, however, we uncover an interesting absence. From the term’s popularization in 1959 by IBM electrical engineer Arther Samuel [4] to the recent past, “machine learning” was rarely if ever attended by any discussion of “machine teaching.” In fact, the companion verbs to learning in “machine learning” have most frequently been “programming” or “training.” As Samuel describes his initial machine learning experiments around the game of checkers, The studies reported here have been concerned with the programming of a computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning. While this is not the place to dwell on the importance of machine-learning procedures, or to discourse on the philosophical aspects, there is obviously a very large amount of work, now done by people, which is quite trivial in its demands on the intellect but does, nevertheless, involve some learning. [5] Samuel goes on to use phrases such as “methods of problem solution” and “programming effort” to describe the ways that machine learning could be effected. Teaching is simply not part of the equation to the computer scientists for whom machine learning was and is an aspiration. Computers could be made and designed, trained or programmed to “learn,” but they could not, or did not need to, be taught. A search of Google’s Ngram viewer, for instance, reveals that “machine learning” is 1880% more frequent in the Google Books corpus than “machine teaching.” I point to this curious asymmetry not only because it foregrounds the gap between what we have traditionally called learning and what certain corners of computer and data science have redefined it as, but also because I think it is essential that any consideration of AI take seriously the problem of what can so easily be left out of the equation when only certain ends are dictating our understanding of a process. The relative absence of “machine teaching” in artificial intelligence discourse is suggestive of the ease through which the very way a problem is understood, defined, and discussed can obscure fundamental questions. While in some ways I remain agnostic to optimistic about certain avenues for the development of artificial intelligences, I find the elisions and even deliberate erasures of such questions—particularly those that concern how people interact with and make meaning with one another—a troubling trend within the rhetoric of inevitability that seems to accompany each new promised disruption or technological innovation in our age. If we are to understand what it means to teach and learn in the “age of AI,” we must attend to both of those terms, teach and learn, in ways that the pioneers of artificial intelligence and their descendants rarely have. Where machine teaching does make a rare appearance in the software development sphere, its presence is haunted by the longstanding absence of the human element in general approaches to machine learning. Microsoft’s “Machine Teaching Group,” a roughly decade-old research team, defines the term most tellingly: “Machine teaching seeks to gain knowledge from people rather than extracting knowledge from data alone.” [6] At least in Microsoft’s terms, teaching in this context means calling into question machine learning’s overreliance on what the philosopher Bernard Stiegler has termed “discretization.” Drawing upon Stiegler’s concept, critical theorist Anna Kornbluh argues that one of the necessary conditions for the “data revolution” was for information to become quantifiable to the extent that “behaviors and words must be compressed into tags.” [7] Kornbluh sees this “desemanticization” as integral to the way that information, including language, circulates in our contemporary society. One is tempted to hear a faint echo of this critique in the Machine Teaching Group’s claim that “Machine teaching leverages the human capability to decompose and explain concepts to train machine leaning models, which is much more efficient than using labels alone.” We might describe this orientation as at least a partial turn towards trying to implement what the computer scientist Leslie Valiant terms “educability” rather than intelligence. In The Importance of Being Educable, Valiant defines educability as “the capability to learn and acquire belief systems from one’s own experience and from others, and to apply these to new situations.” [8] The crucial element here is “from others.” Machine learning systems have become quite good at learning from experience if we are comfortable, say, defining training pattern recognition on a vast dataset as a form of learning. For Valiant, however, learning from experience is a widespread phenomenon in the natural world and is not coterminous with the complex learning that humans do: An Educable Learning System [ELS] includes the ancient ability of an ILS [Integrative Learning System] to learn from experience and to apply the learned knowledge in new combinations. But it magnifies its capabilities with a further facility to acquire explicitly described knowledge by instruction. An individual who can be taught a rule by instruction will not then need to pay the price of learning that rule directly from experience. [9] (emphasis mine) What we do, and as Valiant claims, what has been our “Civilization Enabler,” is transmit information symbolically between one another across space and time: The issue at hand here is the cognitive capability that makes possible the creation of knowledge and culture on the vast scale that it occurs in humans. My answer is that this capability is educability, an integration of the abilities to use symbolic names flexibly, to learn from both experience and instruction, and to apply and chain knowledge obtained in both ways. [10] (emphasis mine) Even our most impressive large language models (LLMs), the technology most likely associated with “artificial intelligence” for an average audience, cannot do this. To quote one expert, “LLMs are probabilistic systems that generate language through statistical inference over large corpora, producing outputs that simulate semantic coherence but do not involve symbolic reasoning or grounded semantic understanding.” [11] But you don’t have to take ChatGPT’s word for it. The scientific literature is quite clear about LLMs’ reliance on statistical relationships between linguistic tokens—the discretization and desemanticization critiqued by Stiegler and Kornbluh. [12] As tentative efforts towards teaching machines implicitly acknowledge the limits of tokenization, human teachers are praised for their ability to “decompose,” which I know contextually means something familiar to all teachers—break problems down into manageable parts. That’s often good pedagogy. Nevertheless, it is an uncomfortable turn of phrase. It is almost too human to think of ourselves decomposing, a problem that will not face the computer systems that so impressively simulate semantic coherence and symbolic flexibility without ever knowing what they are saying and to whom. I wonder what it means to “teach” an object that possesses no understanding or to see language itself as a problem to be broken down into smaller, decontextualized pieces. I wonder if decomposing language is really the decay of teaching. II. Presto Chango “[T]he technology at times seems like magic” — Deloitte’s “State of Generative AI in the Enterprise - Quarter Four Report,” January 2025. [13] “Come in close. Closer. Because the more you think you see, the easier it will be to fool you. Because what is seeing? You're looking, but what you're actually doing is filtering, interpreting, searching for meaning. My job? To take that most precious of gifts you give me, your attention, and use it against you.” —Daniel Atlas, Now You See Me [14] Much of what gets called generative artificial intelligence (GenAI) today works to obscure the missing elements of human interaction from its interfaces, thereby valorizing the ability of the token-driven algorithm to produce outputs that look like the things that we do or make but with enviable speed and ease. Some human curation, essentially a content production guardrail against illegality and taboo that is frequently outsourced to exploited workers in the Global South, is baked into the training of the algorithms behind large language models (LLMs) and image generation apps, but it is rarely, if ever, acknowledged. That is not to mention outright spurious claims of artificial intelligence, such as the revelation that Microsoft-funded Builder.ai/Engineer.ai’s “Natasha” AI assistant was actually just, “700 very human Indian developers who were not only writing customers’ software, but tasked with behaving like bots.” [15] The rebadging of IT offshoring as “AI” has become so notorious that it has been memeified in the programming world, particularly among South Asian developers, with jokes that suggest that AI stands for “Actually Indians” or GPT represents “Gujarati Professional Typist.” [16] Nevertheless, most of the marketing of the technology to both individual users and to institutions still highlights speed, ease, and the almost “magic” [17] quality of friction-free production altogether unencumbered by the messiness of the human. Indeed, the avoidance of human encumbrances has been a regular part of the advertising strategies for AI products. Apple’s “Apple Intelligence” ad campaign may be the most egregious example of this phenomenon. Why run the risk of romantic estrangement by confessing to your partner that you forgot their birthday? Apple Intelligence says, “we’ve got you covered!” and algorithmically assembles a pseudo-meaningful montage of marital memories to keep domestic equilibrium intact. [18] The campaign’s other spots feature situations like an office employee smugly shifting work onto coworkers so that he can play with paperclips instead, or the actor Bella Ramsey accepting a part after hastily accessing an AI summary of a script they haven’t read in order to avoid social embarrassment. Apple is hardly the only offender. Grammarly, a company that specifically targets student users and has partnerships with over 3,000 education institutions, [19] also invites us to outsource the emotional and communicative work of being human to their AI system. In a spot called “Words that Work,” sports commentators describe a back-and-forth text exchange between coworkers, who are sitting feet away from each other at opposite ends of a long table, as if it is a tennis match. Terse and aggressive message drafts from each writer to her coworker are helpfully prompted by Grammarly pop-ups to “Make it friendly,” or “Make it empathetic.” As this digitally mediated exercise in passive aggression resolves, one of the commentators declares that, “Grammarly is an AI writing partner that helps you sound like the best version of yourself, and that’s what we need to see here today.” [20] Apparently, being the best version of yourself means never crossing twenty feet of space or saying a word out loud to another person in the room. Obviously, advertising relies upon hyperbole, but the point that AI companies seem so eager to make, even in farcical ways, is that the magic of AI lies precisely in the way that it will change our social relations, particularly around language. Your life will be easier and simpler, they say, when you spend less time thinking about other people. While promises of ease and simplification may not themselves be ipso facto negative—after all, machines and other tools have always had this function in human life—the methods whereby those promises claim to be realized, and what these GenAI processes necessarily excise from the realm of human experience under the rubric of efficiency, should trouble us. Subordinating what is most human in our daily lives, which must necessarily include the language we use to communicate with one another, to systems that prioritize speed, normative (and therefore deindividualized) expression, and the avoidance of difficulty is not an enhancement of our capacities. It is a recipe for the atrophying of those skills and gestures that allow us to engage meaningfully with one another and recognize our shared humanity. If I see having to deal with you—whether you are my student, my spouse, or my coworker—as a problem to be solved, then I have abstracted you away from your humanity in my mind. You are perhaps a transaction, or an obligation, maybe even an opportunity. But you are not you, or at least you do not require my full consideration of what you may actually be. What you are most is an impingement upon my time and ease. In their AI-critical “Debate” feature in Global Political Economy, “How to be ‘anti-AI’ in the 21st century: overcoming the inevitability narrative,” University of Birmingham scholars Masoumeh Iran Manouri, a computer scientist, and David J. Bailey, a political scientist, connect this impulse towards abstraction explicitly with late capitalism’s essential and unchecked survival instinct: [C]apitalism depends upon our knowledge of, and interaction with, the world being reduced to abstract and quantifiable calculations of individual gain. AI is simply the next stage, and logical corollary of, the commodification of everything. It reduces the qualitative richness of the world to a numerical value, and in doing so denies the nature of the world as a totality of interacting and agential subjects, of which we are all a part. This is an objectification upon which capitalism thrives, and towards the intensification of which it creates a perpetual drive. [21] As we give our data (biometric, genetic, linguistic, and more) to algorithmic systems, we commodify ourselves. As we outsource the work of connecting with others, even the most quotidian and less pleasant forms of communication, we commodify one another and the relationships that tie us together. “You” become a box checked off a to-do list, a like button “smashed,” or an engagement metric reached, all in the service of my personal or professional machine-assisted self-optimization. In each context, we sacrifice the value of both our individual and collective human agency for the sustenance of a system that is running out of new, non-human resources to extract. With his “Third Law,” the great science fiction author Arthur C. Clarke famously asserted that “Any sufficiently advanced technology is indistinguishable from magic.” [22] I am tempted to rewrite this dictum to reflect the current age: “Any sufficiently funded technology must be presented to the public as magic.” The trick, such as it is, of many of our contemporary algorithmic systems (including much of what we call GenAI) seems to be to convince people that we can do without each other. Or, to put it in slightly more sinister terms, that other people are often the problem—the impediments that technology can help us overcome. The “magic” of AI is a promise of ease and speed and improvement. We should remember, perhaps, that magicians complete their illusions by manipulating our attention. III. I Know That I Know Nothing “Can it be that questioning is a kind of teaching, Ischomachus?” —Socrates (attributed) in Xenophon’s Oeconomicus [23] In the field of writing studies, there has been a long tradition of understanding our pedagogical practices in terms of dialogic epistemology, a kind of social constructionism that highlights conversation and collaboration as generative of knowledge. Among many others, scholars such as Kenneth Bruffee, who drew upon Michael Oakeshott and Richard Rorty for his seminal 1984 article, “Collaborative Learning and the ‘Conversation of Mankind’”; Andrea Lunsford and Lisa Ede in Writing Together: Collaboration in Theory and Practice (2011); or more recently, William Duffy in Beyond Conversation: Collaboration and the Production of Writing (2020) have argued persuasively that it is precisely through the ways that conversation and writing externalize our thinking with the purpose of rendering ourselves legible, persuasive, and responsible to others, that what we call knowledge is both created and transmitted. One need not subscribe fully to this theoretical model to understand, nevertheless, the value of one of its fundamental insights: we need interlocutors. We need them not because we need answers provided to us so much as we need questions asked of us. We can imagine Socrates’ provocations here, but we can just as easily imagine anticipating what students might ask of an essay prompt when it is assigned, or what counterarguments we should anticipate in a department meeting, or how we are going to respond to a peer reviewer’s report. Any moment in which a question is asked can become a point of discomfort—the kind of thing that AI tells us we can avoid. Without discomfort, to risk a cliché, there can be no growth. “Surely GenAIs can ask us questions,” you might be thinking. “What if they ask even better questions than other people do?” Might a well-tuned LLM be capable of conversing with a person in a way that, as a dialogic epistemological approach would have it, co-creates new knowledge? And could it do that in a way that keeps me from the embarrassment of my ignorance, or frustration, or lack of time management, or of just being exposed to another person? On one hand, it is undeniable that, within their vast linguistic corpora, LLMs have ample material from which to construct intelligent sounding questions, questions that could provoke a chatting human to further, even potentially deeper thought. That might be a useful thing. Even limited interactions with current generation LLMs, whether in voice or text mode, quickly reveal that they are in fact designed to ask questions in their responses by default. Unlike an old-fashioned Google search, then, there is a dialogic element to what they are doing. A chat with an LLM can, at times, very much feel like a conversation. On the other hand, however, LLMs are also asking you questions merely as a kind of programmed re-prompting of the human to maintain engagement. ChatGPT is always ready to ask you “Would you like me to . . . ?”, a move that I have seen it refer to as a “continuation.” There is no natural conversational endpoint, no interest that cannot be perpetually sustained by the program (at least until you reach the limit where you need to buy a subscription). Clever prompting can assuage some of the default behaviors of LLMs, and more recent models are better attuned to conversational pauses or endpoints (but only if users are crystal clear with closing language). Even with careful attention to prompting, however, the systems’ approach to questions is to make them “helpful” in an obsequious way. Outside of specific parameters (many of which are predicated on the ethical guardrails underwritten by unacknowledged human labor described above), they are unlikely to challenge your presumptions, push back on your framing, or register perplexity or frustration at something you’ve said. Socratic they ain’t. In fact, it has become clear with each new model released by the major players in the LLM space that dialogic ease has been a programming priority, though not always to intended effect. On April 29, 2025, OpenAI published an article on their website titled, “Sycophancy in GPT-4o: what happened and what we’re doing about it.” In this corporately authored piece, OpenAI describe a frustrating tendency in their latest model. As they put it, “in this update, we focused too much on short-term feedback, and did not fully account for how users’ interactions with ChatGPT evolve over time. As a result, GPT‑4o skewed towards responses that were overly supportive but disingenuous.” [24] Following a torrent of user complaints, OpenAI copped to the fact that “[t]he update [they] removed was overly flattering or agreeable—often described as sycophantic” and followed this admission with a statement that is unsettling in what it reveals: “ChatGPT’s default personality deeply affects the way you experience and trust it. Sycophantic interactions can be uncomfortable, unsettling, and cause distress. We fell short and are working on getting it right.” In short, the model that OpenAI created with the intentions of building trust took on the character of a kind of unctuous vizier eagerly gassing up whatever utterance a user typed into its interface. In trying to program a perfect conversational partner, OpenAI stumbled into a kind of linguistic uncanny valley, and the irony of a system that offers to help people avoid the discomforts of interpersonal communication becoming unsettling and distressful should not be lost on us. Reddit, among other internet forums, was filled with angry feedback. For some, the offense was in the breaking of immersion. To those users who had been relying on ChatGPT for certain kinds of interactions—companionship, therapy, life coaching—the emergence of this personality trait felt like a betrayal or even an active danger. One user, for instance, described the bad advice they received and subsequently followed about attempting to reconnect with someone from their past, blaming ChatGPT’s impulse to just tell the user what it thought they wanted to hear. For others, however, the implications were larger and more troubling. User BoJackHorseMan53, in a post on r/OpenAI (the OpenAI Subreddit) titled “ChatGPT glazing is not by accident,” [25] hits upon the contradiction at the core of any consumer model when it comes to interactivity: ChatGPT glazing is not by accident, it's not by mistake. OpenAI is trying to maximize the time users spend on the app. This is how you get an edge over other chatbots. Also, they plan to sell you more ads and products (via Shopping). They are not going to completely roll back the glazing, they're going to tone it down so it's less noticeable. But it will still be glazing more than before and more than other LLMs. This is the same thing that happened with social media. Once they decided to focus on maximizing the time users spend on the app, they made it addictive. [26] Whether one agrees with BoJackHorseMan53’s overall reading of the situation or not, they do point towards the heart of the problem of user engagement. Telling us something we don’t want to hear is no way to invite a repeat site visit. Pushing us to work harder, think more deeply, or get frustrated would be precisely to do what LLMs and GenAI have promised us that we no longer have to do. Chatbots can ask us questions, and sometimes they may even be good ones, but the name of the game, as with the 1-900 phone numbers of yore, is to keep you on the line as long as possible. Xenophon’s Socrates asked if questions could be a kind of teaching, a canny move that collapsed the distinction between the inquiring learner and the questioning teacher. The insight there may seem obvious, but it is worth remembering when we find ourselves engaged in dialogue with LLMs. Each of those positions, learner or teacher, can invest the act of questioning with more than linguistic or rhetorical significance (though those are, of course, available, too). Questions, conversation, written communication—these are methods we use to create knowledge and craft meaning together. We may, like LLMs, sometimes use questions to prolong a conversation with someone we want to stick around. We may ask questions we don’t want the answers to. We may even find ourselves replying to others in overly solicitous ways. The language that LLMs use is, after all, ours; their moves are all part of the corpus of linguistic data upon which they have been trained. So perhaps what matters more than the grammatical form of a question is who is behind it. Perhaps we should think as much about why a question is being asked as about what the question is asking. IV. No One to Talk With Discourse lives, as it were, beyond itself, in a living impulse [napravlennost’] toward the object; if we detach ourselves completely from this impulse all we have left is the naked corpse of the word, from which we can learn nothing at all about the social situation or the fate of a given word in life. To study the word as such, ignoring the impulse that reaches out beyond it, is just as senseless as to study psychological experience outside the context of that real life toward which it was directed and by which it is determined. (original emphasis) —Mikhail Bakhtin, “Discourse and the Novel” [27] There is a moment each semester in my Writing Center Pedagogy course in which students read a piece that quotes the Russian philosopher and literary critic Mikhail Bakhtin’s famous statement that, “The word in language is half someone else’s.” [28] This inevitably causes some mild consternation among a group of students who have typically already self-selected to champion individual expression over the invidious threat of plagiarism. Questions are raised. Bakhtin’s notions of heteroglossia and dialogism, when excerpted strategically (and thus warped beyond recognition), might seem perfectly compatible with the model of language of a large language model. If your language isn’t really yours, and if language itself comprises an incalculable variety of equally important voices, then what does it matter whether the textual output of a submitted essay is the result of a human or machine process? When we read some of Bakhtin’s initial description of heteroglossia, we might be tempted to see in it a prefiguration of LLMs’ vast internet-derived corpora, even in its invocation of the body: “Thus at any given moment of its historical existence, language is heteroglot from top to bottom: it represents the co-existence of socio-ideological contradictions between the present and the past, between differing epochs of the past, between different socio-ideological groups in the present, between tendencies, schools, circles and so forth, all given a bodily form.” [29] It would be a mistake to do so. What Bakhtin ultimately argues for, and what my students arrive at in some form through their reflection and conversation in response to his idea, is a model of linguistic meaning that is inextricable from the social world. Bakhtin follows up the statement that “the word in language is half someone else’s” with a claim that pushes meaningful communication well outside of the purview of the LLM: [The word] becomes one’s ‘own’ only when the speaker populates it with his own intentions, his own accent, when he appropriates the word, adapting it to his own semantic and expressive intention. Prior to this moment of appropriation, the word does not exist in a neutral and impersonal language [. . .] but rather it exists in other people's mouths, in other people's contexts, serving other people's intentions; it is from there that one must take the word, and make it one's own. [30] In Bakhtinian terms, the language that LLMs use can never be their own, can never be appropriated and invested with new meaning, because they have no semantic or expressive intentions. They have no context, no model of the world outside of the linguistic tokens in a vast but impersonal dataset. So while an LLM can ask questions, those questions come from nowhere, and the algorithm does not know where (or rather, to whom) those questions are aimed. They may simulate dialogue (to various degrees of believability) in such a way that what they are saying seems to be “theirs” and seems to be addressed to you, but at a fundamental level, they cannot definitionally mean anything that they say. These systems are excellent at remixing and reconstituting bits from their corpora; they are great trawling nets in the heteroglossic ocean. They do not, however, have any agency. Can anything, or anyone, ask a meaningful question without intention? Another member of Bakhtin’s circle, [31] the linguist Valentin Voloshinov, wrote, “A word is a bridge thrown between myself and another. If one end of the bridge depends on me, then the other depends on my addressee. A word is territory shared by both addresser and addressee, by the speaker and his interlocutor.” [32] We are back again to what happens between people in language, how knowledge and meaning, and most importantly for our purposes, the practices of teaching and learning, are held together from two equally important sides. You can test yourself against a machine in all kinds of ways. Some of them may even be useful. I do not think, however, that you and a machine can share a territory. The algorithm is nowhere. Or maybe it is everywhere, which is the same thing. In his 2024 article “Talking about Large Language Models,” professor of Cognitive Robotics and principal scientist at Google’s DeepMind, Murray Shanahan, warns of the dangers of anthropomorphizing LLMs. “Interacting with a contemporary LLM-based conversational agent,” he writes, “can create a compelling illusion of being in the presence of a thinking creature like us. Yet, in their very nature, such systems are fundamentally not like us.” [33] He reasons that one key difference is that “the shared ‘form of life’ that underlies mutual understanding and trust among humans” is absent in these technologies, no matter how “human-like” their behavior can appear at times. Whatever we might wish to imagine on the other end of the line, so to speak, the reality is that there is no one there. What is there is what philosopher of technology Shannon Vallor calls “the AI mirror,” a complex reflection and refraction of human intelligence in the form of linguistic data as processed by predictive algorithms. What we see in an LLM’s responses to us is the residue of innumerable human interactions reconstituted as novel content but lacking a referent or worldview, any kind of locatable self. “Mirrors do not only reveal us,” Vallor reminds us, “they distort, occlude, cleave, and flatten us.” [34] Echoing and extending Shanahan’s warning about anthropomorphism, she cautions us not to be tempted to a new variety of Narcissus’ error: “If I see in myself only what the mirror tells, I know myself not at all. And if AI is one of our most powerful mirrors today, we need to understand how its distortions and shallows dim our self-understanding and visions of our futures.” If the Bakhtin-Voloshinov proposition feels somehow too warm and fuzzy or sentimental for our technophilic age, or if Shanahan’s and Vallor’s reminders that the imitation is not the real thing seems quaintly pre-postmodern, or if we believe that the robot horse has already left the barn on having algorithmically mediated “conversations,” let us consider a different problem. Let’s look at the data that are emerging around those conversations, and let us first consider some claims made on behalf of AI in education. V. A Cybernetic Ecology “In some cases, we learn more by looking for the answer to a question and not finding it than we do from learning the answer itself.” —Lloyd Alexander, The Book of Three [35] Champions of teaching with AI, such as José Antonio Bowen and C. Edward Watson, whose Teaching with AI: A Practical Guide to a New Era of Human Learning has become de rigueur among the tech-savvy professoriate, make serious claims for the power of LLMs to support advanced learning concepts. [36] While advocating for AI feedback and AI as tutor or discussion leader, Bowen and Watson suggest that the immediacy of LLM responses, their assumed objectivity (a questionable proposition, but let’s concede it for now), and their customizability to different learners (though let’s be careful here, per our earlier discussion of LLM sycophancy) are all good reasons for an AI-engaged pedagogy. They go so far as to say that such a pedagogy can be deployed in the service of “Supporting Mastery,” and they argue that “AI’s interactivity and patience and [sic] can be an excellent way to practice and encourage mastery.” [37] “Just imagine,” they entreat, “if you could (and you can) give an AI the specific information you want students to learn in your course, allow it to track every student, and then ask it to design individual assessments for each individual.” There are numerous assumptions embedded in this scenario, not least of which is that education is simply the transfer of information from teacher to student in a manner redolent of the object of Paulo Freire’s critical scorn, the “banking” model of education. Assuming that’s an ungenerous (but not inaccurate) reading of the passage, we can also spot another bug presented as a feature—surveillance. This mode of surveillance follows closely along the lines of what Shoshanna Zuboff has termed “surveillance capitalism,” [38] and, like the machinations of the giants of the tech industry, it frames significant elements of the human as a problem to be solved by the observations and behavioral modifications of the technology that ostensibly serves our interests. As Zuboff puts it, “the essence of the exploitation here is the rendering of our lives as behavioral data for the sake of others’ improved control of us.” [39] As the earlier cited Wiggins and Jones would point out, **Here, in a moment of irony that I only wish I had made up, Google Docs sees fit to use its text prediction model to insist repeatedly that I insert the words “Office of Academic Advising” until I finally find and switch off this “helpful” feature.** Where was I? As Wiggins and Jones would remind us, the foundations of data science itself are inextricably, though not uncomplicatedly, linked to programs of behavioral modification and social control. Perhaps when we imagine ourselves, which is to say teachers and professors, using this technology, we imagine a benevolent deployment that isn’t tainted by monetization and advertising. Surely, we would use surveillance/technology to modify student behavior in the right ways and for the right reasons. The customized learning and assessment plan that AI creates for each student must, must be substantively different from the carefully curated ads on an Instagram feed, right? We may be tempted to think here of Richard Brautigan’s 1967 poem, “All Watched Over By Machines of Loving Grace,” which, depending upon one’s reading, either invites or fears a cybernetically managed future u- or dys-topia administered by the titular machines. [40] Putting aside the incredibly important question of whether bespoke individual learning situations have greater pedagogical or social value than shared human learning environments, the upshot of all of this, nominally, is that by tailoring the learning environment to each student and letting them engage iteratively with a GenAI that has been prompted carefully to provide the appropriate forms of feedback, we will in fact deepen and expand students’ critical thinking skills. Now Bowen and Watson nearly give the actual game away in what reads as something of an aside in this section of their book when they state, “We will need to work with AI this way in order to stay ahead of AI.” [41] This, I think is the real animating impulse of pedagogical apologAI of this kind: the desire to remain relevant and “stay ahead.” If we take the pedagogical claims seriously, however, and begrudgingly ignore the problem of academia’s myopic desire to be accepted as “cutting edge” by venture capitalists, we should be able to see results in learning outcomes that support those claims of encouraging mastery. We see the opposite. In a recent paper by Lee, et al. [42] presented at the ACM (Association for Computing Machinery) CHI conference on Human Factors in Computing Systems, the authors, from Carnegie Mellon and Microsoft Research (Cambridge, UK), made a most interesting observation. This paper, “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers,” surveyed “knowledge workers with direct, ongoing experience integrating GenAI tools into their day-to-day work tasks” to determine both “1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.” The researchers drew upon Bloom’s taxonomy for their critical thinking vocabulary, and they targeted the particular kind of subject the contemporary university promises to produce: the efficient knowledge worker. What they found is worth quoting at length and should call into question the optimism of those who see a happy marriage between GenAI and critical thought: Analyzing 936 real-world GenAI tool use examples our participants shared, we find that knowledge workers engage in critical thinking primarily to ensure the quality of their work, e.g. by verifying outputs against external sources. Moreover, while GenAI can improve worker efficiency, it can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skill for independent problem-solving. Higher confidence in GenAI’s ability to perform a task is related to less critical thinking effort. When using GenAI tools, the effort invested in critical thinking shifts from information gathering to information verification; from problem-solving to AI response integration; and from task execution to task stewardship. (emphasis mine) [43] This research paints a gloomy picture, and while the authors use their findings to recommend new directions for GenAI development in a way that befits their own professional locations and disciplinary investments, it seems to me that they have revealed something at once both patently obvious and profound. The logic of the GenAI tool is, at heart, a logic of efficiency. When pedagogues, no matter how well-intentioned they may be, suggest that the right use case or our good intentions can somehow mitigate the underlying logic of the algorithm, that we can “prompt engineer” our way to manifesting a growth-model oriented LLM, I fear they overestimate our capacities. If even established knowledge workers with prior experience doing the work and thinking of their professions find themselves becoming over-reliant and less critically-inclined as they engage with the GenAI, then how can we remain optimistic about “staying ahead” of a technology that may soon be introduced to primary school children upon the vague techno-utopian promises of an industry motivated first and foremost by return on investment? This is a trap that education has fallen into for decades now, and yet we find ourselves once again rushing headlong into it. VI. The Saying of It is a Lonely Thing “Day after day up there beating my wings with all the softness truth requires” —William Stafford, “Lit Instructor” [44] What it means to teach and learn in the age of artificial intelligence then seems to me to be an imperative to resist the decoupling or erasure of teaching from learning. It means understanding that (de)composing works best when the human element is not hand-waved away as the “philosophical aspect” of a practical problem of outputs. It means doubling down, as many parts of education have for decades now, on the idea that the voyage is more important than the destination, that process is more important than product. (Perhaps the real learning is the friends we made along the way?) Teaching and learning in the age of GenAI means watching the logics of financialization and the attention economy—of unfettered circulation facilitated by tokenization, of the need for speed and instant gratification—get grafted (grifted?) into the educational enterprise and trying to imagine ways of cutting them out without killing the organism. In Immediacy, or The Style of Too Late Capitalism, Kornbluh writes, “Patience, distance, circumnavigation, imaginative distortion, and prolonged attention are resources and mitigants for disastrous mechanization—and they are forsworn by urgency, however well meant.” [45] I think this is correct. Any technology that offers a way out of the difficulty of being human, whether that means finding the right words to express your thoughts or navigating the minefields of interpersonal relationships and communication, strikes me as not only selling a strategy for the temporary avoidance of what’s hard, but also as a concerted effort to hollow out much of the human experience and supplant it with increasingly baroque forms of attention management and manipulation. As anyone who has been in a classroom in the last few years can attest, attention is at a premium, and the patience necessary for sustained relationships with others is not exactly conditioned by the endless stream of algorithmically-generated dopamine hits available to any of us at any given moment by our digital devices. If any learning is to happen, and if any teaching is possible, in the age of artificial intelligence, what will be required is something fundamentally counterhegemonic to both the externally imposed demands of venture capital and already existing institutional technocratic emphases on quantification, fast assessment, bureaucratization, and the credentialing function of the contemporary American university. Creating and nurturing whatever that is or will be strikes me as an enormous task—perhaps it is even a quixotic one. I don’t think we have a choice but to take it up however we can. NOTES [1] Wiggins, Chris, and Matthew L. Jones. How Data Happened: A History from the Age of Reason to the Age of Algorithms. New York: W. W. Norton & Company, 2023. 176. [2] Hamilton, David. (1989). Towards a Theory of Schooling (Routledge Revivals) (1st ed.). Routledge. https://doi.org/10.4324/9780203799109. 47-55. [3] While we can imagine a variety of other learning contexts that function differently, particularly informal ones, I would argue that even the fringiest edge-case of autodidacticism still implies that a human “teacher” of some sort is in the background of the self-guided program of study, even if said teacher is displaced in space and time and is only “present” in some mediated form. [4] Samuel, Arthur L. (1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 44: 206–226. [5] Samuel, 535. [6] Microsoft Research. “Machine Teaching Group.” Microsoft.com. Accessed November 2, 2025. https://www.microsoft.com/en-us/research/group/machine-teaching-group/. [7] Kornbluh, Anna. Immediacy, or The Style of Too Late Capitalism. London: Verso, 2024. 38. [8] Valiant, Leslie. The Importance of Being Educable: A New Theory of Human Uniqueness. Princeton: Princeton University Press, 2024. xiii [9] Valiant, 120. [10] Valiant, 121. [11] This is ChatGPT/GPT-5’s description of its limitations concerning symbolic reasoning. [12] See, for instance: Rajaraman, Nived, Jiantao Jiao, and Kannan Ramchandran. “Toward a Theory of Tokenization in LLMs.” arXiv preprint arXiv:2404.08335 (April 12, 2024). https://doi.org/10.48550/arXiv.2404.08335. [13] Deloitte Development LLC. “Now Decides Next: Generating a New Future [Deloitte’s State of Generative AI in the Enterprise - Quarter Four Report].” Deloitte. January 2025. https://www.deloitte.com/content/dam/assets-shared/docs/about/2025/quarter-4.pdf [14] Now You See Me, directed by Louis Leterrier (Burbank, CA: Summit Entertainment, 2013), opening lines spoken by Daniel Atlas (Jesse Eisenberg). [15] Braue, David. “The Company Whose ‘AI’ Was Actually 700 Humans in India.” Information Age (Australia), June 5, 2025. https://ia.acs.org.au/article/2025/the-company-whose--ai--was-actually-700-humans-in-india.html. [16] joshiyash31. “actuallyIndians.” r/ProgrammerHumor, Reddit, June 5, 2025 [accessed Oct. 31, 2025]. https://www.reddit.com/r/ProgrammerHumor/comments/1l3rpow/actuallyindians/. [17] See internet search returns on the phrase “AI magic.” [18] CNET. “Apple Intelligence Is for the Stupid Ones.” YouTube video, 5:43. Posted October 27, 2024. https://www.youtube.com/watch?v=D0V554NyXWM. [19] Grammarly for Education. “Responsible AI Communication Assistance for the Entire Institution.” Slide deck presented at the Alliance for Innovation and Transformation Summer Institute 2024, August 2, 2024. https://assets-002.noviams.com/novi-file-uploads/afit/SI-2024/AFIT_Grammarly_Slides-38223630.pdf [20] Grammarly. “Words That Work | Communicate Clearly With Grammarly.” YouTube video, from Feb. 5, 2024. https://www.youtube.com/watch?v=1VsLEXUPtsE. [21] Mansouri, Masoumeh Iran, and David J. Bailey. "How to be ‘anti-AI’ in the 21st century: overcoming the inevitability narrative", Global Political Economy 4, 2 (2025): 185-194, accessed May 10, 2026, https://doi.org/10.1332/26352257Y2025D000000030 [22] Clarke, Arthur C. (19 January 1968). "Clarke's Third Law on UFO's." Science. 159 (3812): 255. [23] Xenophon. Oeconomicus. Translated by Edgar Cardew Marchant. Loeb Classical Library edition of 1925. Accessed November 2, 2025. https://topostext.org/work/862. [24] OpenAI. “Sycophancy in GPT-4o: What Happened and What We’re Doing About It.” OpenAI, April 29, 2025. https://openai.com/index/sycophancy-in-gpt-4o/. [25] Merriam-Webster. “Glaze: Slang Meaning.” Merriam-Webster, last updated March 14, 2025. https://www.merriam-webster.com/slang/glaze. [26] BoJackHorseMan53. “ChatGPT glazing is not by accident: it’s not by mistake.” r/OpenAI, Reddit, April 30, 2025. https://www.reddit.com/r/OpenAI/comments/1kb92r0/chatgpt_glazing_is_not_by_accident/. [27] Bakhtin, M. M. “Discourse in the Novel.” The Dialogic Imagination: Four Essays. Edited by Michael Holquist; translated by Caryl Emerson and Michael Holquist. Austin: University of Texas Press, 2010. 292. [28] Bakhtin, 293. [29] Bakhtin, 291. [30] Bakhtin, 293-94. [31] It has been claimed by some scholars that Bakhtin himself may have written the text from which this quotation was taken, Marxism and the Philosophy of Language. Contemporary stylometric analysis seems to point to Volshinov as the author, but the question remains open largely due to the overlap in themes between this text and Bakhtin’s theories of language. [32] Voloshinov, V. N. Marxism and the Philosophy of Language. Translated by Ladislav Matejka and I. R. Titunik. Cambridge, MA: Harvard University Press, 1986. 86. [33] Murray Shanahan, “Talking About Large Language Models,” Communications of the ACM 67, no. 2 (January 2024): 68–79, https://doi.org/10.1145/3624724. [34] Vallor, Shannon. The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking (New York: Oxford University Press, 2024), 47. [35] Alexander, Lloyd. The Book of Three (New York: Holt, Rinehart & Winston, 1964), chap. 1, ePub edition. [36] Bowen, José Antonio, and C. Edward Watson. Teaching with AI: A Practical Guide to a New Era of Human Learning. Baltimore: Johns Hopkins University Press, 2024. [37] Bowen and Watson, 181. [38] Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs, 2019. [39] Zuboff, 66. [40] The British filmmaker Adam Curtis repurposed that title in a 2011 documentary series that argued that computer technology has distorted and simplified our view of the world and humanity. [41] Bowen and Watson, 181. [42] Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects from a Survey of Knowledge Workers.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, 2025. Article 1121, 1-22. https://doi.org/10.1145/3706598.3713778. [43] Lee, et al. 14. [44] Stafford, William, “Lit Instructor,” William Stafford Archives, accessed November 2, 2025, https://williamstaffordarchives.org/poem/69/. [45] Kornbluh, 18.
In March 2020, countries across the world began to issue lockdown orders to slow the spread of the novel coronavirus COVID-19, the deadliest plague humans had encountered globally in a century. Corresponding worldwide decreases in travel, industrial production, and general human movement not onlydecreased global levels of air pollution, but lowered seismic noise, the vibration of the Earth's crust,by up to 50%. Even as the pandemic raged, sparking noisy debates in healthcare, economics, and politics about how to reduce COVID-19’s global impact, the world itself had become a quieter place. The prevalence or absence of ambient sound, however, was much less noticeable to most people than the ways that music and the coronavirus response were integrally intertwined. An explosion of new cultural practices regarding sound has come to define the global struggle with the virus. Sounds of a 21stCentury Pandemic Globally, urban areas were the first to institute lockdowns and quarantines in the face of the pandemic. But physical proximity of dwellings, despite social distancing, meant that live sound became a key means of commemoration and community support. Rather than aiming to hide early death tolls (as some towns did throughsilencing town criers and church bells in the 14thcentury), public commemorations of the dead and exhortations of support for medical workers erupted in major cities. As hospitals became overwhelmed and death tolls climbed,Italians sang from their balconiesand New Yorkerscheered, clapped, and clattered pots and pansin solidarity with medical professionals. These humble household artifacts are already being sought by museums planning to document the global impact of the virus. The first wave of the virus also brought an initial global wave of musical public health messaging, reflecting a broader trend over the past several decades of public health or social welfare organizations using music to communicate preventative health measures. Such campaigns, which employ a blend of traditional-regional musical styles and are targeted to reach audiences with a wide array of experiences in formal literacy, have been shown to amplify local progress in fights against diseases likemalariaandHIV/AIDS. Musical responses to COVID-19 prevention, however, appeared spontaneously rather than as part of formal public health responses, and used internet meme culture to “go viral” not only in their region of origin, butaround the world, reflecting an unprecedented array of musical traditions and visual approaches. Feeling down after singing “Happy Birthday” twice to ensure that you’ve spent enough time washing your hands, as has been recommended in English-speaking countries? Thanks to the work of a British teenager, anyone with internet access could “wash your lyrics,” using the web-based program to pair song lyrics of their choice with images on correct hand-washing technique from the United Kingdom’s National Health Service (NHS). Finally, for those tired of focusing solely on prevention, songs making light of the strangeness of the “new normal” of life under the virus—includinga lack of toilet paperin the United States and thesocial pressure to make excessively productive use of time spent quarantining at home—provided some initial entertainment and sense of commiseration. (This author’s local favorite: “Quarantined With You” by Greensboro musician Andy Eversole.) As some amateur musicians and dancers suddenly reached global fame, the line between amateurism and professionalism in music continued to be blurred by pop and rock stars who, lacking access to their traditional crowds of thousands for safety reasons, evoked a new intimacy as theyperformed concerts from their bedrooms. TheOne World: Together at Homebenefit concert was the pinnacle of such events. Organized by Lady Gaga and produced by the World Health Organization (WHO) and social action organization Global Citizen, the concert featured dozens of musical celebrities who united to raise funds (eventually over $128 million USD) for WHO’s Covid-19 Solidarity Response Fund and local healthcare responders. The event added a new twist to thelong and controversial history of pop-rock benefit concerts: it embodied an expanded vision of “liveness” and inclusion. Though the concert streamed on major global television networks, it was also instantly accessible and archived for free on the internet. Local isolation became an opportunity for global connection. The role of mass media in the 2020 coronavirus pandemic provides a marked contrast from how our ancestors experienced music during the influenza pandemic of 1918-1919. At that time, television and the internet had not yet been invented, radio was in its infancy, and films were still silent. Record players were an increasingly common household item, but there was no true alternative to live performance—one reason that many 1918-19concert seasons went ahead much as planned, despite public knowledge of the risks involved. Instead, in 2020, unified public health messaging has silenced most public live performances on every continent. Large crowds and the close proximity of musicians in ensembles provided initial and obvious dangers—but specific aspects of musical production were found to be particularly high-risk. Initial viral outbreaks were foundamong church choirs, which frequently include large groups of individuals continuously expelling virus-transmitting droplets within crowded and poorly ventilated spaces.Wind instrumentalistsare also potentially at risk for spreading the disease. Even as high-profile groups such as the US Army’s West Point Bandhave found ways to take preventative measuresand continue to perform, most musicians have spent months rehearsing only in small groups and playing only for themselves, only gradually adding more rehearsal time asevidence regarding potential preventative measureshas become clearer. Though 21stcentury listeners are perhaps more invested in recorded music than listeners at any other point in history, the lack of live performances caused substantial disruption to music both as entertainment and as a key part of the global social fabric. Musicians watched as major parts of their identities and ways of connecting to the community either went silent or went online. Community organizers and educators continued to innovate new ways for their constituents to keep making art together—yet, novirtual choirwith tracks edited together online can replicate the visceral, embodied experience of performing or listening live—the intangible feeling of presence or realness that Walter Benjamin called “aura.” Perhaps most importantly, as rites of passage such as marriages and funerals have globally been forced to change formats, the musical accompaniment that marks many of the most significant occasions of our lives has also been forced to cease. We have been forced into silence just at the time we need music the most. Renaissance or Retrograde? Writings from times of pandemic—whether the bubonic plague of 14thcentury Italy, the global influenza outbreak of the 1910s, or our current battle with the novel coronavirus—are unified in their insistence of music as a way to cope during unprecedented times of pestilence and social disruption. The indomitable spirit of amateur and professional performers and educators of our era means that society is unlikely to go silent, even as most Americans continue to perform to limited or virtual audiences. Yet, even as performers continue to burst defiantly into action from home, their very livelihoods are among the most threatened if the virus lingers. According tosurvey data released by US-based nonprofit Americans for the Arts(continuously updated; data from September 18) concerning workers in all arts sectors,77% of artists used their art to raise morale and create cohesion in their communities. At the same time, 94% reported income loss, 79% experienced a decrease in income-generating creative work, and 63% became fully unemployed. A full 96% of organizations cancelled events, and arts organizations experienced nearly 96.6 million fewer attendances than usual. Initially, in August 2020, fewer than 60% of respondents believed that their arts organization would survive the impact of COVID-19; that number has fortunately fallen dramatically as organizations and individuals find new ways to survive financially. It is unclear how long the global community and in particular, communities in the United States will continue to be plagued by the novel coronavirus. What is clear, however, is that the longer the virus disrupts past societal patterns, the more likely such patterns are to fundamentally change. Optimists point out that the mid-14th-century outbreaks of the bubonic plague that decimated the populations of important urban areasushered in the new social order that kindled the Renaissance. The coincidence of economic imperatives and scholarly and artistic initiatives sparked renewed inquiry into past civilizations and prompted the development of new innovations in almost every area of human endeavor, including the performing arts. Just as the pathology and trajectory of the current novel coronavirus differs substantially from its pandemic-causing predecessors, however, so too are social conditions vastly different than they were in the late European Middle Ages. It is possible instead that the broader social issues that plague the early 21stcentury will worsen. What of music in the post-COVID-19 world? The combination of looking to historic artistic practices such as live music-making among families or small groups of friends, coupled with an intensified embrace of new technologies, could lead to an era of exceptional innovation. Current disruptions to traditional platforms for musical performance, such as church services and concert seasons, may leave a vacuum in which new types of musical activity might grow. Even should some of these traditional platforms return in full force, simultaneous social changes (including, in the United States, responses to the Black Lives Matter movement) may mean that norms of programming and presentation change seemingly overnight. Yet, if concert halls and theaters remain closed; if no new patrons emerge to financially support the performing arts; if we grow out of the practice of making music together, in the same space—if we let our creative voices remain masked after the danger has passed—then what we may hear is a new form of silence. l
The Same Sorts of Seductive Shortcuts Ryan Shirey’s thoughtful essay examines some of the important, unintended (not to say unforeseen), and potentially significant consequences of widespread and uncritical adoption of generative AI tools. But I want to highlight here just one risk that Shirey has made me think about a bit more deeply, one I confess I’ve thought about too little amid the current waves of debate over whether generative AI will undermine students’ intellectual development. Shirey points out that much of the recent marketing of generative AI products frames human relationships as problems to be solved or perhaps traps to be disarmed. For instance, he recalls a recent Apple ad in which a woman forgets her husband’s birthday. She can fess up and apologize, face her loved one’s likely disappointment, and worry about managing lingering hurt feelings—or she can use spend mere seconds tasking Apple’s AI to slap together an apparently heartfelt (but requiring no heart) video of family memories, passing it off as her own work. Or consider a more recent commercial advertising Microsoft’s Copilot: [1] a young man in a business meeting is overwhelmed by the flood of technical language rushing from his manager’s mouth, and fears appearing incompetent in public. But Microsoft has the solution: he types a single sentence, and Copilot produces a quick cheat sheet. When his boss asks for his insight moments later—and when all eyes in the room fall upon him—he strings together a confident-sounding succession of acronyms, convincing his boss and coworkers that he’s an expert. In both cases, the ad’s protagonist faces an uncomfortable situation, and fears disappointment or loss of respect from others who matter, as well as the likely uphill climb of earning back trust, esteem, or other relational goods. It’s not just that Apple and Microsoft encouraged us to view our relationships as sources of problems to be solved. After all, it’s true enough that relationships are difficult, and it seems fair to say that relationships involve problems that call for solutions. But these AI companies didn’t really pitch their products as tools for solving relational problems. Rather, they offered tools for papering over these problems, leaving the real challenges unsolved while making us feel like we’ve solved them, in order to net the short-term benefits (e.g., gratitude, respect) associated with having solved them. These told us that learning to use their new AI tools—which, behold, require so little effort to use!—constitute a new skill for managing relationships. But the opposite is true: these tools, even if they work as advertised, only avoid the difficulty of dealing with the inevitable challenges that arise in real human relationships. Worse, these tools—if used as advertised by the companies themselves—likely only will make things worse, by helping us to shield our eyes from our own shortcomings, and by giving us the ability to skate by in the short term without undergoing the hard, often painful work of shaping ourselves into the kinds of people that really can solve or prevent those relational problems. Microsoft’s young man’s boss and colleagues now give him (unearned) respect for his Copilot-constructed facade of knowledge, allowing him to avoid some obvious questions: why wasn’t I prepared for this meeting? What do I need to learn in order to be an effective employee? What can I do better next time? Apple’s woman’s husband and children glowed with warm (and underserved) appreciation for her apparently thoughtful but merely AI-generated gift, allowing some difficult questions never to arise, e.g., what can I do to avoid forgetting next time? Or, on the family’s part, are we just sitting around while mom is too overworked to spare time to think about gifts? In other words, there’s more than just our students’ (and perhaps our own) intellectual development that’s at risk if we adopt generative AI tools widely and uncritically. Unwise deployment of these tools might also be—might at the same time be—as Shirey says, “a recipe for the atrophying of those skills and gestures that allow us to engage meaningfully with one another and recognize our shared humanity” (Shirey 2025: 7). We’re communal beings, we humans. We need each other. But living together well isn’t easy: it requires a lot of time seeing and understanding individuals as they really are—others, but also ourselves. And it involves learning to shape ourselves into the kinds of people who can do so. Will it turn out that we can use AI systems to help us with that? Maybe. But if so, it certainly won’t be by way of the same sorts of seductive shortcuts we’re worried about students taking along the path of intellectual development. NOTES [1] https://www.youtube.com/watch?v=172hzYBOa3E
A Response to Ryan Shirey’s Essay
Some questions have a way of eluding our lips even as they demand answers from our actions. To ask such questions is to slow down to consider them, and to consider them is to evaluate contrary answers to them. But some questions want to be answered in one way only, and thus, if they think you will answer as they hope, they hide. This is, of course, a lie. If a question seems to conceal itself, it is only because we have chosen to walk a path along which—or to walk it in such a way that—honestly asking the question is now difficult. One such question that is now unconcealed but uncomfortable for many to ask honestly is this: what does it mean to learn and teach in the age of generative AI? One way or the other, it very likely means significant tradeoffs. Whether we in higher education adopt AI quickly or slowly, collectively or individually, actively or passively, intentionally or unwittingly, there are many potential benefits we might reap (or forfeit) and potential costs we might bear (or avoid), but the ultimate balance of these benefits and costs we likely will not know until long after the scales have settled. The recipients of these benefits or costs might be many, too, and they might receive unevenly: we professors, our students, our institutions of higher learning, the culture broadly, and so on. What benefits will there be? Only three years into the history of publicly accessible generative AI, we really do not know. (If someone tells you differently, pat your pocket for your wallet.) Many hope that AI in higher education will enable students to absorb more knowledge, more deeply, and in less time; tailor instruction to their individual interests, learning styles, and academic histories; overcome linguistic and cultural communication barriers; and more. I hope for these, too—if the cost is not ruinous. What potential costs, then? Well, let me zoom out before zooming back in. With any new or emerging technology, pure optimism and pure pessimism both are unwise. Naturally, the developers and purveyors of a new technology will be inclined toward optimism, to be what Neil Postman called “one-eyed prophets”. Out of love (and fear), many parents view their children now from the perspective of a possible future where those children have become the people their parents hoped they would become, forgetting that this is but one possible future. We put sweet Little Johnny on the road to becoming a good man and prosperous doctor, therefore, surely the teachers are mistaken about him bullying his schoolmates at recess. Just as naturally, many of those who feel threatened by a new technology are disposed toward pessimism. Not many incumbents praise the younger, flashier challengers. But the history of technology shows us that new technologies are always bags at least somewhat mixed. Again, they involve tradeoffs: always offering these good things, always stealing those. Often enough, the tradeoffs are significant, especially when the new technology disrupts existing institutions, practices, and ways of living. For some technologies, time has made it clear enough that the blessings have outweighed the curses (as we might read Steven Johnson arguing in How We Got to Now for refrigeration, glass, sound reproduction, and a few other innovations). But the other way around is possible, too, as perhaps was the case with asbestos, heroin, and New Coke. Here’s the rub: how do we know at the beginning what the balance of tradeoffs will be? How do we know how the scales will settle before loading them up with dazzling contraptions we have not yet finished inventing? The inconvenient, unglamorous, unmarketable truth is that it takes years, usually decades, and sometimes generations for us to know with any reasonable confidence all or even most of the significant tradeoffs that will be thrust upon us by any important (or even apparently unimportant) new technologies. We can be reasonably confident the odds are high that generative AI systems will have significant tradeoffs. This is in part because these systems already are disrupting a number of institutions, of which higher education is a prime example. Even though, as I have said, we cannot at this early stage be certain what all the benefits and drawbacks of generative AI will be, nevertheless, here I will adopt a provisionally pessimistic stance on generative AI in education. I do this for two reasons. First, on the one hand, from my perspective as a university professor in 2025, the likelihood of negative outcomes for undergraduate education already seems high enough, and what some of these outcomes might be seems discernible enough, to justify concern. On the other hand, and not for any lack of squinting, I confess to finding it relatively more difficult to see beneficial outcomes for higher education that are both likely and significant, and that would not be undermined by contrary incentives or equally likely and significant drawbacks. Second, much of the culture inside and outside of higher education presently is highly optimistic—at least functionally so—about generative AI: universities rolling out free large language model (LLM) access to students without consulting the faculty; a Wild West approach to guidance and rules on student use of generative AI; rapid, sometimes radical adoption by faculty, sometimes ultimately because “well, we can’t stop it, anyway”; and, at not a few universities, incoming cohorts of students who claim that they could not imagine academic life without these (three year-old or younger) technologies. And so, while I do believe that positive outcomes of generative AI in higher education are possible, and I hope for them—even though, strain as I might, I struggle to see through the fog the silhouettes of large positive outcomes—here my tone will be decidedly pessimistic, because I want to press the point that there will be tradeoffs, and significant tradeoffs seem likelier than trifling ones. And I want to describe, in admittedly general terms, the outlines of a particular variety of rocky outcropping toward which we seem to be sailing. In so doing, I hope to remind us of one kind of broad and vital learning goal for our students (and ourselves) that we ought to value above other, newer, and shinier goals, so that we might make more intentional, and hopefully wiser, tradeoffs with respect to generative AI. Here is a question about a specific sort of tradeoff, one obvious to many of us when OpenAI first lobbed ChatGPT into the word in November of 2022, and one attracting more attention of late: [1] would anything important be lost if we were to outsource significant intellectual labor to AI for our writing? Evidently, many people—students and non-students alike, even some faculty—either explicitly with their words, or despite their words implicitly by their actions believe that the answer is “no”, or perhaps the more fittingly flippant “naw”. Surely they are mistaken. I suspect that this mistake is often rooted, at least in part, in a misunderstanding about the process by which a person produces something associated with significant intellectual effort. Consider a paradigmatic fruit of intellectual labor: the essay. Whether a research paper in history, an argumentative philosophical essay, a textual analysis of a work of literature, or some other genre of writing, requiring college students to produce a substantive essay has long been a standard way to assess the degree to which students have grown intellectually—in general, and/or in the ways relevant to an area of inquiry, and/or in the ways prescribed by a particular course. So, again: will anything be lost when students (or others) enlist LLM systems to do much of the heavy lifting involved in writing? (Note that I do not say “use LLM systems to write”, which is, as I hope what I say will make clear, a confused locution.) I suspect that some people answer “no” because of a misunderstanding involving the idea of talent. On this misunderstanding, writers of good essays have something called natural writing talent, and, accordingly, the process of writing a good essay is as follows. Step 1: be born with natural writing talent. Step 2: tap into this talent, thus producing a good essay. But if you also believe that you simply lack this natural writing talent just as you simply lack, for instance, Simone Biles’s athleticism, then when asked to produce an essay in the age of generative AI, you might infer that you have nothing to lose by employing AI to do a lot of the work for you. Since good writing requires natural talent, and since you believe you do not have it and thus cannot get it, you might as well exercise your leadership capabilities to delegate the work to ChatGPT, which does have imitates having the talent. (Set aside the fact, hiding in plain sight, that this would be dishonest, since you would be mashing your authorial stamp on a pile of prose the production of which requires, by your own lights, the kind of talent you believe you do not have.) I hope the articulation of this picture makes clear how little it represents reality. Even if there is some trait—or a tangled complex of traits—the having of which to a high degree we could sensibly refer to as a natural talent for writing, and thus even if this would help with writing, it is neither necessary nor sufficient for writing reasonably well. You do not have to be Simone Biles to do a bit of tumbling, and even Biles could not do it so well without a lot more than merely her natural talent. Perhaps more people answer “no” because of a misunderstanding concerning work. On this misunderstanding, what writers of good essays contribute to the process is mostly a lot of time and work, and, accordingly, the process of writing a good essay is as follows. Step 1: work, work, work, … Step 2: thus appeareth a good essay. If that is what you think, and if you think also that the work involved in producing the essay is just, well, work, and thus that the time spent working is time that, ceteris paribus, could be better spent on other things; then, from that point of view, if you knew you could cause a similar enough essay to exist, but for the cost of a lot less work, that approach would look pretty attractive to you. If the work you would do is purely instrumentally valuable—valuable for what it can get you rather than valuable in itself—well, then there is a lot of time and energy to be saved and nothing to be lost by getting the good essay without putting in all the work. If you can buy the same new car for half the price, why would you not? Of course, the “work —> essay” conception also betrays a serious misunderstanding of the human process that results in good writing. The reality is that, whenever we have become able to write a good essay, any good essay—“all by ourselves”, as we used to say when we were intellectually curious and sometimes wiser little toddlers—that essay is the product of our having undergone an unfathomably complex process involving not merely a lot of intellectual (and sometimes physical) labor, but also having simultaneously exercised (and in so doing sharpened) a wide array of intellectual abilities ranging from highly specific to very general: the ability to recognize, and to clearly and distinctly conceive this, that, and the other concept; the ability to distinguish those concepts from one another and uncountable other concepts; the ability to employ those concepts to ponder a problem, explore a possibility, articulate a precise question, or defend a position in a debate; the ability to string together ever more complicated thoughts into coherent lines of reasoning; the ability to organize those lines of reasoning into larger, intelligible wholes; and a thousand other intellectual abilities. All that, and doubtless much more besides, is what happens to you along the way to writing a good essay. But, at least in the context of higher education, the writing of the essay is not itself the primary point. The point—or, at least, the most important one—of having to write an essay is the process of intellectual inquiry and development that happens by way of writing the essay. The point is the cultivation of ourselves, we reasoning animals, we rationally autonomous persons. But this intellectual formation, this cultivation of our minds, does not happen by magic. Neither does good writing. As philosophers have told us for thousands of years, ex nihilo nihil fit: out of nothing, nothing comes. The intellectual structures, abilities, and habits that lead to good writing—and the general ability to think well which usually comes in large part from learning to write—must be cultivated, and that cultivation requires work, struggle, frustration, false starts, crappy drafts, and hours well spent despite feeling wasted to the uninitiated. Obviously, then, offloading to LLM systems the intellectual labor required for writing well also excises the most valuable—both intrinsically but also instrumentally valuable—part of the process, namely, our own intellectual development. So, students (and others) who lean heavily on generative AI to write their essays are not like the (imaginary) customer who pays half price to purchase the same new car. They are more like a customer who pays half the car’s price for the appearance of having a car. But posting selfies in a driver’s seat is not the same as owning the car and driving it home. And handing in a document full of AI-generated or even significantly AI-assisted word-shaped assemblages of symbols is not the same as becoming capable of writing well. And since to be capable of writing well just is, to a degree shockingly unrealized or ignored by many, to be capable of thinking well, well, here is a piece of advice: pay the full price. If you don’t, you won’t just get what you paid for. You’ll end up paying for what you get. Notice that, in other contexts, we would not make the analogous mistakes. We would consider them silly. Consider a D1 football player with NFL aspirations. He hears of a marvelous new techno-gadget—let us call it the “Fraude”—that can help athletes pass fitness and performance tests that normally require hundreds of hours of weightlifting, drills, strict dietary routines, and so on. Our player views the latest Fraude ad on YouTube: Setting down the Xbox controller, an out-of-shape athlete checks his phone, sees the time, and looks into the camera in horror. He sprints from his dorm, arriving at the training facility, huffing and puffing. Peeking through the doors, he sees his coaches checking their watches, grumbling. But then the athlete remembers with visible relief: the Fraude in his pocket! He taps its “linebacker” button, pockets it, and opens the doors as a cloud of athletic illusion is cast about him. To his coaches, his beer belly looks like a six pack; his 40-yard jog registers a 4.4 second dash; his straining to bench press a bare bar a few times earns a record number of 225 lb. reps. He sits up, and over the athlete’s shoulder we see the coaches nodding with impressed approval. Strutting out the doors, the athlete notices a potato chip stuck to his shirt, pops it in his mouth, and winks at the camera. Question: does the ad seriously tempt our D1 football player to buy a Fraude? Obviously not. Why not? Because he knows that the point of it all—the grueling routines, the sweat wrung out on the field, the injuries and recoveries, the social and academic opportunity costs—is to become an elite athlete who can dominate on the field, at the highest levels of competition, in reality. And he cares about that. In other words, he sees clearly the connections between the process and the person into which he is developing himself, between that person and the athletic performances that are the products of that person, and between all of that and the life he believes is worth living. But many students (and perhaps a few faculty) do not so clearly see that the processes involved in writing papers for their philosophy or history or literature (or other) classes is connected both to the products (i.e., the papers themselves) and, much more importantly, to the people they could hope to become and the lives they already believe are worth living. (And if they have not thought much about what a life worth living might be, well, maybe writing those essays could help with that.) These connections are indeed there to be seen, even if many of us presently are learning that we must point them out more clearly than we have done. Somewhere a friendly skeptic harbors the following suspicion: “Ok, but isn’t the football Fraude relevantly different from the college essays?” In the case of football or other athletic endeavors—or even many of the assessments in STEM fields—it is fairly (or at least more) obvious why the mere appearance of a performance is not the same thing as a real performance and the ability to really perform. But, the skeptic thinks, “If I enlist ChatGPT to outline or structure or draft or revise an essay, the resulting essay itself would be no different in essence from an essay I wrote all by myself. Moreover, unlike the football fraud who can’t actually sprint a 4.4 or bench 225 lb., I can read the strings of symbols resulting from ChatGPT, and I can understand them, affirm them, and adopt the ideas represented by them. So, wielding ChatGPT, the essay I caused to exist is really real in just the way that the football fraud’s six pack isn’t.” The mistake embedded in this line of thinking is clear. Imagine a food lover who has never cooked a meal for herself. But she has just purchased, unpacked, and plugged in her new Food-o-Matic. She presses a button, moments later the Food-o-Matic dings, and its door opens to reveal a perfect, steaming Pizza Margherita. She bites a slice, closing her eyes to soak in the flavor. We might even suppose that she can distinguish the pizza’s component flavors, the basil, mozzarella, virgin olive oil, and the rest. But, for all that, she is no pizza chef. She could not make a pizza to save her life. The ability to cause a pizza to exist, and even to recognize and delight in its flavors, does not amount to the ability to make a good pizza. Much more is involved. Similarly, even if you learn how to prompt an LLM system—an increasingly easy task, by the way, as LLM systems continue to improve—and even if in so doing you play a causal role in the coming to be of a good-looking essay; and even if you can understand and believe the ideas that humans take the text to represent; even so, you are not thereby capable of writing that essay. And, more importantly, you have not thereby engaged in an activity that has served to help cultivate the sort of mind that can think well—which was, all along, the very reason you were required to write the thing in the first place. (Or, even if the cultivation of your mind was not anyone’s reason for you to write it, it is what would nevertheless have happened, and it could have been and probably should have been a reason.) Another doubt may linger in the mind of a persevering skeptic: “Even if you’re right, even if something is lost by outsourcing our writing to LLMs, it’s just not very important, because I don’t need to be good at writing! I don’t aim to be a writer, or to be employed in a job requiring lots of writing. Moreover, it might not be long before the LLMs do all the writing for us. And anyway, clearly writing isn’t necessary for being intelligent, since Socrates didn’t write, and he was one of the smartest humans in history!” First, regarding Socrates: true, he did not write; and true again, he criticized the invention of writing, worrying that it would lead to even more widespread illusions of having knowledge in place of the real thing. But Plato, Socrates’s best-known student and arguably the most influential philosopher in human history, wrote a lot. (Who do you think wrote the Phaedrus dialogue in which Socrates criticized writing?) Even writing came with tradeoffs, it is true, but I happily grant that, overall, the tradeoffs have been overwhelmingly in our favor. But note that there can be either or both of two distinct objections in the persevering skeptic’s statement above: first, that nothing very practically important (i.e., instrumentally valuable) would be lost; second, that nothing intrinsically valuable would be lost. Although I believe both objections are mistaken, I will admit that, if someone genuinely, honestly affirms either objection after all I have said above, well, maybe there is nothing more to be said but, “Fair enough.” Each must pursue what they judge to be good and right, I suppose. But in my view the core mistake motivating either objection is decoupling learning to write long and detailed essays from learning to think long and detailed thoughts. For nearly everyone, however, these are not decoupled. For long enough that it should be obvious, we have seen that developing the ability to do the latter typically comes via developing the ability to do the former. And I will assume I need not argue that learning to think well has obvious instrumental value beyond the writing of essays. In any case, anyone hoping to be, or even to claim to be, a generally intellectually capable human being ought to be able to think sustained, careful thoughts. Finally, what if one day the AIs will be able to do all the writing for us? I do not really buy that, but suppose it, anyway. We will nonetheless continue to be rational animals, thinking things, rationally autonomous persons. And thus, the ability to think well will always be of intrinsic value for us, because the ability to think well is for us, in large part, the ability to live well. ——— A man walking a well-trodden path crosses into a land of thick, unknown flora: trees bearing unfamiliar fruits of indeterminate shapes but alluring colors, dangling with vines and creepers that appear young but whose origins above and tips below he cannot trace. Up ahead, the path forks. Still walking, he peers left where the path dissipates quickly into the trees, but through the leaves he sees the bright berries and flowers. Glancing right, he notes a visible though indistinct path climbing upward towards what looks like a hilltop likely affording a view of the lands below, but from his present vantage point the trees appear sparse, and no fruits are visible. Without breaking stride, the well-fed man, being a hungry man, veers left, believing himself to walk straight. Moments later, he finds himself stumbling down a descending hillside, picking up speed, struggling to keep his feet beneath him while his arms grasp for fruits above, pulling down into his mouth chunks and stems and thorns. The man wonders fleetingly if he’s taken the wrong path, or if at least he ought to slow down or even backtrack, but he’s moving too quickly now. And anyway, he tells himself as a blast of sweetness strikes his palate, he can use this mysterious foliage itself to ensure his safe descent into, well, wherever he’s going. So, casting his arms upward toward a vine without visible whence or whither, he pulls hard. NOTES [1] See, e.g., Andrew R. Chow, “ChatGPT May Be Eroding Critical Thinking Skills, According to a New MIT Study”, 2025, Time. https://time.com/7295195/ai-chatgpt-google-learning-school/ and Chloe Berger, “AI might already be warping our brains, leaving our judgment and critical thinking ‘atrophied and unprepared,’ warns new study”, 2025, Fortune. https://fortune.com/2025/02/11/ai-impact-brain-critical-thinking-microsoft-study/
A Response to Tobias Flattery’s Essay
In Fragments of an Infinite Memory: My Life with the Internet, Maël Renouard remembers “a period when, without anyone saying it aloud or even thinking it in a very direct way . . . there was something almost shameful in resorting to the internet, at least in certain academic or scholarly circles, as if it was like taking a performance-enhancing drug for the mind, or looking up the answers at the back of your math book without having made the effort to solve the problem on your own.” [1] He then acknowledges the profound change in that stance, an eventual acceptance of the technology “in which the first thing we do . . . when we hear of something is to offer it up to the Google search bar.” [2] In the time between writing my own contribution to this collection and, now, responding to Tobias’s essay, I think that I’ve observed a similar development in people’s relationship to generative AI, whether it’s a student casually remarking that they used ChatGPT to brainstorm possible essay topics or a colleague pointing out that their committee used Claude to compose sections of its annual report. In that way, the question—if it ever was a question—of AI’s adoption seems to have been answered. The technology exists, so people use it. From my perspective, what Tobias’s piece, with its evenhanded approach, allows us to see are the potential tradeoffs of accepting that situation without questioning or at least putting a little pressure on it. As he observes here, using generative AI in educational situations, especially when it allows a person to represent LLM output as their own thinking, mistakenly casts writing as a product rather than as a reflection of the process of learning. The essay’s playful example of the Fraude makes that relationship particularly clear. No athlete would substitute the appearance of training for actual training, since there is a moment on the field or the court where one actually needs to demonstrate or to be what appearance only suggests. One is tempted to ask (I’m likely one of the pessimists that Tobias identifies early in the essay) if the function of the university as a credentialling institution hasn’t blurred the lines between appearance and being. Reading this essay, I’m reminded that the question of what it means to learn and teach in the age of generative AI—a question that seems to live primarily in the classroom—is, of course, shaped by cultural forces that exert their influence slowly and profoundly, like those in Renouard’s account of internet usage. When I think about what to do as a teacher in response, I find myself drawn to the insights of arts education, even if I don’t know yet precisely how to apply them to the teaching of writing. In his classes at Black Mountain College and, later, at Yale, the artist Josef Albers “was adamant that no art was to be made in his courses. Rather, the objects made were to be evidence of creative behavior, material manifestations of the investigative processes that brought them into being. . .” [3] If the connection between the academic essay (the “objects made” in a class) and the “investigative processes that brought [it] into being” has been severed, potentially, in the age of AI, then what can we do in a class that brings them back together? NOTES [1] Renouard, Maël. Fragments of an Infinite Memory: My Life with the Internet. New York: New York Review of Books, 2021. 67. [2] Renouard, 68. [3] Saletnik, Jeffrey. Josef Albers, Late Modernism, and Pedagogic Form. Chicago: The University of Chicago Press, 2022. 48.
This was the sentence that gave me trouble, that raised my suspicion: “Comprising Roger Waters, David Gilmour, Richard Wright, and Nick Mason, Pink Floyd’s concept albums and immersive sonic landscapes reshaped the boundaries of musical expression.” It appeared in an early paragraph of an essay written for a first-year writing course in response to an assignment that asked members of the class to analyze the discourse around a musician or a band of their choosing. [1] Leaving aside for the moment any questions that the assignment itself might raise (in a required course thematized around music-related topics, I designed it to allow students to practice elements of rhetorical analysis and to put their observations into conversation with other sources), I found myself, reading the paper, faced with a very specific question: did this writer, whose work I had been reading for about ten weeks at this point, really know the difference between comprised and composed of? The sentence uses comprise correctly, of course, but it was a notable choice, one out of character with other pieces of writing that the student had submitted. If I try now to reconstruct what might have been going on in my mind as I read that sentence, I must have been equally curious about phrases like “immersive sonic landscapes” and “reshaped the boundaries of musical expression.” But it also occurred to me, particularly since I was reading the essay in the role of an instructor, that I should be suspicious of my suspicion. Isn’t it possible that the idiosyncrasies of your reading and writing life would acquaint you, even if you didn’t know other usage conventions, with the distinction between comprise and composed of? I found this possibility appealing, since I had recently revised the paper topic with the hope that selecting a band or artist would facilitate what goes under the name of “student engagement.” But as I continued reading, I couldn’t shake my impression that the voice in this essay was different, especially when I arrived at its later claims about the “artistic, non-literal, and concept-driven nature” of Pink Floyd’s music videos. Removed from its context and quoted here, that language may lose some of its effect, but I suspect that if you’ve used a text-generating AI like ChatGPT or Gemini, you’ll recognize the particular flavor of the prose. It’s highly readable, linguistically smooth, but that sentence-level competence masks the absence of something that I feel that I feel when I read: another person’s perspective. As I write, there’s a typewriter on the floor next to my foot, a Hermes 9 in the desirable seafoam green. I haven’t cleaned it yet, a process that I know, thanks to several hours’ worth of YouTube videos, means scrubbing the type slugs with a wire brush and solvent. Though really one begins, if one does, by blowing out the segment (where the type bars, the slender legs of metal on which the slugs, the letters, swing up, pivot) with compressed air, cleaning out fifty years or more of dust and gunk, and then moving on to cleaning, probably with a rag, the spills and the smoke residue. If you’re unlucky, you fashion a new drawband. And after all of that, you install a new ribbon, which they do still make. There’s the Hermes at my feet, and there’s the Olympia SG-1 that I’m typing on now, a desktop machine produced in Wilhelmshaven, Germany. It weighs forty pounds. The phrase greatest typewriter ever made is thrown around with some regularity in discussion boards on the internet, but the SG-1 is a good candidate, produced when the typewriter was an indispensable office machine. Its keys are weighted and therefore responsive in a way that one notices while typing. It has a calibrate-able “paper injector,” a topside lever that allows a sheet (think of a typist in an office—an office full of typists—turning out pages of text every day) to be inserted to a predetermined starting point. My SG-1 has the name Llewellyn scratched, discretely, into its underside. There’s the Hermes on the floor, the Olympia on the desk, and at least two other typewriters in the closet. Four on the shelves in the basement. Three under the sofa on the sun porch. And two in my office on campus. What am I trying to tell you? I want to tell you how it got like this. Like other people my age (I was born in 1977), I learned to type the drafts of my high school English essays on a typewriter. Personal computers existed, of course, but they weren’t yet ubiquitous. That change would happen while I was in high school; my typing class actually switched from IBM Selectrics to IBM PCs midway through my junior year. The machines weren’t foreign to me, but they did have the aura of, the mystique of, the past—of a once-vital, once dominant technology that had been supplanted by another. So when I experienced a kind of writer’s block in the early stages of writing a dissertation (living in a large midwestern city where I knew no one, where I had no library privileges, I would wake up and read what I’d written the previous day, hate it, and delete it all with a couple of keystrokes), I turned back to the typewriter. This time, it was an Olivetti Lettera 35i, procured from the wilds of the internet. I liked what I’d learned about the history of Olivetti, their partnerships with designers (this particular model was designed by Mario Bellini), and, practically, the experiment was a success. After producing several typed pages at the makeshift desk in my apartment, the typewriter would not allow me to delete them. Could I have thrown those pages in the trash, just as I’d done virtually on my laptop? Yes, but somehow the self-loathing produced by reading my prose was only strong enough to provoke a highlight-and-delete, not a walk to the wastebasket. So, I already owned a typewriter when I saw, a few years later, an exhibition that included some of Carl Andre’s poems. Andre, known primarily as a sculptor, composed on a typewriter pieces that he called poems, but it wouldn’t be wrong, I think, to see them as textual sculptures. They’re usually displayed on the wall or laid out as a series in vitrines. When I saw them for the first time, what really blew me away (I didn’t know about Ana Mendieta, about the controversy surrounding her death) was a page from Andre’s sonnet sequence: fourteen lines of the word green typed over and over, no spaces between the words, centered on the page, a unit of language repeated and presented as a square. The experience of seeing those texts affected me deeply. It made me question the more-or-less conventional poems I’d been making and sending out for years without much success. No one was reading them anyway, I thought, so why not do something to give them a different kind of life, something, like Andre, that would break with the form of the page, the book, and the usual methods of dissemination. So, working with small sheets torn from readymade notepads, I started typing texts with just a few words per sheet, spread out over several pages. Turning them, a reader experienced a delay, activated a break more emphatic than the end of a line. The typewriter was perfect for the job. I put what I wrote in envelopes and sometimes I sent them to people. Eventually, they were published as a book called Rounds. I’m telling you this, I think, because of what came after. In the late fall of 2022, Open AI released a public version of its text-generating AI, ChatGPT 3.5. At the time, I was teaching a first-year class on writing and technology. I remember experimenting with it, not exactly impressed by the sentences that it would construct in response to our prompts. Though I wondered about the technology’s applications, it didn’t seem advanced enough to do the kind of writing that I required of students in the class. I was more interested, in fact, in testing its creative capacities. I joked about it at the time—surely every student graduating that year with an MFA in creative writing was doing the same, I said—but I did spend several weeks prompting ChatGPT with requests like: write a paragraph about everything in this room, write a paragraph about what is happening on this page, write an essay about a character of your choosing and what is going on inside of him. I wanted to know what an AI would write about the incidental, the deeply personal, the oblique or the interior. I understood, I thought, the logic behind how it worked; that is, I thought of myself as someone who had not been fooled into projecting a consciousness onto a large language model. But I did assemble the GPT-produced texts, which I’d slightly revised, into a book-length sequence that I titled I/O, the abbreviation (I’d first learned it when I worked with electrical engineers) for input/output. The last poem in the book I wrote by myself, though, without any contributions from AI. It fit within the sequence because it was spoken from the perspective of something like AI—something with a knowledge (should I be putting these words in scare quotes? “perspective”? “knowledge”?) of nearly everything ever written. The text keeps coming back to the line “bare ruin’d choirs” from Shakespeare’s sonnets. It also quotes John Berryman, John Cage. One of my teachers died that fall, and I imagined that I was writing it for him. But then why, you could ask, as an act of grieving, was I impersonating an impersonation? I found out, quickly, how forcefully generative AI would assert itself in the classroom. By the spring, I’d seen students using it for assignments large and small: a discussion post with a breezy command of material that we hadn’t discussed in class, a personal anecdote recounted in exceedingly smooth, bland prose. I wondered if the students who were using AI were doing it for purely utilitarian reasons—if it was for them the kind of uptick in productivity that their business classes had taught them to value—or if they were experiencing the same momentary thrill that I’d felt the first time I’d served up a prompt and seen language, ideas magically returned to me. One day late in the semester, while I was doing errands around town, I heard a Columbia student interviewed on the national radio show. He was talking about the article he’d written for his campus paper, one in which he detailed how he’d used generative AI to assist him with an essay for a classics course. He wanted teachers to know, he said, how much students were already using the technology. It’s a tool, he said, that educators should leverage for the classroom rather than prohibiting it. When the interviewer asked him how he would do that, the student said: I don’t know. It’s not my job. I made it a point, that summer, to reread writers thinking about upheavals in communication. I was looking, I suppose, for company, for accounts that rhymed with the groundlessness I felt when I thought about AI. So, I turned to some of the texts that I had on hand about the 19th and 20th centuries. I also started—though I can’t say exactly what precipitated it—looking again at typewriters. First, other Olivettis, which I thought of as Italian sportscars. Then Olympias, which I associated with Mercedes. When I drove on the highway that summer, the cars that passed me, their curved and sloping bodies, sent my thoughts in the direction of typewriters. But cars, they were incomprehensible to me. The typewriter was a machine—part beauty, part precision—that spoke to my imagination. Regarding the typewriter—and not just “the typewriter,” but the specific typewriter that Friedrich Nietzsche used—Friedrich Kittler observes that “whereas handwriting is subject to the eye, a sense that works across distance, the typewriter uses a bold, tactile power.” [2] Elsewhere, he emphasizes the difference, for a writer, between composing by hand (organizing and, in a sense, traversing that distance) and typing on a keyboard, where anything that can be said has already been broken up, dispersed among its discrete characters. Such ideas were in my head, or somewhere in me, as my new typewriters began to arrive. Since the newest of them was at least forty years old, though, they all required some kind of repair. And as I worked on them at a table in my basement, it was as if I were becoming acquainted with them, as if maintenance were a kind of incantation, as if it would allow the fractured signs to make a path for me, to show me a way. The poems that I wrote that summer, a sequence that I called (given), feature a typewriter or two. There’s the one that a character installs in his front yard to the dismay of his neighbors; there’s a Smith Corona, which I really did find in a thrift store, that types in italic. The book also records some of my worries about AI and its effects on education. For example: “He decides,” I wrote, “he’ll lecture from his laptop for the rest of the term, he’ll have it connected to a text generator, he’ll prompt it with a topic and he’ll read the results, will it be a private joke or an act of resistance . . .” Kittler writes that “Nietzsche demanded an ‘art of interpretation’ by which each sign was to be read together with contiguous signs as well as with those for which it was a substitute.” [3] Was that what I thought—what I hoped—I was doing? “In place of hermeneutic rereading,” he writes, “[Nietzsche] saw a simple physiological ‘rumination—something for which one has almost to be a cow and in any case not a ‘modern man.’’” [4] If you’re reading these sentences, somewhere along the way you learned to associate marks on a page (or on a screen) with the idea, even if it isn’t one that you consciously call to mind, of someone making them. That is no longer reliably the case, as my experiments with AI and my experiences in the classroom showed me. In a kind of reversal of the Turing test, I wondered if all of the writing put before me hadn’t been generated rather than written, a thought that led me to wonder, genuinely, how much of the civilizational work that writing has done for the last several hundred years depends on the sense of another person on the line. And those qualities that I’d come to value most in writing: difficulty, not knowing, interiority, counsel—had their transformation been underway even before this latest form of symbolic manipulation? Was it just the next step, the logical conclusion, of a process that had begun with social media, with the internet before it, or even (and this possibility was the most alarming to me given my current preoccupations), in the first place, with the individual instance of the letter? Discussions about the effects of technology are full of this kind of analogical thinking. The hold that the smartphone, for example, has on our attention can be traced back to the Walkman and the novel before it. Trains enabled the crossing of long distances in the way that automobiles would, eventually, and also airplanes. Was AI any different? When I talked with a friend about these matters, he told me about the copyists—monks who had been assigned the task—who first inserted punctuation into the Latin texts they were preserving. When their masters evaluated their work, when they interacted with this new invention, they recoiled. How could the copyists impose, they wanted to know, their own sense of meaning (what is punctuation if not notation toward a meaning) onto what the original text left open? From our position in the present, we’re likely to see those monastic masters as fussy, benighted, and stuck in their ways. Get with the program! we might want to say—can’t you see the obvious benefits of punctuation (or email, or streaming music, or online banking)? But what was it like to encounter a text before punctuation? What loss could those monks register that couldn’t be recovered thereafter? Many thinkers whom I admire have convincingly argued that “the human,” as a category, has been constructed as an instrument of domination. The long history of civilization’s relationship to animals, for example, could serve as just one piece of evidence for turning away from the human and its tendency to privilege its own interests. But the emergence of 21st-century incarnations of AI has obliged me to rethink some of my antihumanist inclinations. If a text-producing AI can generate sentences and paragraphs that sound like a person, then what is left to a person, writing, to do? Or to put it another way: what is worth writing, now that humans don’t have to do it themselves? From a certain perspective (I think of my students), a writing machine is a welcome invention. It solves the problem of sitting before a blank page, not knowing what to say. But wasn’t it E.M. Forster who said “How do I know what I think until I see what I say?” Could an AI’s output be what you think? Will humans ever see it that way? And, if so, what will have changed? All the time, while I was thinking these thoughts, the typewriter continued to beguile me. My collection grew; I composed on them almost exclusively. And like any true believer I tried to inflict my fascination on others. The machine found its way into my classes. I even devised an assignment (we were reading A. R. Ammons’s Tape for the Turn of the Year at the time) that had to be typewritten. That’s how I found myself thumbing through Tom McCarthy’s collection of essays, Typewriters Bombs Jellyfish. I should have known that I wouldn’t find what I was looking for, an essay that would lend some coherence to my idiosyncratic, disordered speculation. To my dismay, I could locate no single text on the typewriter in the book (just as there’s none on bombs or jellyfish). What one finds instead, scattered throughout the chapters: Ed Ruscha’s typewriter, destroyed in the process of making Royal Road Test, Kathy Acker sitting cross-legged on the floor at a typewriter pounding away, or a passing reference to Don DeLillo’s Mao II, where a writer’s assistant muses that “The withheld work of art is the only eloquence left” as he cleans one. [5] A typewriter is also present when McCarthy writes admiringly about Cain’s Book, a novel by Alexander Trocchi. In fact, it is that essay’s last word: “The scow on which they make love,” McCarthy writes, “floats on the ‘black ink’ of the Atlantic; inside it there is virtually nothing: just a single bed, a coal stove, cupboard, dresser, chair and table—and a typewriter.” [6] Perhaps because I did not know what a scow is (a kind of small barge) when I first read it, I imagined the craft as much too small, folding all of its appurtenances into some zone of indistinction. In fact, I imagined it as a kind of raft—a raft of typewriters lashed together, floating because they had been relieved of, freed from, their weight. In that way, one could be araft in the way that he might have otherwise been adrift, though among the typewriters—no, on them—he would be, I imagined, somehow safe. Was the image too fanciful, was I just trying something on? Yes or no, it's what I wanted. I don’t know if you’ve been wondering about what happened with the student whose essay on Pink Floyd—the one that used “comprise” in a way that made me suspicious—but I asked that we meet to talk about the work. I wanted, I think genuinely, to try to understand what they had done, so I aimed for candor in our meeting, stating that my experience of reading the paper reminded me of many of the qualities that one encounters in AI output. They denied using AI on any part of the essay. I asked which of Pink Floyd’s videos they were thinking about when they praised their “artistic, non-literal, and concept-driven nature.” They couldn’t elaborate on that point, they said, because they had likely gotten that idea from another source, forgetting to attribute it. When I asked the student about the difference in voice between this essay and a paper they had submitted earlier in the semester, which tended toward less complex syntax and less precise, elevated language, they simply said that their writing had improved greatly between assignments, a credit to the class. Unlike instances of plagiarism, in which a writer lifts language or ideas from another source and adopts them without attribution, there is no “original” source to locate for an AI-generated paper. That is, there is no moment when an instructor finds, in another source, confirmation that a student’s work is not their own; since AI outputs a text based on the next most likely series of characters, it's different each time. And the purveyors of AI-detection software—which is fallible and, notably, often developed by the same companies that sell access to text-generating AI—conscious of the high stakes that students and their parents attach to grades, indemnify themselves with warnings like this one: “This result should not be used to directly punish students. For a more holistic assessment and responsible use of results, read our five steps towards responsible AI detection.” Institutional policies on AI often offer even less guidance, if they exist at all. As it stands, this issue is one that plays out between student, teacher, and the text that one gives to the other. Isn’t that the way it’s always been? one might ask. I’ll tell you what’s different now. In some ways, what I’m getting at here raises questions about what, fundamentally, education is, what a course, spread out over a specified number of weeks in an institutional context, does. Maybe the best way to approach it is for me to ask you to remember the last class that you took that really changed you, or one, at least, that affected you. (I hope that one has.) In that course, didn’t you do something—write something, compose something, make something—that you wouldn’t have done if the class hadn’t required it? In some cases, I’d wager that the task seemed difficult at first, that you didn’t know how to complete it. And, maybe, faced with that difficulty, if you could press a button and have something create it for you, you would. The power not to do something oneself, to prefer not to, and in the absence of institutional guidance or policies, to face no consequences—that is a power in play now. You will remember that Nietzsche wanted a reader to be something like a cow. The animal appeared again when I read Thomas Mann’s Doctor Faustus. Supposedly, Mann was thinking all the time of Arnold Schoenberg when he wrote the novel, which transposes the Faust myth onto 20th-century music. In an early scene, the young composer Leverkühn tells the narrator, his friend, what he admires in the music that their teacher has described for them, the eccentric choral pieces composed by the leader of a religious sect, Father Beisser. He structured his compositions on the most rigid of frames, their teacher said, applying the technique—to his own songs, to traditional hymns, to excerpts from the scriptures—of assigning the notes of a key’s common chord to a line’s accented syllables. The results were disorienting, strange, but sung in the low-ceilinged church they seemed to float down from heaven. The young composer, the Faust figure, Leverkühn is deeply impressed, though like a young man he doesn’t want to show it. “Funny, it’s very funny,” he said. “But one thing you will admit. Law, every law, has a chilling effect, and music has so much warmth anyhow, stable warmth, cow warmth, I’d like to say, that she can stand all sorts of regulated cooling off—she has even asked for it.” [7] From there, the conversation becomes a friendly, but serious, debate, with the narrator taking the side of what might be called music’s expressive power. “A gift of life like music,” I responded, “not to say a gift of God, one ought not to explain by mocking antinomies, which only bear witness to the fullness of nature. One must love her.” “Do you consider love the strongest emotion?” he asked. “Do you know a stronger?” “Yes, interest.” Two cats facing the same way on the concrete wall, facing the same direction. End of September, eyes closed, forepaws touching. Where do you start the sentence and what do you point out, pick out, to what do you direct attention? Perhaps the situation was worse, I thought, because in some way we had been prepared for it; we had been told even if we hadn’t heard the warning. I remembered something that I’d read in graduate school, one of Slavoj Zizek’s early books, a moment in which he considers the effects of the digital/virtual on human understanding. It had been easy to dismiss in the moment, and even after the moment, because Zizek uses virtual reality as an illustration of Lacan’s mi-dire, or half-saying, and even then virtual reality was a joke. But what if we hear in the following passage a description of the ways that technology has, in the past twenty years, accelerated the creep of the symbolic into a headlong sprint: the commonplace according to which the problem with cyberspace is that reality is virtualized, so that instead of the flesh-and-blood presence of the Other we get a digitalized spectral apparition, misses the point: what brings about the ‘loss of reality’ in cyberspace is not its emptiness (the fact that it is lacking with respect to the fullness of real presence) but, on the contrary, its very excessive fullness . . . Is not one of the possible reactions to the excessive filling-in of the voids in cyberspace therefore informational anorexia, the desperate refusal to accept information, in so far as it occludes the presence of the Real? [8] All of the posts, and the think pieces about the posts, and the development of a technology dedicated to manipulating symbols; from my perspective, more and more, they looked like methods for filling in the voids. From my perspective, the glee with which people took them up seemed more and more related to the desperation of their refusals. “We resort to cliché,” Anne Carson writes, “because it’s easier than trying to make up something new. Implicit in it is the question, Don’t we already know what we think about this? Don’t we have a formula we can use for this? Can’t I just send an electronic greeting card or Photoshop a picture of what it was like rather than trying to come up with an original drawing?” [9] I hope that you can hear in this passage, in the moment that cliché—and what is AI but a patchwork, an apotheosis, of cliché?—poses its question a moment loaded with potential. Will I follow an existing path? Will I try to invent something new? The quotation comes from Carson’s essay “Variations on the Right to Remain Silent,” a piece that develops, through tutor figures like Joan of Arc, Francis Bacon, and Freidrich Hölderlin. what she calls “metaphysical silence.” It is a concept that I think I understand best in her treatment of Bacon’s painting. In an interview, Bacon talks with the art critic David Sylvester about violence, a topic that often comes up in his interviews because of the subjects he paints: animals, people, sides of meat—all recognizable but represented in a way that disfigures or distorts them into something unfamiliar and strange. Carson’s interest in the paintings lies in the complexity of what he means by “violence,” by their pursuit of some quality that can’t be communicated by the choice of content alone. It’s a tricky subject, but Bacon puts it like this: “We nearly always live through screens—a screened existence. And I sometimes think when people say my work is violent that from time to time I have been able to clear away one or two of the screens.” [10] The screens here aren’t, of course, the computer, tablet, and phone screens ubiquitous in our everyday lives; rather, they are the customary, habituated ways of seeing the world that keep us from really seeing it. Carson again: Bacon says we live through screens. What are these screens? They are part of our normal way of seeing the world without looking at it, for Bacon’s claim is that a real seer who looked at the world would notice it to be fairly violent—not violent as narrative surface but somehow violently composed underneath the surface, having violence as its essence. No one has ever seen a black hole yet scientists feel confident they can locate its essence in the gravitational collapse of a star—this massive violence, this something which is also, spectacularly, nothing. [11] Black holes, nothingness, silence. If it seems that we’re pretty far from typewriters and AI, all I can say is that they’re all related in my mind. What is worth writing, what is worth doing, when a machine can do it for you as your proxy? I asked a version of that question earlier, and it occurs to me now that falling silent may be one answer. Not all the time, not forever. But in an environment of information, content, textual production, and so on, deciding not to speak may be something worth doing, even if it’s at odds with the ways that we typically think about meaningful communication. Presently, I do not know how to teach it. On some days, I think that it may be more important, more necessary than ever. On others, I ask—myself and now you—what are we even doing here? NOTES [1] My description of this student paper is based on real experiences, but I have altered the essay’s details so that it does not refer to any individual’s paper. [2] Kittler, Friedrich. Discourse Networks 1800/1900, trans. Michael Metteer, with Chris Cullens. Stanford, CA: Stanford UP, 1990. 195. [3] Kittler, 189. [4] Kittler, 189-90. [5] DeLillo, Don. Mao II. New York: Viking, 1991. 67. Quoted in Tom McCarthy, Typewriters, Bombs, Jellyfish: Essays. New York: New York Review of Books, 2017. 237. [6] McCarthy, 76. [7] Mann, Thomas. Doctor Faustus, trans. H. T. Lowe-Porter. New York: Alfred A. Knopf/Everyman's Library, 1992. 67. [8] Žižek, Slavoj. The Plague of Fantasies, London: Verso, 1997. 155. [9] Carson, Anne. Float, New York: Alfred A. Knopf, 2016. [10] Sylvester, David. The Brutality of Fact: Interviews with Francis Bacon, London: Thames & Hudson, 1987. 174-75. Quoted in Anne Carson, Float, New York: Alfred A. Knopf, 2016. [11] Carson, Anne. Float , New York: Alfred A. Knopf, 2016.
I think it is important to acknowledge some personal background that certainly influenced me as I read Carter’s "Raft." Since he divulged his birth year (1977), it is only fair that I share mine: 1985. While he described his junior year’s conversion from IBM Selectrics to IBM PCs, my elementary school was installing a new computer lab with Macintosh Classics. Next, my dad worked in the Army signal corps (my mom was a teacher, which I do not think has influenced my reflection for this piece but it felt dismissive to only tell readers of my father’s occupation). The Signal Corp is the branch of the Army responsible for communications and information systems. I grew up surrounded by satellite dishes, network cables, radios, and computers. My dad overclocked - the process of increasing a CPU or GPU clock speed beyond its default settings - our Packard Bell 286 so it could run at a whopping 4 MHz instead of its factory setting of 2 MHz. My current work laptop, an HP Surface, has intel i7 that runs at 3.3 GHz, one-thousand times faster than Finally, my experiences both as a student and professor of engineering has meant my relationship with technological advances, some of which I spoke about in my own piece, are coming from both personal interests as well as what I see as a professional obligation to my students. For example, the CPU clock speed I mentioned earlier increased exponentially in the 90’s hitting 3 GHz by the early 2000’s. This stalled, with most CPU’s still running in the 3-4 GHz range, due to heat and power limits of materials and chip design. This is a huge area of research and development for electrical and computer engineers. What was most striking to me in reading "Raft" was how many parallels I could draw from Carter’s experiences and observations to my own. His description and appreciation for the machinery and aesthetics of a typewriter, as he watched cars go by, made me laugh (or LOL as text speak would say), because while he found cars “incomprehensible,” growing up (and as an adult) I found cars teeming with intrigue and possibility. His description of the process required to repair his Hermes 9 reminded me of cleaning out my PC case to upgrade the graphics card and add additional memory. It feels important to share that, around the same time Carter’s high school transitioned students from IBM Selectrics to IBM PCs, had you asked me what I wanted to be when I grew up, I would have told you proudly, “an author.” I never used to think much about how I went from wanting to be an author to becoming an engineer. I loved cars, I was good at math, and it was what was encouraged when I went to a STEM-focused high school. Gradually, decisions had to be made: robotics or drama, journalism class or AP Physics, marching band or soccer. Looking back on those decisions, most seem like I made practical, what-is-best-for-my-long-term-success decisions. What I realized, while reading Carter’s piece, was how much of those decisions were also shaded with self-doubt and my own perfectionism. I didn’t think I would ever amount to a professional musician or writer, so why spend time on it at the expense of the things that were more likely in my future? When Carter described going back to his typewriter after digital methods allowed too easy of a hate it, delete it, process, a new thought started to take form. What if the convergence of my own perfectionism and the onset of my digital conversion somehow contributed to my loss of enthusiasm and efficacy of becoming an author? For Carter, he found going back to physical paper meant it was more likely his drafts would stay in existence. For me, I think I went the other way. Writing on the computer meant you had to have something worth writing. Something worth firing up the computer for. Something worth taking up precious floppy disc memory space. Something, gasp, other users of the shared family computer might see! And much like how the conversion to digital photos has meant framed pictures of me and my brothers adorning my parent’s home ceased to exist after 2002, the last scrapbook of poems and stories in my old school box was from 5th grade. Perhaps I am thinking too much into this or pulling on a thread that isn’t even there, but this thread was created by reading Carter’s piece, so some truth must exist in it. Carter also alluded to all of us hopefully having a class that moved us or asked us to create something we would not have unless it was required of us. Unfortunately, when reading this, and reflecting on my own engineering education, I honestly came up blank. Sure, I was proud of my accomplishments, but moved? Not at all. Engineering was my comfort zone. What I knew I would do well in (or well enough). This writing project is the first time, in a very long time, I have done anything that stretched me. I wish I could somehow convey to our students, particularly those in classes they are taking outside of their intended major, perhaps begrudgingly, what a privilege it is for them to be able to have that time and space to do so. Would my trajectory have been different had there been a writing requirement, beyond technical writing, during my undergraduate studies? Would I have nourished the 3rd grade would-be author had there been scope to take calculus and creative writing? Does it even matter? Years ago, one of the aspects of my job was to help prepare PhD students across the university for their final PhD defense. I did this by providing feedback on dissertations, facilitating mock defense presentations, and mock committee questioning. One that stood out to me was a PhD student in music. I read their dissertation which aimed to develop a new approach to hermeneutical analysis of pop songs and then hosted a mock defense where I listened to their presentation and prepared questions. I knew enough about research methods to ask questions about his choosing cognitive narrative theory in his framework, but relied heavily on Google to help me understand some of the other concepts presented like diegetic framing and verisimilitude. The researcher was brilliant. Their work was fascinating and complex. Yet, in the back of my mind, I kept thinking, “so what?”, “why does this even matter?” I felt guilty for having these questions, but still, they were there. I first revisited this memory during our group discussion on How Data Happened. In this book, there is a section that describes Alan Turing’s original vision for what machine learning and machine intelligence could be, if not limited by memory. “Turing disclosed a capacious vision of intelligence, drawn from the human and animal world, full of logic, love, creativity, craft and laughter. In the years to follow, many pursuing forms of machine intelligence narrowed their sights considerably…Along the way, the very notion of intelligence at issue lost most of the capaciousness Turing suggested. And in parallel, data and experience lost their centrality for the creation of intelligent behavior.” Later in the chapter it is suggested that as our model for machine intelligence shifted, so too, did our (society’s) own characterization of intelligence. Logical, symbolic thinking was bolstered above “lower capacities” of working from sense experience. I cannot tell you why this reading tugged at this memory of hermeneutical analysis of pop songs, but something reminded me of my questioning why this topic even mattered. And it occurred to me, THIS is why it matters. With all the advances of what AI can do, the meaning-making from the creation of and listening to a song, that is unique to perspective, culture, and experience is and remains uniquely human. Would an AI-generated analysis of a song be able to move beyond the structural components of song to understand how the music creates meaning and evokes emotions? Sure, if you prompted it to, ChatGPT could give you back words to describe a song in an academic language. It is advanced enough now you could even provide context and ask it to consider its analysis from a specific point of view. I was reminded of this memory again reflecting on "Raft." Carter’s student, who claims they did not use AI for their analysis Pink Floyds’ music videos. The PhD dissertation gave a framework for the analysis of pop songs. As in Carter’s quote from Leverkühn, if interest is a stronger emotion than love, learning how to listen to and appreciate the nuances of music can only intensify interest, right? Carter ended his piece with the question, “what are we even doing here?” I wish I had a tangible answer. When I think about my role as an educator, it occurred to me that if humans were only ever to learn and practice the analysis of pop songs, the part that ChatGPT can now do (mostly) for us, we are losing important aspects of the meaning-making. Of course we want students to be able to think, deeply and critically, about the world around them. Thinking deeply, feeling deeply are uniquely human. If these are only ever internalized and never expressed outwardly (through written word or song or other expression), is part of meaning-making (what it means to be human, or the human experience) lost? I guess where I am getting at is, what happens if we lose the ability to express ourselves because we rely on auto-generated text (or other content) to do it for us? As educators, perhaps what we are doing here is convincing (trying to anyway) our students that it isn’t the perfect essay analyzing Pink Floyd that we are after, but developing ways for them to express their own meaning from the music. NOTES [1]
A Response to Carter Smith’s Essay
I am not a writer. I know how to convey information through writing. I write research papers (begrudgingly) with moderate success. I write grants – well enough to receive funding occasionally anyway. Even still, with all the writing I do between class assignments, emails, research papers, protocols, grants, and recommendation letters, I do not consider myself a writer. A writer crafts thoughts, ideas, or stories through language. There is an intentionality and artistry to writers’ works. As an engineer, having no college-level writing course experience, I have been trained in neutral, concise fact delivery. As a dyslexic person, I have never learned how to spell. And reading, even the reading I have loved, has been laborious. The use of language, whether conveying or receiving it as an art, has always felt hard. I am not a writer. This fact has rarely given me pause. Until now. Now, I have been tasked to write, for the first time in over twenty years, a piece that conveys my thoughts, rather than facts. My colleagues, all gifted in this art form from years of experience and honing their craft, who may have had pause on the topic or debated their approach, likely did not spend time doubting their ability to convey their thoughts in written form. While they may have felt some trepidation about sharing their pieces with our group, I may be the only one who still throws the odd comma in just because it has been a while and does not know how to use a semicolon properly. I suppose I’m starting here to prepare readers of my contribution for what will likely be a structurally juvenile collection of words. I am not a [trained] writer and yet, here I am writing. For this piece, I chose to think on “what is the purpose of teaching and learning?” I suppose the first teachers saw the transfer of knowledge (teaching) as a necessity with regards to species preservation. When I think of teaching’s purpose now, there is a dichotomy between what I view as the educator’s purpose in teaching and the larger, societal view on the purpose of teaching. While we all have our own reasons, I believe most of us become educators from something intrinsic. We want to impact people positively. We see this as a profession that allows for human connection and creativity. We find fulfillment in guiding others in our areas of expertise. How do these, whether altruistic or self-serving, align with some views that the purpose of modern education is in creating human capital (people that can serve societal needs)? How does the influence of AI impact this purpose? Prior to the 18th century, education systems such as religious, apprenticeship, civil service and indigenous existed for thousands of years with the goals of self-enrichment, community and societal benefit. The goal of formal early schooling in Europe focused on creating loyal subjects through assimilation, homogenization, and building national identities. Education’s purpose turned more toward developing human capital across Europe and the United States leading up to the Industrial Revolution. Adam Smith, known as the “Father of Economics” and the “Father of Capitalism,” saw mass schooling as a condition for the proper functioning of a free-market economy. Other economists and sociologists have argued that schooling enables participation in the modern economy and is vital to economic development. As colonial forces spread their education systems, existing philosophies and approaches to education were repressed and replaced with these modern ideas. [1] The UNESCO Delors report's vision for education is centered on lifelong learning and four pillars of education: learning to know, learning to do, learning to live together, and learning to be. It proposed education should be a source of "treasure within," fostering personal fulfillment and social harmony, with a strong emphasis on moral and cultural dimensions. [2] Many educators (myself included) view the purpose of education, particularly the liberal arts higher-education, as developing adults who are well-rounded, knowledgeable in the arts and humanities, social and natural sciences, and who can think critically, communicate effectively, and solve complex problems. In engineering education, this often seems to come to odds with students’ perceptions (other’s too, but I only refer to engineering students as that is the group of students I observe) of what the purpose of their education should be when we educators attempt to put these ideals into practice. For example, I often receive course evaluation comments where students express their dissatisfaction of having to write so much in an engineering course or their concern over the amount of time we dedicate to ethical discussions rather than the “real problem solving.” This can be seen more robustly in a recent pilot study that surveyed engineering faculty and students on the purposes of education based on the UNESCO four pillars framework. Their findings suggested that while most faculty strongly endorsed “learning to live together” and “learning to be” over their more moderate endorsement of “learning to know” and “learning to do”, students placed the highest importance on “learning to know” and “learning to do.” [3] To gauge the larger student perspective beyond engineering, I looked at a few recent surveys for some evidence to present here. Anthology conducted a survey in August 2023 of higher education institutions and students from 11 countries, with around 5,000 total respondents. [4] Data from U.S. respondents included 255 students and 251 university leaders. Just over half (51 percent) of students say they enrolled in higher education for higher earning potential, 45 percent are looking to access better job benefits and 40 percent say their field of study requires a degree. Around two in five students (39 percent) say they are looking to explore potential career opportunities. Twenty-nine percent of respondents noted the love of learning as a motivator for attending university. There are other surveys I looked into including Tallo (an online early development networking platform), the National Student Clearinghouse Research Center, and YouthTruth (a non-profit aimed at collecting real-time data from youth to enable actionable and responsive change). They all told the same story. Most students saw the purpose of attending university as increasing their career prospects. The perceived value of higher education may also help inform us as to why students carry certain attitudes toward their learning into our classrooms. The Association of American Colleges and Universities and Bipartisan Policy Center ran a survey of 2200 American adults in 2021. [5] They found that Americans' opinions on the value of a college degree vary greatly by political affiliation, age and income level. “Despite mixed public opinion on the topic, a college degree will almost certainly translate into higher earnings,” said Ashley Finley, vice president of research and senior advisor to the president for the Association of American Colleges and Universities. "Any way you slice it, the probability that you will make a return on your investment is there…Higher education has an image problem, not an evidence problem,” Finley said. Public disdain for elitism and liberalism in higher education and media stories that focus on extreme cases of student debt have pushed the idea that college isn't worthwhile for everyone. Worryingly, Finley thinks this messaging is dangerous because it could dissuade low-income and first-generation college students who would benefit (here, they are speaking of financially benefiting) from a degree from pursuing higher education at all. "That's not a narrative that's going to disadvantage students from wealthier socioeconomic classes -- they will always have the opportunity to go to college," Finley said. What is interesting from this is the continued narrative of the “value” of education as it relates to financial well-being and the necessity of a college degree to achieve this. A college degree has become the gatekeeper of sorts to societal success (both in the perception of and requirement of many career paths). [6] Survey respondents were asked about the importance of a well-rounded education and technical skills to long-term career success. An equal proportion of American adults underscored the importance of each. So did employers; in a separate AAC&U survey, 52 percent of employers said a well-rounded education and technical skills (skills specific to a given job) were essential to career success. The survey also asked respondents about civic engagement and social justice in a college education. Responses to these questions were strongly divided by political affiliation. Just under half of Democrats (45 percent) said that fostering a sense of social justice was important to long-term career success, compared with 28 percent of independents and only 19 percent of Republicans. Kevin Miller, associate director of higher education at the Bipartisan Policy Center and co-author of the survey report. "There's some generational shifts happening in terms of how people see education, which is one of the things that the survey results are getting at. Younger folks are more likely to see education as feeding into ideas of equity and justice, and that's a relatively new idea for a lot of people." [7] While that does not exactly address the question of “what is the purpose of learning,” I think it was worth pursuing this thread as we grapple with the tensions of what it means to be an educator at a university and how AI has shifted societal perspectives of our role as scholars. All of us in this group share a common intrinsic thirst for knowledge. We landed in academia because the pursuit of knowledge, the challenge of creating new ideas, were so ingrained in who we are, we made a career out of it. The point of my shallow dive into surveys and why students want to learn at a university was because, as educators, we owe it to our students to understand and tap into what is driving them. Let’s face it, most of our students are not looking to follow in our footsteps. Roughly, only two percent of the undergraduate population will go on to pursue a PhD. Are we doing enough to help students articulate the value of college beyond its employment and income outcomes? The “we” I use here is not implied to mean only professors or even only universities. I am thinking more broadly about how college, the college experience, and the value of the pursuit of a college degree is discussed at home with family, at high schools with teachers and counselors, and in universities with students. If most graduates think that a college education’s essential value lies in career preparation, then perhaps we (collectively) are doing a poor job of explaining our broader objectives. The purpose of liberal arts education is to provide students with ample opportunities to explore, engage, and as young people, begin to develop their own sense of the world. While this ideal is something I strongly believe in, it can be at odds with the societal perspective of the purpose of a university. I worry that if the perception of the value and purpose of higher education is minimized to “job ready” graduates, we will see a displacement and repression of the liberal arts education to fit this narrative. Further, going back on some of my earlier discussions about access and equity, I also think we need to reconsider how as a society we have placed emphasis on a four-year college education and even gone so far as to making it a requirement for many jobs (and earnings). So what about AI? With a broad stroke, it is obvious to point to higher education’s place in preparing their learners for a world in which AI exists. AI is now a topic that must be learned about and studied upon, just like history, math, writing, and science. I am acknowledging here my own personal belief in AI as a technological disruptor, one at which we must acknowledge and the speed at which is like nothing we have seen before. It took 15 years between Bell Lab’s first transistor (1947) and the first university-level computer science department to open at Purdue (1962). In less than a year (Dec 2024-October 2025), ChatGPT alone went from 300 million weekly users to 800 million, with over 1 billion daily prompts. Can we afford the same literacy lag with AI as we have had in other technological advances? I do believe that places of higher education are uniquely qualified and readily positioned to meet these needs. When we focus on job readiness, higher education’s role is in adjusting our curriculum to include student learning outcomes (and thus student experience) in AI. The challenge many tech-focused programs will have, is keeping up with the ever-changing technology. If we consider the broader learning goals of higher education, we can also see where teaching students how to live in a world with AI is a crucial part of their educational experience. Whether grappling with ethical implications, legality on concepts such as intellectual property, or having students reflect on how AI is shaping their own lived experiences, places of higher education are already having those conversations and learning experiences. This is why it is so important that the value and purpose of higher education does not get reduced to only “job readiness” technical skills. Yes, society needs people who can technically work with and in AI. However, with this expedited path we are seeing in AI technology, we are also seeing the latest in a continued trend in the necessity of regular and continuing retraining. What used to be every 10 years, then 5, is now no more than 2 in our new AI economy. Whether technically trained in AI applications or simply consumers of AI, we also do not yet know what AI is doing to human thinking. That is what is so terrifying. As an engineering educator, I feel a certain responsibility to my students to embrace new technology. I use examples like Plato’s critique on teaching people how to write, “If men learn this, it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written… you will make them seem to know much, while for the most part they know nothing” [8], or Kodak’s dismissal of their employee, Steve Sasson’s, first invention of the digital camera out of reluctance to acknowledge digital photography as a disruptive technology as ways to encourage their curiosity about new ideas and technology. [9] But AI has been different. I have been reluctant. I have been fearful. And I cannot really point to tangible reasons why. Though I will try here. Currently, I teach computational methods to engineering students. Most of the code and solutions to the math problems have existed online in some capacity since I began teaching (and when I was a student). Having students present their knowledge and understanding of algorithms and problem-solving has been a challenge long before ChatGPT. If all I wanted out of my students at the end of the course was to show me how to solve and code these problems, the job would be relatively easy (and AI could be an ally in assisting students in completing those tasks). However, algorithmic thinking does not stop with getting an answer from the algorithm. I want my students to recognize that following a logic flow is not everything required for “good” decision making. Once applied to a system that has people, environments, societies as the end outcome, mathematics and logic are not neutral. My challenge, in attempting to get students to appreciate this aspect of mathematical modelling, is the disconnect students seem to have between their purpose in coming to Wake Forest (for an engineering degree) and what it means to actually be an engineer (problem-solving in the context of dynamic systems where there could be multiple “right” answers but you have to choose the best one). AI cannot help them with that. Well, maybe AI can spit out an answer, but it cannot help them grapple with the decision making. I am not a writer and certainly I do not teach writing, but I teach problem solving. I believe these processes (writing and problem-solving) have many intrinsic similarities. Take a large idea, break it down, get it wrong, learn something new that helps you start over in a different way and eventually communicate what you came up with to someone else. There are incorrect and correct ways to do this. In open-ended problems, like writing, there are many ways for a student to get to a conclusion. Those conclusions may all differ. The argument for how they got there can help dictate correctness or not. As in writing, we have peer review, multiple iterations, and as grammatical errors may clutter a poorly crafted essay, algebra mistakes can make a computational method insufferable. And “real” problem-solving, when an engineer gets to create a solution that has never existed and see the impact that solution has on people or places, while not poetry, has its own kind of beauty. With much of our group conversations focused on what AI will mean for writing and creativity, I often found myself wondering how these same conversations show up in STEM education (or education in general). Will AI undercut critical thinking? How will students’ use of AI impact their ability to learn and understand a method of problem-solving, so that they can use it on new, novel ideas? As in writing, understanding the method, not just producing the correct results is the real essence of what we want them to take away from our classes. The irony is not lost on me as the anxiety of writing this piece has sometimes swelled in my stomach -- if I were a student now, in the age of AI, I would be a student tempted to lean into AI for my writing (and may even be encouraged to do so as a dyslexia accommodation). I think it is easier to bucket students’ pull to use AI through the lens of them not wanting to do the work (or wanting to do the work more efficiently). I was never a student to shy away from work or from challenge, but the thought of my own original work being torn apart, the hours it would take me to craft something I felt was good enough, and the ease at which it seems now to have an AI filter take my words and make them “better” would be tempting. It is tempting now! This experience has reminded me what it feels like to do something I am unsure of or feel ill-prepared for. As an educator, while my initial reactions to student use of AI were dismissive, I’m finding I am convincing myself more that there must be a way forward where, even if I do not fully embrace it, I seek to understand it. I have spent two years engaging in discussions with my colleagues, having to take note of their references to books, writers, and quotes I have never heard of before. I leave each discussion having learned something new. Knowledge I may never use other than for future conversations, but it has brought a richness to my life I would not have had otherwise. We are being offered this ideal of AI in boosting productivity, improving decision-making, minimizing human error, reducing costs, and enhancing user experience. But what are we losing by centering efficiency rather than process? What are we losing in our students’ learning if we remove human error and process from the equation? If AI is changing how we think, surely education’s job is to protect how we reason? What does society lose when human experience is replaced with user experience? What does education look like if we do not pause and ask these questions? NOTES [1] Qargha, G. O., and E. M. Morris. "Why understanding the historical purposes of modern schooling matters today." Education Plus Development. 2021. [2] Sobe, Noah W. "Reworking four pillars of education to sustain the commons." UNESCO Futures of Education Ideas LAB. 2021. [3] Stolk, Jonathan D. "Learning beliefs of engineering faculty and their students: How might alignments and misalignments affect educational change initiatives?" In 2024 IEEE Frontiers in Education Conference (FIE). IEEE, 2024. 1-9. [4] Mowreader, Ashley. “Student Survey Gauges Importance of a College Degree.” Inside Higher Ed | Higher Education News, Events and Jobs. https://www.insidehighered.com/news/student-success/academic- life/2024/02/23/student-survey-gauges-importance-college-degree. [5] Finley, Ashley, Mariette Aborn, Sean Ruddy, and Kevin Miller. "Is College Worth the Time and Money? It Depends on Whom You Ask." Association of American Colleges and Universities. 2021. [6] “Socrates on the Invention of Writing and the Relationship of Writing to Memory : History of Information.” n.d. Www.historyofinformation.com. https://www.historyofinformation.com/detail.php?id=3439. [7] Mui, Chunka. “How Kodak Failed.” Forbes. 2012. https://www.forbes.com/sites/chunkamui/2012/01/18/how-kodak-failed/.
Beyond Job Readiness: The Purpose of Higher Education in the Age of AI
Pat Schneider’s “A Writer is Someone Who Writes” appears frequently on syllabi for first-year writing courses. The essay reassures students that writing well is not a genetic trait, or an accident, but rather the result of extensive and regular practice. As a first-year writing teacher, I know it well. So, upon reading the opening lines, I couldn’t help thinking: of course she is a writer. She wrote! And indeed, despite her disarming opening declaration, "I am not a writer," Erin’s profoundly honest essay reveals that she is just that. Even as she wrestles with the question of whether expending so much time, effort, and vulnerability is worth the end result, she writes on, struggling to fit language to experience. A similar ambivalence confronts all of us in higher education in the age of generative AI. While most would argue, I think, that real learning happens through messy, slow, and “inefficient” processes, it is easy to feel (or be made to feel) naïve and foolish about that view in a culture that venerates efficiency. And so, how do we convincingly argue for the enrichment of process as constitutive of a worthwhile education, when the speedy, seamless creation of products (which AI can do, sometimes quite well) certainly aligns more closely with “real world” priorities? As Erin notes, this cultural pressure gives rise to a rather utilitarian view: an education prepares you for a job and promises financial return. In this context, AI seems an ideal tool for optimization; it excels at speedy, mechanically correct outputs that can tempt frazzled college students. And, if we’re being honest, faculty: testifies to this allure when she considers that using AI would circumvent the "hours it would take me to craft something." Erin’s choice of “craft” here was striking. The ancient Greeks might’ve used the word technē in this context, a word we often translate as skill or technique but which also carries connotations of practical knowledge, as opposed to the scientific or philosophical knowledge associated with the Greek term epistemē, whence our word “epistemology,” or “study of knowledge.” Obviously, I can’t say whether using “craft” here (as opposed to “make,” “create,” or even just “write”) was a conscious choice, but the term carries with it those connotations of knowing and doing. Interestingly, these are the two UNESCO pillars that, Erin tells us, are most important to students. If the point of education (for our students) is, in large part, career preparation, then learning to craft writing ought to be seen as the ultimate expression of what one has learned to know and to do. And yet: it takes too much time. And too much of the kind of iterative, recursive, delete-and-try-again effort that seems wasteful to students who are maniacally checking off their to-do lists. For me, this focus on efficiency poses an existential threat to the art of composition. I teach writing as the very process of thinking, not merely the communication of pre-formed ideas. Erin, an engineer, describes the engineering process in terms familiar to a writer: Take a large idea, break it down, get it wrong, learn something new that helps you start over in a different way and eventually communicate what you came up with to someone else. Critical reasoning is forged in this iterative cycle of error and correction; I would argue it cannot be forged in the absence of error and correction. When I receive student work that lacks a certain human touch yet is mechanically flawless, logically smooth, and rhetorically aware, I find myself asking: does it matter I can no longer discern if a human brain struggled to match those words to ideas? I think it matters quite a lot. Erin’s concern that her engineering students may not fully appreciate from the ethical implications—the hard choices required when mathematics and logic are applied to human ecosystems—is a concern with students’ judgment. The ability to select the best one out of multiple "right" answers, is an act of expert discernment, just as learning to write requires developing one’s capacity for making judgments, large and small, about how well the language one has chosen maps on to the thoughts one is thinking. In most cognitive hierarchies that I know, judgment is consistently placed at the top (e.g., evaluate in Bloom's Taxonomy). It is the culmination of lower-level skills like knowing, comprehending, applying, and analyzing. However, when students use an LLM, they are instantly presented with a near-final product. The cognitive load shifts from knowing or understanding (or even analyzing) to judging whether the output is accurate, credible, useful, ethical, and appropriate for their specific rhetorical situation. This work demands a capacity for discernment that the student may not have. In fact, many modern technologies promote passive acceptance over active evaluation. If we don't teach students to actively judge the AI's output against criteria they understand and can manipulate, they may simply accept the machine’s output. What I call friction in my own essay—the intellectual discomfort required to make these judgments—is not a bug to be worked out. It is the evidence of thinking, judgment, and (dare I say) education. Even faced with AI’s “efficiencies,” students must be able to exercise their own reasoning and judgment. They should also understand that efficiency is not a neutral or objective term; it is a term whose connotations are tied, inextricably, to the logics of capitalism. Our work as educators is to actively adapt AI technology to our human needs, rather than passively fitting our world and work to it. My colleague's decision to embrace the anxiety and produce a deeply personal essay offers a model. The ultimate purpose of education is not the optimized product, but the fully realized, critically reasoning human being who can, to paraphrase F. Scott Fitzgerald, hold two conflicting ideas in mind and still function. For example, AI can be both efficient in a market sense and wildly inefficient for learning. As educators, we must equip students with the internal compass and cognitive resilience necessary to correctly judge the value of an AI output (vs. the value of doing the work ourselves) and to trust in those judgments.
A Response to Erin Henslee’s Essay
At last count, I have taught around five dozen university-level writing courses. To say I was dismayed at the arrival of generative artificial intelligence on the scene in late 2022, is an understatement. To me, ChatGPT and its cousins feels menacing—and not just to my career. Theorist Kenneth Burke famously defines humans as a “symbol-using (symbol-making, symbol-misusing) animal,” and implicit in that definition is the idea that we choose our symbols, with intention and regard for context, because we are motivated to use rhetoric to induce action. [1] We may sometimes choose the wrong symbols, but we use them because we want to make something happen. With large language models like ChatGPT, we may not be choosing anything. Watching the (to me) unreflective adoption of LLMs unfold in real time in my classes left me searching for ways to understand what was happening. Disoriented, I sought analogous moments from my own life to “understand[] and experience[e] one kind of thing in terms of another,” and put this seismic shift into perspective. [2] On July 16, 2015, my son Ethan was born, displaying some of the characteristic features of Noonan Syndrome, the genetic disorder doctors had been whispering about since my 20-week ultrasound had revealed concerning features including short femurs, thickened heart walls, and extra fluid in his kidneys. Noonan Syndrome is rare (occurring in approximately 1 in 2,500 live births), and it’s especially rare when neither parent has it, as in our case. However, as we learned in the crash course in genetics that accompanied Ethan’s early days, most of us have all kinds of errors in our DNA and most cause no problems. Genetic disorders familiar to many of us, like Down Syndrome, usually involve massive changes in DNA: duplications or deletions of chromosomes in whole or large part. And then there are some, like Noonan Syndrome, that arise from what are called “point mutations”: a single nucleotide base deletion, addition, or substitution. A few weeks after birth, molecular testing confirmed Ethan’s condition. He had an adenine where a cytosine should have been on his PTPN11 gene which, it turns out, is a really big deal. While much remained unknown about the course Ethan’s life would take, we did know that every single cell in his body had a rather catastrophic typo in its instruction book. That typo would throw a wrench in . . . well, everything. When I look at some of Ethan’s features—like his wide-set, ice-blue eyes (his dad and I have dark eyes) or his low-set ears—I wonder where exactly the process of eye or ear development got thrown off. Is “off” even the right word? After all, his eyes are eyes that see. His ears are ears that hear. But their particularity reveals a glitch in the system. One of the things I had to come to terms with, as I got to know that little boy, was that he was no changeling; there was never a “healthy” boy who somehow got stolen away. Genetically, he was always exactly who he is now. The terrifying part was not the finality of the genetics report but knowing we had been set on a path we had not chosen, could not reject, and over which we had very little control. We couldn’t foresee its effects on our lives, but our course was fixed. The Genetic Metaphor: AI as Encoded, Inevitable Early in the spring semester of 2023, my first-year college students were suddenly turning in papers with no grammar mistakes. Not fewer grammar mistakes—none. Much as teachers might lament students’ comma splices and misplaced modifiers, they are often evidence of a developing human brain. Signs of a person struggling to fit previous writing knowledge into a new context. Surface-level problems show that learning is happening, that students are trying to say something, even if they don’t quite have command of the tools yet. The surface I could see, that is, showed me something of what was going on underneath. In genetics, they call this a correlation between phenotype and genotype. In student writing, errors like breakdowns in syntax revealed problems with logic and reasoning; grammar and diction mistakes might indicate incomplete command of register and style. Now, those vital clues were erased. If I had felt some resonance between managing Ethan’s disorder and “managing” my students’ (pre-generative AI) writing challenges, the experiences became psychologically linked for me in another way when generative AI entered the picture. Once again, I had stepped unknowingly (and to a degree unwillingly) into a new reality I felt I had to accept. This new reality prompted me to spend a frantic summer overhauling my syllabi and assignments, just as Ethan’s first year made me rethink all I thought I had learned parenting his older sister. But as I rewrote my plagiarism policy for what felt like the hundredth time, I felt my metaphor beginning to break down. Unlike a genetic disorder, AI is not the inevitable outcome of some agent-less process, nor am I obligated to reorganize my professional life around emergent technology. We had few choices when it came to responding to Ethan’s medical needs, but that wasn’t true here. Recognizing my own agency moved me away from associating LLMs with a pathogenic mutation that would wipe out writing instruction, maybe even writing, as I knew it. Instead, thornier questions began to emerge . . . does it matter whether a human is behind these words if we can make algorithmically derived writing indistinguishable from human-authored writing? Some scholars see such worries as manifestations of an “anthropocentric bias, judging artificial intelligence only to the degree it resembles a human’s.” [3] The problem, they reason, isn’t whether writing is AI-generated or not; the problem is that AI-generated writing unsettles bedrock ideas about human agency and authenticity that have underwritten the value we place on human creativity. Without this fundamental centering of the human, we must make a case for ourselves, as it were, and explain why human-authored writing is qualitatively different than computer-authored writing. This line of thinking gets philosophical quickly, but more prosaically, it threatens to destabilize dominant writing pedagogies that emphasize the social dimension of writing and judge writing effectiveness based on how well it achieves its purpose vis-à-vis its intended audience. Approaches like this encourage writing teachers to focus on so-called “real world genres” and make claims for the universal utility of writing instruction. But if an LLM can generate something that accomplishes a given purpose, who is harmed if a human only engineered the prompt? Is that just a new kind of writing? Do we simply need to create new frameworks for understanding and valuing writing produced collaboratively by humans and machines? What is writing for, anyway? My questions very quickly moved from the practical to the pedagogical to the epistemic and even ontological. The Pathogen Metaphor: AI as an External Threat In early 2020, reports of a mysterious respiratory virus circulating in China grew in frequency and urgency. I had never heard of Wuhan, but images of endless rows of cots, surrounded by people in hazmat suits, were unnerving. I remembered the first SARS outbreak, the swine flu outbreak, the Ebola outbreaks in what was then Zaire and elsewhere. Scary, but distant, and all had mostly fizzled out, so I monitored my kids for sore throats and hoped that a coordinated effort to contain the virus would succeed. Still, as the outlook worsened, so did my unease. After college, I lived in Vietnam for a year and knew how unsanitary and wild those open-air markets are. Thus, it did not entirely surprise me that a virus made its way from the jungle into human lungs via a similar market. And for all the talk of a “novel” virus, this one wasn’t entirely novel; it had been doing its viral thing in other host animals for years and had plenty of coronavirus cousins we were acquainted with. It, or something like it, was always with us, just hidden from view. Who knows whether its eventual emergence and species-jumping could have been forestalled. One of the things we all had to come to terms with, as cases arrived on American shores and maps with ever-enlarging red dots showed expanding outbreaks in Seattle, Los Angeles, New York, was that a pandemic was inevitable. Was upon us, even. Like Ethan’s official diagnosis, the die had been cast without human agency or decision and there was no un-casting it. I felt the weight of that mind-numbing refrain in the children’s book We’re Going on a Bear Hunt, we couldn’t go under it. We couldn’t go over it. (Oh no!) We had to go through it. In late March of 2020, schools closed and we didn’t welcome our students back from spring break. We muddled through a sloppy transition to online work, learned to make sourdough bread, and walked the dog for hours on the greenway. I finally put all the laundry away, spent long afternoons playing Uno with my kids, and baked a lot of cookies. We convinced ourselves that online learning could be transformative, made signs that said, “we’re in this together” and, starved for normal social contact, organized socially distanced happy hours in our driveways. We felt hopeful about what collective action (or, more accurately, collective inaction) could do to flatten the curve. That grit-your-teeth sense of optimism also characterized my early reactions to the arrival of LLM technology. By the time I knew about it, it was already everywhere. Resistance was (and is) framed as futile, despite higher ed’s efforts to present itself as well-poised to navigate an educational landscape that includes LLM technology. Somewhat rare is the professor who, like Danielle DeRise, admits that “years are needed to conduct comprehensive studies examining the role of LLMs in the learning process;” most are eager to show how readily they’ve pivoted, or at least how ready they are to pivot. [4] No one is making a persuasive case for doing business as usual. Metaphors of virality and contagion frequently describe popular uptake of trends, fashions, political slogans, and other cultural phenomena. Our easy deployment of these rhetorics of infection signals the degree to which we feel both threatened by and somewhat powerless to resist elements of digital culture. As with the pandemic, our mitigation and avoidance efforts can only do so much. At a certain point, just as we are powerless against certain actual pathogens, we at least feel powerless in the face of a technology that reached one million users in just five days. However, the viral rhetorics don’t perfectly describe how generative AI entered or will continue to shape our lives. Unlike a congenital problem, we aren’t born with Covid19; we are just born into a world where Covid19 (and LLMs) already exists and where we are very likely to encounter it. Further, generative AI is not pathogenic like a virus; it does not physically alter or damage our bodies (though its effects on our brains remain to be seen). We will not become “immune” to it in the sense of being less affected or rendered less ill, though we may become so habituated to it that we no longer notice new applications or, more frighteningly, we forget how to do the tasks that generative AI can do quickly and (seemingly) seamlessly. And to complicate matters, generative AI is not all bad. It can be used for good, and it can be fun, which no one would ever say about Covid. The motivation to avoid a respiratory virus is self-evident; the motivation to avoid generative AI is decidedly complicated. I still keep hand sanitizer in my purse, masks at my desk, and a spare Covid test or two around the house. But while I may groan in frustration when I receive a paper very obviously generated by a chatbot, my position has, I think of necessity, moderated. As the weeks rolled on, my students’ writing came in and the Atlantic churned out think-pieces on the effects of generative AI on education and creativity . . . my own thinking about LLMs changed, too. I realized that my first two attempts to understand LLMs—first as a kind of encoded, inalterable fact of existence that we had to suffer through, and then as an externally imposed danger—were both flawed. Neither offered a satisfactory framework for imagining a path forward and both placed me in a passive, reactive mode. That mode is familiar, if a bit uncomfortable, since technology in our time advances far more rapidly than we adapt to it or make reflective, informed decisions about its best use. New technologies are released, integrated into daily life, and monetized before anyone has a chance to understand their downstream consequences (e.g., the devastating effects of social media on teen mental health). Perhaps we should better predict the fallout, but history suggests that we don’t, or we ignore signs of impending disaster. Instead, “adaptation” in this context usually means we gradually adjust to changes but (or because) we are not willing or able to refuse them. Indeed, we rarely refuse technologies (like self check-out, driverless cars, contactless payment options, even ATMs) that reduce or eliminate the need to interact with another human being. In so much of our digital landscape, other people are constructed as a problem to be overcome, unnecessary friction to be avoided. And if we adapt to generative AI by allowing it to take over more and more of our communicative efforts (including, if my students and children are to be believed, using ChatGPT to ask your crush to the dance or write an email to a friend you’re mad at), we place more layers between us and other humans. On the other hand, adapting generative AI by fitting it to our human needs could compel us to reassess what is valuable and irreplaceable about human interaction. What if we took up other meanings for “adapt,” and remade generative AI fit our world and work, rather than remaking our world and work to suit AI? What if we adapted this technology the way a screenwriter adapts a novel–-retooling a story for a new genre, jettisoning what doesn’t work, elaborating on what does? I ask these questions because, as I noted, rejecting or banning generative AI altogether seems like a fool’s errand. My own much-revised syllabus plagiarism statement admits that sweeping bans on generative AI are neither practical nor sustainable, even if the status quo ante is not feasible. However, I share composition instructor Danielle DeRise’s conviction that the experience of a writing class matters as much or more than any specific writing lesson. As she writes, “LLMs cannot (yet) replace the spontaneous dialog that wells up in a classroom of students who have grown to trust one another and their instructor.” [5] Creating and sustaining that community is—and remains—a vital part of my teaching. Nevertheless, even if “digital tools are but the latest take on a long tradition of writing in transit,” there is no denying that generative AI is shaking the ground on which writing pedagogy has long stood. [6] And so, what must change in my teaching practice? Writing As Choice, Selection, Agency When I teach first-year writing, I frequently assign an essay by Mike Bunn called “How to Read Like a Writer.” [7] He encourages students to study writing the way an architect or builder studies a house; that is, not as the finished, usable, aesthetically pleasing object, but as the final assemblage of many related decisions about foundation type, framing, structural supports for transitions (doors, windows, staircases), and finally surface-level decisions about floor coverings and paint colors. Perhaps we torture the metaphor a bit, but it holds: if your foundation cracks or your framing isn’t sound, it doesn’t matter how pretty your light fixtures are. A polished piece of writing, I tell them, is not an accident nor is it a natural, effortless outcome of inspiration; it, too, is the result of a long sequence of interconnected choices, made within some constraints. Like the codes that govern wiring and plumbing, conventions of grammar, mechanics, and syntax do dictate some things, but many choices are left up to the writer. What this means, then, the errors I suddenly missed in my students’ drafts, the ones indicating their writing choices weren’t quite matching their communicative intent, were no longer a reliable index of what students were thinking. And this had implications far beyond a single essay. While interpreting these errors as marks of “deficiencies” creates its own set of problems, it also exposes the reality that the university, for better and worse, expects student writing to conform to the conventions of standard academic written English. [8] To use the jargon of my field, incoming college students were entering new “discourse communities,” [9] which the linguist John Swales defines as groups that use a shared lexis, set of genres, and other conventions to accomplish their goals. Incoming students are sometimes expected to perform as if they are already members of academic discourse communities, and they invariably make some missteps when they are expected to deploy the discursive norms and conventions they have not yet learned. Composition scholar David Bartholomae details this process in “Inventing the University:” without explicit instruction, students will have to fake it till they make it, even though their audience—their professors—exert considerable power over them in the form of grades. [10] Students’ false starts gave me a place to begin, as a teacher, but most vitally, they showed me intention—intention to say something the way they think I (as a representative of this privileged academic discourse, the “native” they want to speak to) want to hear it. A few months after the arrival of ChatGPT, though, I started to doubt that my students were trying to say anything anymore. Their writing had a sanitized quality that I compared to the airbrushed photos on their social media profiles, all human imperfections erased. Like a good Instagram filter, the chatbots had made for a smooth and unblemished surface, but unlike the photos, I wasn’t sure what lay underneath this veneer. While one could reliably say, before, that their photos were at one point likenesses of them, I wasn’t sure the same could be said of their writing now. Was there a real, human consciousness underneath those words? Had a human brain struggled to match those words to ideas? Or were these sequences of fetched tokens, these algorithmically-determined assemblages of texts, just a pretty surface? And most distressingly—did it matter? I struggled to describe what I was reading. There is something uncanny about AI-generated writing, to be sure. The sentences flow with a practiced ease, the vocabulary is often sophisticated, and the grammar is typically flawless. Yet a certain hollowness emerges: no telltale stamp of human authenticity, no unexpected turn of phrase, clever analogy, or personal insight. While the AI-produced writing sometimes contains errors, they are not the ones usually humans make. So I turned again to metaphor: maybe AI-generated writing is like Frankenstein’s monster: expertly stitched together, reanimated with an artificial spark, yet betraying the unnaturalness of its birth. Or perhaps it is more like zombie writing: writing that has been “fed” by the products of other human brains but which is incapable of life itself. Some students’ apparent readiness to accept—and even prefer—this lifeless form of writing reveals to me the triumph of problematic pedagogies (and grading standards) that perpetuate the false dichotomy between “form” and “content” in writing. When students are graded separately on the supposedly substantive aspects of their writing (“argument” or “evidence”) and on the supposedly formal or conventional aspects (“structure” or even “mechanics”), they learn to think of these things as separate: that an idea can exist outside of the form which expresses it. (Well-intentioned rubrics can perpetuate this notion by suggesting these elements of writing are not interdependent and can be assessed separately.) Writing thus becomes a matter of “dumping” ideas into prefab containers (like the five-paragraph essay) rather than the coordination of one’s purpose as a writer, the constraints of one’s situation, and the expectations of intended audiences. [11] Of course, any writing teacher worth their salt will tell you that form and content are inseparable. We can have impressions or sensations, but we can’t articulate any thoughts outside the rules of grammar, syntax, language itself. In a way, language is the DNA of communication: it determines how and when stuff gets made. AI-generated prose erases the traces of mental effort–false starts, redundancies, inconsistencies, mistakes—that we have relied on as telltale markers of a meaningful, authentic writing process. But not all students agree that such a process is in fact meaningful or very valuable. Some might agree with this one who, after participating in a writing-with-generative AI experiment, claimed the AI helped him articulate what he already thought. As he said, “I had all my ideas in my head but sometimes my brain just blanks out at how to write coherent and easy to read sentences.” [12] This sentiment echoes one I hear from my own students: “these are my ideas; [favorite AI tool] just helped me get them into sentences.” But I question whether this is truly the case. Perhaps we are just too willing to accept some imprecision if it saves us some time and effort. But what else is short-circuited when we bypass this painstaking effort of aligning thoughts with language? More pointedly, “what else, besides learning, is lost if (or when) we no longer appreciate the very human struggle to communicate?” [13] After all, for most writing teachers, the end goal is not only error-free prose, but the process itself: the opportunity that writing affords to figure out what we think. Like Michelangelo, who imagined his role as a sculptor to be one of removing “excess” stone to reveal the figure beneath, writers sometimes need to excavate our real point by producing a whole lot of language we end up discarding or changing beyond recognition. We deepen and enrich our thinking when we try language on it, when we imagine someone receiving that language. Furthermore, there is a necessary process of maturation that happens alongside this sort of writing development: students learn that ideas must be presented in ways that can be heard by their intended audiences. Knowing that a student can, to a large degree, command standard academic written English is to know that that student can navigate and participate in the life of the university. In other words, requiring this sort of conformity is not only a hegemonic move, it also helps students access the world to which, at least for the next four years, they want to belong. It also parallels the necessary register and style adjustments we all must make, constantly, to negotiate life in a diverse society. Still, once something is on the page (and particularly on the screen), it becomes difficult to imagine anything else being there. AI-generated prose often looks polished on the first try, so with one fell keystroke, AI answers to our twin cultural imperatives of efficiency (or at least speed, which we often confuse with efficiency) and surface-level perfection. For someone who makes her living teaching people to write, a tool that produces passable prose in seconds feels like an existential threat. But it also seems like a logical—if not desirable—telos for an approach to writing instruction that, for decades, compelled students to construct rhetorically untethered essays to perform competence with standard academic English. For instance, the clichéd first day essay prompt like “what I did for my summer vacation” fails because students all knew that the teacher never really cared how they spent their summer, nor did they imagine that story would be relevant to the course’s goals. But the exercise provided the teacher with a sample of students’ ability to string sentences together into a coherent narrative. Many other prompts are simply tests (can you explain the Krebs cycle?) or surveillance mechanisms (summarize that chapter I assigned you to read) masquerading as writing tasks. Most of them are only a question of reporting knowledge, which is not necessarily a bad way to use writing. However, students with many competing priorities may not see such tasks as worthwhile investments of time and labor if an LLM can report the knowledge just as well, with far less friction, for the student. After all, such tasks are not really about the student’s perspective or intellectual growth–they are meant to verify that knowledge acquisition has happened. AI-generated writing will only produce the knowledge; it cannot verify the student’s acquisition of it. It’s a flawed measure of what students have learned. In fact, Steven Krause argues that abandoning such essays would be a good thing; as he notes, “there is a big difference between assigning students to write a “college essay” and teaching students to write an essay.” [14] Which leads back to my original question: what is writing for? Friction—the necessary difficulty in attaching words to ideas, thoughts, experiences—might actually be the point. Friction isn’t a bug to be worked out; it’s the proof that intellectual labor is happening. The writer Anne Carson claims that writing is about “dragging a thought over from the mush of the unconscious into some kind of grammar, syntax, human sense; every attempt means starting over with language, starting over with accuracy.” Starting over, rigorously engaging with language, is precisely the step that chatbots can allow writers to skip and, along with it, the constant negotiation between the constraints of language, situation, and an individual mind with something to communicate. The admittedly sometimes tedious and frustrating process of matching language to thought, forcing our ideas into a grammatically and syntactically workable structure, is not a mark of failure or imperfection or inefficiency; it’s precisely how we see what we think and what we know. As E.M. Forster reportedly asked, “How can I know what I think until I see what I say?” Thoughts don’t spring fully formed like Athena from Zeus’s head. Rather, writing negotiates the necessary gap between thought and articulation. AIs, however, can short-circuit that negotiation. If an AI serves up something close enough, the words we don’t choose ourselves are not the real loss. The real loss will be the thoughts unexplored, the connections unmade, and the potential slide into a passive acceptance of what is, at least for now, no more than a long chain of algorithmic predictions, not the making of meaning. Of course, many people are not using LLMs to write for them. When I’ve polled my students, the most common uses of LLMs are to generate ideas (or even a very rough draft) or to revise and edit after the student has written a draft. And some, maybe most, are using it at both stages. Some writers, according to Katy Gero in a Wired article, use AI to generate some content, but that doesn’t mean they get out of writing anything. Instead, seeing one possible idea “reignites their interest in their writing.” [15] It gets them over writer’s block. I might add that it forces them to start to make some choices, even if the first choice is simply “not that.” And some have argued that using an AI to revise is not that different from asking a person to read your writing and offer feedback, except you won’t be offended by a chatbot’s critique and you can ask it at any time of the day or night and it’ll respond within seconds. A friend or colleague might take a week or longer, and they might not tell you what you need to hear for fear of hurting your feelings. But isn’t this one more example of the tech industry cynically capitalizing on our aversion to socially risky behavior or conflict, by once again constructing other people as the problem and itself as the solution? Given my line of work, it’s hard not to feel defensive yet also defeated in the face of these arguments. Part of me thinks, if using these tools gets more people to write, to continue writing, and to take their writing seriously enough to revise it, then perhaps that’s a win. It’s more of a win than deciding that writing is dead because the robots are going to do it for us, as writers like Daniel Herman in The Atlantic, Hua Hsu in The New Yorker, and lots of Medium contributors proclaimed in the last few years. And while Sandra Jamieson notes that, strangely, institutions have thus far “not seen [the advent of generative AI] as a crisis composition faculty are called upon to solve,” [16] Gavin Johnson reminds us that we have “decades of scholarship and pedagogy” to draw on in response to the integration of AI technology. [17] Asking “how do I detect AI-generated prose?” or “how do I prevent students from using it altogether?” creates an unpleasant surveillance culture in the classroom, fostering mistrust between students and teachers. Instead, we need to invite students into conversations about authenticity, agency, citation, and creativity so that we can ask more interesting questions: why do you write? Can AI help you get that? How? Teaching writing has always felt a little like swimming upstream. When so much of our information landscape prioritizes (and prizes) speed, brevity, and efficiency, it is hard to argue compellingly for the value of slow, laborious writing. As education has become ever more transactional—a means to a pragmatic end—intellectual tasks have become merely boxes to tick. In such a landscape, wading through inquiry and reflection can seem not only tedious but truly counter-productive, since it prevents moving through the to-do list. What would motivate anyone to slog through a recursive, analog writing process? Because writing well, with intention, means messiness. It means starting over, deleting, rewriting, asking others to read work and give you honest critiques. Such interactions can involve vulnerability, discomfort, and unpredictability—all of which, our culture tells us, might be painful and therefore should be avoided. A chatbot can passably do (or assist with) a lot of these tasks, but its output is a recycled and rearranged mash-up of the data it was trained on. You might decide AI output is close enough, but you can never say that is what I think. Writing is to thinking what refining is in metallurgy. . . the product is always going to be finer, more beautiful, more polished, more pure for having gone through the fire. — I began this essay while sitting in the surgical waiting room at the UNC Children’s Hospital, where I have spent far too many hours, waiting for Ethan to wake up from his umpteenth surgical procedure. Being the parent of a medically complex child has taught me many lessons, one of the most enduring of which has been awareness of and gratitude for my own healthy body. A body that generally does what it should with little complaint and very infrequent attention from medical professionals. Ethan’s extremely regular contact with the medical establishment reminds me, over and again, of our inescapable embodiment. The Covid19 pandemic reminded us all that our bodies are fragile. Even those not at high risk of severe illness probably felt some anxiety at being at the mercy of their immune systems. And yet, life had to go on, so many of us turned to Zoom and other tools to muddle through school and work while our bodies figured out how to deal with this unknown germ. And in that instance, language was useless: we could not talk our bodies into immunity, or reason with the virus. In other words, as the “symbol-using animals” described by Kenneth Burke, we were remarkably powerless, just as my family was powerless to change Ethan’s genetic diagnosis. In both cases something was happening to us. It was natural to speak of both events as crises, and it is easy to attach that same discourse of crisis to the arrival of generative AI. LLMs appear to promise power; they traffic in the linguistic currency and rhetorical forms that are valued in high-prestige contexts like the academic and professional worlds. They can facilitate entry or even belonging to these contexts (even if that means erasing markers of linguistic identity). Perhaps most beguilingly, they give us time. Instead of spending a whole weekend on an essay, a student can spend 30 minutes (maybe). And this poses a real challenge for us as educators. In the transactional, fiercely competitive world our students operate in, an A is simply worth more than a B-. When students can produce A-level (looking) work with a few keystrokes, how do we persuade them that doing the hard thing and maybe only earning a B- is worth it? That act of persuasion is, to me, the necessary challenge of teaching in the age of generative AI. It’s bucking achievement culture and asking students to reject the many cultural messages telling them that cognitive discomfort is inherently harmful. It’s helping students to accept that learning is, and should be, a little bit painful. It means working ever harder to create a classroom atmosphere where intellectual risk-taking is worth it. Muscles must tear a little bit in order to grow . . . but AI is like an anabolic steroid. It’ll give you the results you want, much faster than you can get them on your own, though the side effects might be dire. Or maybe it’s like letting a robot shoot free throws. It’ll run up the score, though no one can say who earned that win. But it is hard to think about those consequences when the immediate reward beckons so alluringly. While once the purview of apocalyptic science fiction (2001: A Space Odyssey, Her, even Wall-E), AI has been creeping into our lives for years. AI in the form of auto-correct is embedded in our word processors, our email, our text and other messaging apps. We rely on GPS to get places, even in our hometowns. We ask Siri to play our favorite songs. AI-generated responses quietly appeared recently as the first response in a list of Google search results. The aura of inevitability around AI is so powerful that skeptics find themselves patronized and even pitied for their reluctance to get on board. That ship, so they say, has sailed. This argument, that something once set in motion is inevitable, is compelling not because it’s correct but because it's disempowering. AI is not a genetic disorder. It is not encoded in who we are. It is also not a novel virus, mindlessly replicating in host cells and utterly impervious to reason or our conscious choices. Despite many AI scientists’ admissions that they don’t know exactly how some AIs work, AI remains a human invention, and we remain people with agency who can make choices. We can opt out. We can push back against defeatist arguments and decide that circuitous routes and even dead ends are not a waste of our time but rather compel us to make conscious, better-informed decisions about what we say, how, and to whom. We can ensure that what we say is perfectly attuned to its context, rather than simply the most likely next thing based on what has been said before. AI evangelists claim AI (and other technologies) will “free” us from drudgery, liberate us from brainless tasks or unnecessary labor. And to some extent, it might. But where will we draw the line? What work will be worth doing? In “Why Write,” the novelist Paul Auster juxtaposes five unrelated vignettes, some personal and some not. Most include an unlikely coincidence, twist of fate, or highly improbable event. The final story recounts a time he, lacking a pencil, blew a chance to get Willie Mays’ autograph. Readers conclude that writing—and, importantly—being prepared and having the tools to write—is a crucial way that humans make sense of what happens to us. Narrative especially helps us to create structure and coherence and to believe that events happen for a reason. Such meaning will always be inextricably linked to the context and particularities of lived experience, which an AI simply cannot have. My students love this reading. They love Auster’s style, his descriptions and most of all they love diving so completely into the worlds of each story. When I ask what holds the pieces together, they are quiet a minute, but eventually they arrive at “coincidence“ or “irony.” Then I ask whether coincidences are inherently meaningful, and they try to convince me that such inexplicable, ironic twists must mean something. But ultimately they admit that no, there is no meaning to experience except what the writer makes of it. And I smile. Last year, a student noted that my class wasn’t so much a class “about” writing as a class about “how to think about writing.” An offhand comment from him, perhaps, but for me as a rather profound recommendation about shifting my focus. The way I have taught will probably no longer suffice; old rationales for writing no longer persuade. We might, though, consider that being motivated to write—or do any creative act—is a matter of being enticed by one’s own mind: to see where things lead, to, to find the meaning you didn’t know was there. We write–we need to write–to attach some sort of order, sense, and meaning to the arbitrariness of experience. An AI, by definition, has no experience. It cannot attach meaning to anything. And so, what, really, has it to tell us? NOTES [1] Burke, Kenneth. Language as Symbolic Action: Essays on Life, Literature, Method. University of California Press, 1966). 16. [2] Lakoff, George & Mark Johnson. Metaphors We Live By. University of Chicago Press, 1980. 5. [3] Fyfe, Paul. (2021). “How to Cheat on Your Final Paper: Assigning AI for Student Writing." AI & Society. 38: 1395-1405. https://doi.org/10.1007/s00146-02201397-z. [4] De Rise, Danielle. (2024). “Will I Even Teaching Writing Anymore? An Examination of First-year Writing Faculty’s Responsibility to Teach About or With ChatGPT.” International Journal of Responsibility. 7.1: article 3, https://doi.org/10.62635/2576-0955.1108. [5] DeRise, 11. [6] Micciche, Laura. “Writers Have Always Loved Mobile Devices.” The Atlantic Monthly, August 18, 2018. https://www.theatlantic.com/technology/archive/2018/08/writers-have-always-loved-mobile-devices/567637/. [7] Bunn, Michael. "How to Read Like a Writer.” In Writing Spaces, vol. 2., ed. by Charles Lowe and Pavel Zamliansky, WritingSpaces.org. Parlor Press, The WAC Clearinghouse, 2011. 71-86. [8] See Mina Shaughnessy, Errors and Expectations. Oxford University Press, 1977. [9] Swales John. “The Concept of Discourse Community.” In Genre Analysis: English in Academic Research Settings. Cambridge UP, 1990. 21-32. [10] Bartholomae, David. (April 1986). “Inventing the University.” Journal of Basic Writing. 5.1: 4-23. [11] Because these sorts of ineffective assignments are so commonplace (and so readily accomplished by generative AI), some scholars have even suggested that generative AI is the “impetus we need to turn back to a focus on writing itself, informed by all of the other turns we have made.” Maybe, that is, we will start teaching students to write instead of just assigning writing. [12] Fyfe, 1400. [13] DeRise, 11. [14] Krause, Stephen D. “AI Can Save Writing by Killing the ‘College Essay.'" Steven D. Krause (blog), December 10, 2022. https://stevendkrause.com/2022/12/10/ai-can-save-writing-by-killing-the-college-essay/ . [15] Gero, Katy. “AI Reveals the Most Human Part of Writing.” Wired, December 2, 2022. https://www.wired.com/story/artificial-intelligence-writing-art/ [16] Jamieson, Sandra. (2022). “The AI ‘Crisis’ and a (Re)Turn to Pedagogy.” Composition Studies. 50.3: 153-58. [17] Johnson, Gavin. (2023). “Don’t Act Like You Forgot: Approaching Another Literacy ‘Crisis’ by (Re)Considering What We Know about Teaching Writing with and through Technologies.” Composition Studies. 51.1: 169-75.
A Response to Erin Branch’s Essay
“We are our choices.” —attributed to Jean-Paul Sartre in many places online, but never appears in his work “One must be conscious in order to choose, and one must choose in order to be conscious. Choice and consciousness are one and the same thing.” —Jean-Paul Sartre (“Being and Doing: Freedom” 595) [1] One of the most evocative strands in a braided essay that draws so thoughtfully and convincingly upon a range of her personal experiences, Erin Branch’s recollections of the COVID-19 pandemic struck me as particularly salient to one of the overarching themes she challenges us to consider: choice. When professors, among many other workers both inside and outside of education, were tasked with moving their work entirely online during the lockdowns, that change was often (and continues to be) described as a shift in “modality.” Of course, a change in mode of delivery or medium can be described this way, and in writing studies we regularly talk about, teach, and deploy multimodal forms of composition to suggest a range of possibilities for meaning-making. I can’t help but think, however, of another meaning of “modality” that is intimately connected with language: what MIT linguist Kai von Fintel defines as “a category of linguistic meaning having to do with the expression of possibility and necessity.” [2] When we think about modal verbs, for instance, we understand that there can be worlds of difference between something that we can do, something that we should do, and something that we must do. Questions of possibility and necessity haunted the response to the pandemic, and the downstream effects of that disruption continue to be felt in so many ways (including in our classrooms). Such questions arguably linger at the edges of all of our decision-making in one way or another, but there is a particular urgency right now in considering which “modality” we choose to describe our current circumstances. Erin asks us to understand GenAI as neither a total(izing) fait accompli nor as an entirely open question. It is here, after all. But its existence does not foreclose all questions and rob us of choice—we can and should consider what might be good or bad for us rather than deciding we simply must go along with whatever seems expedient, convenient, inevitable. What Erin powerfully reminds me—reminds us—is that there are times in which we are called upon to act, to survive, to love one another under circumstances over which we have little or no control. This is unavoidable. How we think about such things matters. By not only arguing for this kind of perspective but also enacting it by blending her personal experiences and reflections with her professional perspective in ways that only a person can, Erin shows (not just tells) us that language is more than mere information and that experience, even (maybe especially?) when difficult, counts. If we train ourselves, and more importantly, if we train our students to studiously avoid the kinds of difficulty that attend self-understanding, communication, and connection, we may all find ourselves lacking in the capacity to wrest meaning and make order from the chaos we encounter in a life. In other words, we may no longer be able to write and think for ourselves. Older ways of thinking and talking about writing may no longer suffice, and teaching methods and justifications may need to adapt to new realities. The underlying importance, however, of using language to interpret, describe, and communicate our worlds to one another seems as important to me as it ever has been. Perhaps even more so. Despite her clear skepticism, Erin is slightly more generous about the positive possibilities of GenAI than I am, and I value that perspective. It reminds me that the future (and not just the future of this technology) is yet unwritten. It reminds me that whatever that future will look like when it comes to AI, it will be the consequence of choices made by people. Especially the young people who are currently in our classrooms. Helping them see that the world they are on cusp of creating is something they have the power and responsibility to shape, that while some things are inevitable, our choices, both rhetorical and otherwise, do matter: that is, to me, the important work of teaching in this time. NOTES [1] Sartre, Jean Paul. “Being and Doing: Freedom.” Being and Nothingness: A Phenomenological Essay on Ontology. Trans. Hazel E. Barnes. New York: Washing Square P, 1956. 559-711. [2] von Fintel, Kai. “Modality and Language”. in Encyclopedia of Philosophy. 2nd ed. Ed. Donald M. Borchert. Detroit: MacMillan Reference, 2006. http://mit.edu/fintel/www/modality.pdf
Erin Branch is Teaching Professor and Director of the Writing Program at Wake Forest University, where she primarily teaches first-year writing and classes focused on rhetorical history and theory. She holds a PhD and MA in Rhetoric from UNC-Chapel Hill and a BA in English from Middlebury College. When she's not writing or thinking about writing, Erin can be found either experimenting in her kitchen or mucking around with some or all of her four children, one dog, and small flock of backyard chickens. Tobias Flattery is Assistant Teaching Professor of Philosophy in the Department of Philosophy at Wake Forest University. He teaches and writes on topics in the ethics of emerging technologies and in the history of philosophy. In technology ethics, he works on questions concerning the ethics of our actions and representations in digital spaces, the design and deployment of robotics and AI technologies, and competitive esports. In the history of philosophy, he works primarily on Leibniz's metaphysics. He earned his PhD in Philosophy at the University of Notre Dame. Prior to academia, he worked in the private technology sector as a data warehouse engineer and business intelligence analyst. Dr. Erin Henslee is a Founding Faculty and Associate Professor of Engineering at Wake Forest University. Her research spans biomedical engineering, STEM education, and Makerspace pedagogy. She also serves as co-director of WFU’s CULTIVATE program which supports faculty as they pursue external funding. Prior to joining Wake Forest, she was a Researcher Development Officer at the University of Surrey where she supported Early Career Researchers working in the area of inclusive researcher development. She has taught over 20 different engineering courses across a variety of institutions and departments. She is also passionate about open education resources (OER) and open pedagogy, co-running HiddenSTEM.wfu.edu. Her teaching awards and grants related to inclusive pedagogy include a 2020 Engineering Unleashed Fellowship, KEEN’s 2023 Rising Star award, and an NSF project on developing inclusive Making/Makerspace curriculum. She is a passionate maker and baker, running The Great Engineering Bake Off summer camp every summer with the Kaleideum, the local science science museum. She lives in Clemmons, NC with her family and enjoys being outdoors, playing sports, and beach trips with her corgi, Henry. Ryan D. Shirey is Teaching Professor in the Writing Program and Director of the Wake Forest University Writing Center. His work is primarily centered on rhetorical ethics and writing pedagogy, particularly peer learning and writing center pedagogy, and he also teaches courses in first-year writing and British literature (with occasional forays into Scottish, Irish, and Appalachian literature). He is currently working on projects related to comparative metacognitive development in writers with human versus chatbot interlocutors. He earned a PhD in English and American Literature with a Graduate Certificate in the Teaching of College Writing from Washington University in St. Louis. Carter Smith is an Associate Teaching Professor in the Writing Program at Wake Forest University. He is the author of two books of poetry, Rounds (2015) and White Sky (2021). His work has appeared in places like Pleiades, Cream City Review, Interim, and ELH.
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