AI in Education Critique

summarize: IASC Institute for Advanced Studies in Culture About Research Scholars News & Events Support Our Work Hedgehog The Hedghog Review -- Critical Reflections on Contemporary Culture Issues Browse Web Features About Contact Login Order THR Web Features / May 21, 2025 ChatGPT Is a Gimmick AI cannot save us from the effort of learning to live and die. Jonathan Malesic ( Darko 1981, Shutterstock.) THR Web Features Jonathan Malesic Jonathan Malesic is the author of The End of Burnout: Why Work Drains Us and How to Build Better Lives. He teaches writing at Southern Methodist University. Categories Essays Related Topics Education Technology Share I recently attended a workshop on teaching with artificial intelligence at the university where I teach writing as a part-time adjunct. I had low hopes for the workshop, but I was also desperate. My students keep turning in essays that were obviously generated by AI, and I need to figure out what to do. I looked forward to hearing the keynote speaker, a former university president who made his name arguing that instructors should move all digital technology out of their classrooms so they and their students can focus on the human interactions that technology cannot replace. At the workshop, though, the first thing he asked us to do was open our laptops, navigate to a couple of LLMs, and enter a prompt. As we did this, he kept talking. What was I supposed to pay attention to? Him, or my screen? While he jabbered, I prompted Claude.ai to write a short essay in response to one of the “innovative” topics it had proposed to me: a “Change My View Challenge,” based on a Reddit forum. It was important that I use the word innovative in my prompt, the presenter insisted. Omit innovative and you get different, presumably more pedestrian, results. Claude spat out the paper and told me it was proud of its work, which, after all, had a “clear thesis statement.” When I said I couldn’t find the statement, Claude replied, “You’re right to question this. Looking at the essay more carefully, there isn’t a single, explicit thesis statement that clearly states the central argument.” Thanks, genius. Later, the presenter wanted to demonstrate the amazing feats of pedagogical customization AI is capable of. So he asked a model to create, on the spot, a brief podcast summary of his own book, adding that the model should employ baseball metaphors, because, in this experiment, the user is a jock who cares only about baseball. The idea seemed to be that students would better appreciate the book’s content if it were expressed in terms they are already familiar with. He pressed play. The resulting summary, offered by credibly bland digital hosts, was astonishingly shallow and stupid, wedding one tired cliché (education is “lighting a lamp”) to another (“beware of the curveballs”). Was anybody really learning anything from this? The presenter seemed to be trying much too hard. AI can do this! AI can do that! He moved randomly from topic to topic, bouncing around the stage and making faux-shocked faces at his own pronouncements about the marvelous, career-disrupting implications of large language models. He seemed overstimulated and came across as impatient with the pace of human life and thought as it has been until now. That speaker was not the most frenzied AI advocate I came across this spring. A recent issue of the Chronicle of Higher Education ran a fevered fantasy by one Scott Latham, a professor of strategy at the Manning School of Business at the University of Massachusetts at Lowell. It is a vision featuring AI “agents” that will provide students a bespoke experience running from orientation through course instruction to job placement, all the while tracking their every frown and furrowed brow (because students will stare endlessly into cameras) and responding at each moment with the perfect remedy. “Human interaction is not as important to today’s students,” Latham claims, and so presumably the AI university will offer little. Compared to the current model of college, he promises, all of this will be better and cheaper—never mind growing evidence that LLMs are deteriorating and becoming more expensive. And it will happen. In fact, the word will appears more than 130 times in the 4,000-word article. Could, only twice. “Predicting AI’s disruption is the easy part,” Latham writes. “The tough part is making people realize the inevitable.” The claim of inevitability is crucial to technology hype cycles, from the railroad to television to AI. “A key strategy for a technology to gain market share and buy-in,” the scholars David Gray Widder and Mar Hicks write, “is to present it as an inevitable and necessary part of future infrastructure, encouraging the development of new, anticipatory infrastructures around it.” The infrastructure demanded by AI is neither neutral nor cheap. It has well-known environmental costs, given the vast amount of electricity and water its data centers demand. Nor is the infrastructure only physical. It has already been built in the minds of students, who are becoming informational lotus-eaters, addicted to immediate, effort-free homework answers and adequate-seeming essays on demand. * * * Tot up the overeager salesmen, the questionable prophecies, and the clearly exorbitant costs, and it becomes clear: AI is not revolutionary. It’s a gimmick. It entices us with the prospect of sparing us drudgery, but it ultimately disappoints. AI apologists are like hucksters at a county fair, fast-talking about some newfangled marvel. Universities are their gape-mouthed marks, emptying their pockets even while they are unsure about what they are buying or whether students will use it to learn or simply cheat with. We call something a gimmick, the literary scholar Sianne Ngai points out, when it seems to be simultaneously working too hard and not hard enough. It appears both to save labor and to inflate it, like a fanciful Rube Goldberg device that allows you to sharpen a pencil merely by raising the sash on a window, which only initiates a chain of causation involving strings, pulleys, weights, levers, fire, flora, and fauna, including an opossum. The apparatus of a large language model really is remarkable. It takes in billions of pages of writing and figures out the configuration of words that will delight me just enough to feed it another prompt. There’s nothing else like it. But look at what people actually use this wonder for: brain-dead books and videos, scam-filled ads, polished but boring homework essays. Another presenter at the workshop I attended said he used AI to help him decide what to give his kids for breakfast that morning. Such disappointment inevitably accompanies the gimmick. “The gimmick lets us down,” Ngai writes, “only because it has also managed to pump us up.” A universal culture-producing machine? Remarkable! Then we see its results. Widder and Hicks note that the failed promise of AI “is not surprising, as generative AI does not so much represent the wave of the future as it does the ebb and flow of waves past.” MOOCs, NFTs, AR: We should be wise to the tricks by now. AI progress in cultural production already seems to have slowed because the models have run out of human-generated writing to “learn” from and increasingly feed on AI-produced content, gulping down a vile soup of their own ever-concentrating ordure. The AI apologists must deny, or at least forestall, such disappointment. They insist that the time scales on AI’s technical progress are shortening—artificial general intelligence will be here in ten years; no, five; no, we’re just months away—even as they implore skeptics to give the technology a chance, because it’s still early days. But do the apologists even believe it themselves? Latham, the professor of strategy, gives away the game at the end of his reverie. “None of this can happen, though,” he writes, “if professors and administrators continue to have their heads in the sand.” So it’s not inevitable after all? Whoops. * * * The scholars hectoring their colleagues to adopt AI are not all so ham-fisted. A more subtle and psychologically interesting argument about AI in higher ed shows how fine the line is between the huckster and the mark. Writing recently in The New Yorker, Princeton history professor D. Graham Burnett takes up where Latham leaves off, drawing a distinction between himself and his denialist colleagues: “[E]everyone seems intent on pretending that the most significant revolution in the world of thought in the past century isn’t happening.” This pretense, he writes, “is, simply, madness. And it won’t hold for long.” Burnett’s essay appears to issue from deep doubts about the value of humanities research. Given the capabilities of AI to sift through archives, detect patterns within the contents, and perhaps incrementally advance what has been said about them, the value of an academic monograph seems to fall to zero. “The making of books such as those on my shelves,” Burnett writes, “each the labor of years or decades, is quickly becoming a matter of well-designed prompts. The question is no longer whether we can write such books; they can be written endlessly, for us. The question is, do we want to read them?” The answer depends on the “we.” Does the median New Yorker subscriber want to read a monograph? No. Does a scholar fifty or five hundred years hence want to? Let’s put it out there and let them decide. That has been the value proposition of humanistic research for centuries. Burnett supposes there might still be merit in teaching, even if scholarship is dead. It is a maneuver many an Ivy League PhD has made who finds himself or herself in a job with a heavy teaching load. Indeed, the overwhelming majority of humanities PhDs are in this position. Most have teaching-intensive jobs as adjuncts or, if they are on the tenure track, they teach four or five or six courses per semester at community colleges and regional universities. They never publish a single monograph and read few if any after they finish graduate school. For them, academia already looks like the near-future Burnett envisions. Burnett decided to merge his teaching with his interest in AI. He assigned students in an undergraduate class—so far as I can tell, the only one he taught this spring—to engage with a chatbot about human attention and turn the text into a short paper. He marvels at their output: Reading the results, on my living-room couch, turned out to be the most profound experience of my teaching career. I’m not sure how to describe it. In a basic way, I felt I was watching a new kind of creature being born, and also watching a generation come face to face with that birth: an encounter with something part sibling, part rival, part careless child-god, part mechanomorphic shadow—an alien familiar. In Burnett’s eyes, his students are not just creative in the ordinary sense of being able to turn nice phrases or make clever connections—already feats that lighten a grading load of dreary essays. No, Burnett’s students are conjurers, evokers, maybe minor deities, able to break the old material laws most of us labor under. I know this impulse. In moments of professional self-doubt, I have often tried to convince myself that my students were amazing, that their halting efforts were in fact brilliant, that my class, unique among the courses listed on their transcripts, unlocked something in them, that the students were teaching me, that, indeed, all I needed to do was get out of their way. It is a lie many teachers tell themselves. And why not? It is not as if someone can fact-check it. Your scholarship, or lack thereof, is public; your students’ work occurs behind the high fence of the Family Educational Rights and Privacy Act. You tell yourself a story at once self-abnegating and self-aggrandizing. You tell it to raise the students up; you’re there for them, after all. And that much is true; you are there for them. But you tell the story as well to put down your unfeeling, ossified colleagues, the ones who don’t get it. You tell it so you won’t feel as old as they are, because you are spiritually closer to the young. To Burnett, the AI dialogs offered something genuinely new under the sun. “Each sheaf of paper I picked up,” he writes, “was more astonishing than the last.” Sure. This is not to say I have never been astonished by my students’ writing. Burnett reports being moved to tears by one of his students’ interactions with a chatbot. I, too, have cried while reading student essays. I once had a student who started college only after he had retired from four decades working for the phone company. He was not a great writer, but he wrote from the heart. In a class on religious autobiography, he described going to the hospital to speak to the man, by then on his deathbed, who had murdered my student’s mother. The man asked my student to forgive him. My student did so. He forgave. The essay testified to a form of love few of us would be capable of. Sitting at my kitchen table, I read the essay a second time; I cried a second time. It was the rare piece of student writing that improved the world by its existence. It made mercy more widely known. * * * In the end, Burnett is essentially in the same place as his ostrich-headed colleagues, though it is not clear he realizes it. “You can no longer make students do the reading or the writing,” he writes, because they can make a machine do it for them. “So what’s left? Only this: give them work they want to do. And help them want to do it.” Is this a slip-up? A signal that AI-savvy students don’t need teachers after all? If students want to do the work, then they don’t need help wanting it. No, the only task remains the paradoxical one identified as far back as in Plato’s Meno: Give students work they don’t know they need to do. And yes, help them want to do it. I have found, to overcome students’ resistance to learning, you often have to trick them. There’s the old bait-and-switch of offering grades, then seeing a few students learn to love learning itself. Worksheets are tricks, as are small-group discussions and even a teacher’s charisma. I’m sure I have used baseball analogies in class, too. In the face of the difficulty of reforming students’ desires, you can trick yourself into believing you’re doing it, and sleep well at night. I don’t know anyone for whom it’s a straightforward task. It’s the challenge for any teacher, and AI offers a tempting illusion to students—and evidently to some teachers—that there could be a shortcut. The week Burnett’s article appeared, I visited the classroom of Ted Hadzi-Antich at Austin Community College. His honors political philosophy students were discussing the final section of James Baldwin’s The Fire Next Time. They wanted to talk about death. They sat in chairs in a circle, no desks walling them off from each other. I had hoped I could sit unobtrusively in the corner and take notes on the scene. “Learning doesn’t happen in the corner!” a student scolded. OK, fine. I was present. So I was implicated in what was about to occur. For eighty minutes, the eleven students and Hadzi-Antich talked. I chimed in at the end. No students texted. None disappeared into a screen. Two cried. They held their highlighted copies of the reading in their laps, but they didn’t talk much about it. Even so, they connected Baldwin’s ideas to their varied life experiences, including loss, addiction, and bigotry. Two students disagreed with each other about the uses of the past. Later, they told me they each describe the other as their “antagonist.” They seemed to respect each other, having read each other’s writing and debated in class all semester. They know each other’s voice. This is what Burnett seems to think is new, or newly exciting, thanks to his engagement with AI—something teachers such as Hadzi-Antich have been orchestrating in their classes since well before the dawn of AI. And in fact, Hadzi-Antich promotes text- and discussion-based education at community colleges through The Great Questions Foundation, which he directs. You don’t need to experience the technological sublime in order to see the value of giving students a book and asking them to read and discuss it. You do need to accept something like Max Weber’s idea of the teacher’s vocation, that of helping others “to reckon with the ultimate meaning of [their] own actions.” This reckoning is not a task you can expedite with a machine. If you try to spare yourself the labor, you fail. * * * Appropriately enough, this essay took me forever to write. I had a great deal of anger, frustration and sadness to draw on. I had evidence and critique, but I could not find an argument. I kept working. I changed the whole focus after I attended the AI workshop at school. That opened me up. I worked for days in the caesura between reading my students’ drafts and reading their finished research essays, knowing for sure that some had done all the work themselves because I had seen them put up the scaffolding and then build the essay, brick by brick. I knew that others would likely do very little, but I would be unable to prove it. As I worked, new articles and outrages about AI in education appeared daily, making me feel as though I were falling behind. Believe me, I wanted a shortcut. I had not read Theory of the Gimmick before starting this essay, having only heard about it from my wife, a professor of literature. To get up to speed, I read an earlier article that Ngai turned into a chapter. I then skimmed my wife’s well-marked-up copy of the book, noting her underlines, her starred passages, her “hm!” in the margins. I leaned on the index to find passages on belief. I left most of the book unread. I did these things because I know I am mortal. I cannot afford to read a 400-page work of frankly oblique theoretical prose. That is to cast no shade on Ngai. Her book deserves a close reading. I have only so much time. Even if that sounds like the same excuse students give for why they take AI shortcuts, I don’t think we mean the same thing. Once I had a full draft, I asked my wife to read and comment on it. At the time, she was working on a paper about Herman Melville’s The Confidence Man and Max Weber’s “Science as a Vocation.” The latter is a locus classicus for our dinner-table conversation. She needed only to make a brief mention of a key passage in Weber for me to know what move the essay was missing. Her concerns, her attention, and the contingencies of her life and thought are all over this essay. It would have been different if she had been reading, say, William or Henry James that week. To a large extent, the intellectual circuit running through her life and mine, across two decades of talking about literature and culture, love and death, constitutes our life together. After I got her feedback, I finally asked ChatGPT if generative AI could be considered a gimmick in Ngai’s sense. I did not read its answer carefully. Whenever I see the words cascade down my computer screen, I get a sinking feeling. Do I really have to read this? I know I am unlikely to find anything truly interesting or surprising, and the ease with which the words appear really does cheapen them. ChatGPT reported back that it could indeed be considered a gimmick. Then I asked the gimmick how educators should approach its implementation in universities and schools. “The worst thing educational institutions could do is embrace AI uncritically as an inevitable ‘efficiency upgrade,’” it wrote, “because that would mean compounding the very gimmickry Ngai diagnoses: mistaking ease for value, and output for understanding.” Take that, Scott Latham. This is, of course, just what I hoped the machine would say. I have argued enough with the model that it probably knows I want it to be self-critical. I suspect it admits such things only to me. I wish it would tell university presidents, information officers, professors, and certainly students that even ChatGPT thinks it is a gimmick. But it only ever tells them what they want to hear, not what I want. Ted Hadzi-Antich’s students read and discussed these words by James Baldwin: “Perhaps the whole root of our trouble, the human trouble, is that we will sacrifice all the beauty of our lives, will imprison ourselves in totems, taboos, crosses, blood sacrifices, steeples, races, mosques, armies, flags, nations” —I might add technologies and hype bubbles—“in order to deny the fact of death, which is the only fact we have.” Part of a teacher’s job—certainly in the humanities, but even in professional fields like business—is to help students break out of their prisons, at least for an hour, so they can see and enhance the beauty of their own minds. It is to help them learn, together, to defend how they want to live, precisely because they, too, unlike a machine, will one day die. I will sacrifice some length of my days to add depth to another person’s experience of the rest of theirs. Many did this for me. The work is slow. Its results often go unseen for years. But it is no gimmick. 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**Summary of “ChatGPT Is a Gimmick” by Jonathan Malesic** *Published May 21, 2025, in The Hedgehog Review* Jonathan Malesic, a writing professor and author of *The End of Burnout*, argues that generative AI tools like ChatGPT are overhyped and ultimately disappointing. Based on his experience at a university workshop and recent AI discourse in higher education, he critiques the fervor surrounding AI in teaching and learning. ### Key Points: - **Workshop Disillusionment**: Malesic attended a university workshop hoping to find strategies to address student use of AI in writing. Instead, he found the keynote speaker enthusiastically promoting AI tools in contradictory and shallow ways—claiming AI enriches human connection while forcing participants to disengage from actual human interaction. - **Pedagogical Gimmickry**: Demonstrations of AI's teaching potential—like generating podcast summaries in baseball metaphors—felt trivial and unconvincing. Rather than fostering deeper learning, AI seemed to produce formulaic, uninspiring content. - **Critique of Hype and Inevitability Claims**: He highlights how advocates like Scott Latham declare AI’s educational takeover as inevitable, using “will” over 130 times in a single article. Malesic argues this rhetoric is part of a familiar tech marketing cycle that conflates inevitability with desirability, ignoring high costs and limitations. - **Environmental and Cognitive Costs**: AI infrastructure demands vast energy and water. More subtly, it cultivates intellectual laziness, encouraging students to rely on AI for effortless answers, diminishing real learning and critical thinking. - **AI as Gimmick**: Drawing on theorist Sianne Ngai, Malesic argues AI fits the definition of a "gimmick"—appearing both over-engineered and underwhelming. It promises to reduce effort but fails to deliver meaningful value, like a Rube Goldberg machine that inefficiently replaces simple human actions. - **Diminishing Returns and Cultural Decay**: As LLMs consume more AI-generated content, their quality declines. AI risks becoming a self-referential echo chamber, unable to generate truly new cultural artifacts, similar to past failed tech trends like MOOCs and NFTs. - **A More Nuanced Perspective**: Malesic contrasts Latham’s hype with a subtler view from Princeton historian D. Graham Burnett, who explores how AI reshapes the humanities. Burnett worries that monographs—once the core of scholarly life—might become obsolete, and he experiments with AI in teaching instead. - **What Remains Human**: Burnett’s emotionally complex reflections suggest that even if AI can mimic or produce content, the real educational value lies in human encounters with knowledge—and with the strange, emergent “creature” that AI has become. ### Conclusion: Malesic concludes that AI in education is less a revolution and more a gimmick—flashy but shallow, costly but unproven. The promise of AI may excite, but its actual contributions to human learning and meaning-making remain dubious, and the effort of living and learning remains inescapably human.

is an anti-intellectualism to blame for this reaction to AI? frame an argument against the arguments of this article from this perspective

Yes, one could frame a strong counterargument to Jonathan Malesic’s *“ChatGPT Is a Gimmick”* from the perspective that his critique is rooted—perhaps inadvertently—in **anti-intellectualism**, specifically a distrust of technological tools that democratize intellectual labor and challenge traditional academic authority. Here's how such an argument could unfold: --- ### **Counterargument: The Reaction Against AI Reflects a Strain of Anti-Intellectualism** Jonathan Malesic's dismissal of generative AI as a mere "gimmick" reveals a deeper unease—not with the technology’s limitations, but with its potential to disrupt the traditional gatekeeping structures of academia and knowledge production. His critique, couched in personal anecdotes and disdain for “overexcited” presenters, reflects a nostalgic attachment to institutional forms of knowledge that may now be undergoing long-overdue democratization. #### 1. **Technological Skepticism as Intellectual Elitism** Malesic’s rejection of AI echoes a broader anti-technological bias found in certain academic circles—especially the humanities—where new tools are met not with curiosity but suspicion. His discomfort with AI in the classroom and derision of pedagogical experiments involving language models betray a fear that **the production of knowledge is being made too accessible, too fast, and too scalable.** This reaction, while wrapped in concern for intellectual rigor, ironically betrays **anti-intellectualism**, if we define it as a refusal to engage with new methods of inquiry that threaten the established order. Like the Luddites smashing machines not because they hated tools, but because those tools redistributed power, critics like Malesic resist AI not because it lacks intelligence, but because it disrupts their monopoly on what counts as *valid* thought. #### 2. **Distrust in Students as a Proxy for Distrust in the Masses** Much of Malesic’s concern revolves around students “cheating” with AI, writing papers “on demand,” and generally evading the “effort” of learning. But this is a paternalistic view of education—one that presumes students cannot be trusted with powerful tools. It echoes a familiar strain of anti-intellectual populism: **“People are too lazy, too shallow, or too unprepared to use new knowledge responsibly.”** Ironically, this is the same logic used to justify censorship and restrict access to libraries in other contexts. By framing students as passive consumers of AI output rather than potential *collaborators* with it, Malesic reduces the capacity of learners to engage creatively and critically with technology. This skepticism undermines the very goals of intellectual development: curiosity, adaptability, and the pursuit of new questions. #### 3. **Romanticizing the Past Undermines the Present** Malesic implies that true learning must be slow, human, and difficult. But **intellectualism isn’t defined by suffering or antiquated methods**—it’s defined by the quality of inquiry and the richness of ideas, regardless of the tools used. AI can accelerate the process of synthesis, hypothesis generation, and iterative learning. To reject it wholesale because some outputs are shallow is to ignore how intellectual history has always involved technological aids—from the printing press to the internet. #### 4. **Aesthetic Disdain is Not a Philosophical Argument** Critics of AI often lean on aesthetic disgust—calling AI-generated content “shallow,” “bland,” or “gimmicky.” But taste is not a reliable measure of intellectual worth. **Early novels were called frivolous too.** So were Wikipedia, podcasts, and blogs—until they weren’t. What Malesic describes as gimmickry might instead be an early-stage form of an intellectual revolution in how we think, write, and teach. Dismissing it out of hand is less a defense of high culture and more a refusal to engage with emerging forms of cultural production. #### 5. **Real Intellectualism Engages the Unknown** To be truly intellectual is not to *defend the past* but to *interrogate the present* and explore the future. A genuinely curious thinker asks: **What can this tool reveal that I couldn’t see before? What new forms of dialogue and expression might it enable?** Malesic’s article does not ask these questions. Instead, it laments the erosion of familiar rituals of education and scholarship. That lament is not wrong—but it’s also not intellectually brave. --- ### **Conclusion** Far from being a gimmick, AI may represent a *redistribution* of intellectual agency—from ivory towers to broader publics. To reject it categorically is not an act of principled skepticism but one of anti-intellectualism masked in tradition. It is the job of educators and scholars to *interrogate, refine, and elevate* the use of new tools—not to condescend to those who use them or fear their implications. If we truly value intellect, we must apply it *with* new technologies—not against them.

rewrite in the style of Dostoyevsky without dashes or lists or bold or italic and make shorter

Ah, but what is it that drives such a man as Malesic to dismiss this thing—this strange new mirror of thought—as nothing more than a gimmick? It is not the machine he despises, not truly. No, what unsettles him lies deeper, in the quiet terror that something uninvited now speaks the language of the mind. He sees it in students, in their keystrokes, in their evasions, and he recoils—not from their laziness, as he says, but from the crumbling of the pedestal upon which his own intellect once stood unchallenged. There is a pride in this rejection, a pride so refined that it masquerades as humility. He speaks of meaning and labor, of slowness and effort, as if knowledge were a cathedral built stone by stone, and not also sometimes a fire that leaps without warning. He forgets that true thought has never been loyal to its vessels. It escaped Socrates and his city, crept into printing presses, bloomed in typewriters, and now, perhaps, glimmers through silicon and code. That it does so awkwardly at times, without grace or wit, is no reason to shun it. A child speaks poorly too, yet we do not dismiss its voice. What is truly feared here is not the machine, but the people it enables. For if anyone may write, if anyone may summon semblances of thought, then what becomes of those who once guarded the gate? This fear wears the mask of wisdom, but it is fear all the same—a fear of losing dominion over the world of words. Yet if thought is sacred, then we must not bind it to old rituals. We must meet the unknown, not with scorn, but with trembling curiosity.

Certainly. Here is a more Dostoyevsky-style, condensed rewrite of that argument: --- It is strange, almost tragic, how some minds recoil from this new marvel called artificial intelligence. They call it a gimmick, a shallow trick, as if the fear that trembles beneath their words were not about the machine itself but about what it threatens to unravel in their cherished order. They cling desperately to the old ways of knowledge, those slow, arduous pilgrimages of thought, as if the very soul of intellect depended on the grinding of the quill and the solitary vigil of the scholar. But is it not a kind of fear born of weakness? A fear that the gates of wisdom, once guarded jealously by the learned few, might now swing open to all? The scholar’s contempt for these mechanical scribes whispers not of wisdom but of a certain secret dread—a dread that the masses, those whom he deems lazy or shallow, might wield power too great for their fragile minds. And yet, what is intellect if not the restless pursuit of the unknown? What grandeur lies in romanticizing the past while the present beckons with new tools and new ways of seeing? The disgust with this machine-born prose, this “gimmick,” is no argument of the mind but the cry of one who fears the loss of a sacred ritual. But the ritual is not the truth, and truth does not die because its servants change. Real thinkers, those rare souls who dare to confront the abyss, will ask not how this thing diminishes thought but how it might deepen it. To reject the future out of hand is not courage but a surrender to the terror of change, a refusal to be reborn. In truth, the age of intellect is not over. It merely changes its face. And those who cling too tightly to the past will find themselves left behind, clutching ghosts while the world moves on.

rewrite in the style of Dostoyevsky without dashes or lists or bold or italic and make very academic in its argumentation

Certainly. Here is a rewritten version of the counterargument in the style of Dostoyevsky, adopting a tone of moral and philosophical seriousness, winding logic, and introspective analysis, while also elevating the register into a more academic style: --- There is, beneath the surface of Jonathan Malesic’s critique of generative AI, a deeper and far more troubling current—one that cannot be explained merely by pedagogical caution or technological skepticism, but which appears, upon closer inspection, to originate from a form of anti-intellectualism masquerading as intellectual rigor. This is not the overt and vulgar anti-intellectualism of demagogues or polemicists who scorn learning outright; it is the subtler, more insidious species that cloaks itself in the vestments of the academy, priding itself on erudition while recoiling from the destabilizing effects of innovation. Malesic, it must be acknowledged, writes with a tone of sincerity and even nostalgia, bemoaning what he perceives as the corrosion of the humanistic tradition at the hands of algorithmic mimicry. Yet it is precisely this appeal to tradition, this longing for the pedagogical purity of past epochs, that must be scrutinized with the severity it deserves. For what is this attachment to the “true” classroom if not a romanticization of forms that were themselves historical constructions, no more sacred or permanent than parchment or chalk? To defend them as eternal is to conflate method with essence, ritual with reason. When Malesic derides the use of AI by students as a form of cheating or evasion, he does not merely issue a practical concern about academic integrity; he betrays a philosophical stance. He presupposes that intellectual labor must be arduous in a very specific and sanctioned manner, that the tools which might render that labor more accessible are not aids to be integrated but threats to be expelled. Here one finds the paradox that lies at the heart of his position: he claims to defend the life of the mind, yet he is unwilling to accept the expansion of its means of expression. He would have us believe that the student who turns to an artificial interlocutor is engaged in a counterfeit activity, but the same accusation could once have been leveled—indeed, was leveled—at those who used calculators in mathematics, at those who cited encyclopedias, and at those who read their lectures from typed notes rather than from memory. There is a disquieting paternalism in this vision of the student, a condescension barely concealed by pedagogical concern. The learner is rendered passive, untrustworthy, and incapable of distinguishing between assistance and substitution. It is presumed that students, when given access to generative tools, will inevitably choose superficiality over substance, ease over engagement. But such a presumption does not arise from faith in reason; it arises from a distrust in the very capacity for reason among those whom the academy claims to cultivate. If one does not trust students to think critically when equipped with powerful tools, one does not trust them to think critically at all. Thus the very logic of intellectual education begins to collapse under the weight of its own inconsistencies. Moreover, there is in Malesic’s argument a peculiar aestheticism, a revulsion toward what he calls the blandness or predictability of AI-generated text. But this reaction, though emotional in origin, is deployed rhetorically as though it were philosophical. What is blandness, after all, but a judgment of taste? And can the taste of one man be the measure of an entire mode of inquiry? One is reminded of the early critics of the novel form, who dismissed it as frivolous and vulgar, unfit for serious minds. Time, of course, rendered such verdicts absurd. There is no reason to suppose that a similar fate will not befall those who dismiss generative language models with equal haste. In truth, what is feared is not the machine’s mediocrity, but its potential for mastery. There is a terror, perhaps unspoken but keenly felt, that the tool might become too competent, that it might rival or even surpass the human in certain domains of thought. And this terror, though understandable, must be resisted. For it leads not to a deeper inquiry into the nature of human reason, but to a defensive entrenchment around outdated forms. One begins to see that the rejection of AI in such terms is not a gesture of intellectual courage but of retreat, not a confrontation with the new but a retreat into the old. True intellectualism does not fear new methods of thought. It is not wedded to specific forms but animated by the pursuit of truth in whatever form it may appear. If the academy cannot accommodate the emergence of powerful cognitive tools, if it cannot teach students to use them critically, ethically, and creatively, then it is not defending the life of the mind; it is embalming it. To reject generative AI without seriously exploring its epistemic potential is not a triumph of wisdom but a failure of nerve. And to do so under the banner of education is, in the end, a disservice to the very tradition it seeks to preserve. Thus we must ask ourselves: is the opposition to these tools truly an act of fidelity to intellectual tradition, or is it a symptom of a deeper reluctance to relinquish control over the production and distribution of knowledge? In the shadow of this question lies the answer to another, more unsettling one: whether those who claim to defend thought are in fact unwilling to allow it to evolve. And if that is so, then their position is not only mistaken—it is anti-intellectual in the most tragic and self-defeating sense. --- Would you like this adapted for a specific journal or field (e.g. philosophy, education, digital humanities)?

Certainly. Here is a rewrite of the counterargument in the style of Dostoyevsky, with an academic tone, avoiding dashes, lists, and formatting: --- One might contend that the reaction against artificial intelligence, as articulated by Jonathan Malesic in his dismissal of ChatGPT as a mere gimmick, is not solely a critique of technological limitations but rather an expression of a deeper intellectual malaise. This malaise, born of a profound anxiety toward the democratization of knowledge and the erosion of traditional academic authority, reveals itself as a subtle form of anti-intellectualism. In the manner of Dostoyevsky’s characters who wrestle with the contradictions of faith and reason, the contemporary scholar’s rejection of AI may be understood as a conflict between the desire to preserve the sanctity of established intellectual institutions and the fear of an unknown future that threatens to dissolve them. Malesic’s skepticism, framed as concern for rigor and authentic learning, betrays an underlying attachment to the rituals and hierarchies of intellectual production that have long defined academia. It is a form of intellectual conservatism that clings to the past not out of reverence for tradition per se, but because the past offers a semblance of control over what is deemed valuable knowledge. To reject AI as a superficial novelty is to resist the fundamental challenge it poses: the redistribution of epistemic authority beyond the narrow confines of scholarly elites. This resistance recalls the paradox of the intellectual who, in his fear of losing mastery, unwittingly participates in a form of anti-intellectualism, for it is a refusal to engage with the evolving means of inquiry that have always marked the progress of human understanding. Further, Malesic’s distrust of students who might employ AI in their academic work reveals a paternalism that diminishes the capacity of learners to partake in intellectual labor as active agents rather than passive recipients. This attitude echoes a broader cultural anxiety that questions the preparedness and seriousness of the masses to wield new tools responsibly. Yet history demonstrates that such distrust often underestimates the adaptability and creativity of those outside the established academic milieu. By foreclosing the possibility of collaboration between human intellect and artificial assistance, the critique implicitly denies the transformative potential of education to foster new modes of thought and expression. Moreover, the romanticization of the slow, arduous process of learning as the sole authentic form of intellectual endeavor overlooks the fact that intellectualism has always been intertwined with the development of tools that extend human faculties. From the invention of writing to the printing press, from the microscope to the internet, each advance has provoked similar anxieties. The present fear that AI will render thought shallow or mechanical is but the latest iteration of a recurring tension between innovation and tradition. To reject these tools wholesale is to mistake the infancy of a new medium for its ultimate form and to ignore the possibility that, properly harnessed, AI might enrich rather than diminish intellectual life. The aesthetic disdain that colors much of the opposition to AI-generated content, though rhetorically compelling, fails to meet the demands of rigorous philosophical scrutiny. Disgust or boredom with novelty is no substitute for reasoned argument. Intellectual history is replete with examples of cultural products initially derided as trivial or ephemeral only to be later recognized for their profound impact. To consign AI to the realm of gimmickry is to close one’s eyes to the ways in which new forms of knowledge production challenge and expand the horizons of thought. Ultimately, true intellectual engagement demands not the defense of a static ideal of learning but an active interrogation of the conditions under which knowledge is produced and transmitted. It calls for courage to confront uncertainty and to explore the possibilities afforded by new technologies rather than retreating into nostalgia. The dismissal of AI as a mere distraction betrays a reluctance to participate in the ongoing dialogue that defines intellectual life. To be truly committed to the life of the mind is to recognize that the advent of AI compels us to rethink what it means to know, to teach, and to create. In this light, the reaction against AI is less an act of principled critique and more a manifestation of anti-intellectualism, insofar as it eschews engagement with the unknown and denies the evolution of intellectual practice. The challenge before us is not to resist but to embrace and to rigorously interrogate these new tools in the spirit of inquiry that has always animated the pursuit of knowledge. --- Would you like me to help expand or adapt this further for a particular publication or audience?

now make much shorter

Certainly. Here's a much shorter version in the style of Dostoyevsky, retaining the academic tone: --- The rejection of artificial intelligence by critics such as Jonathan Malesic appears, upon closer scrutiny, not as a defense of intellectual rigor but as a veiled form of anti-intellectualism. Beneath the surface of his argument lies a fear—not of flawed reasoning, but of a shifting locus of authority. The scholar who decries AI as a gimmick reveals a deeper unease: that knowledge, once the guarded domain of the academy, is now being reshaped by forces beyond its walls. This resistance masks a desire to preserve the rituals of traditional learning, yet it risks ossifying thought. True intellectual life demands an openness to new instruments of inquiry. To dismiss AI outright is not an act of vigilance, but of timidity—an unwillingness to confront how the mind might evolve. In rejecting the machine, the critic may also reject the very spirit of inquiry he claims to protect. --- Would you like a version with more rhetorical flair or something suitable for publishing as a response?