Generative AI and Higher Education. A collection of recent op-eds

I see a new article nearly every week about the state of higher education due to the rise of generative AI. These are mostly pessimistic articles, and they definitely carry some truth. Still, in a broader perspective, I cannot help but think that higher education/academia was in trouble before generative AI. The latter pushes it further into an already declining institution. My thoughts on the academia series are a witness to this decline. However, I should also note that I still firmly believe in the necessity of academic knowledge and in future series I will also offer more positive ideas.

This time, I asked Gemini’s deep research to curate relevant articles in 2024-2025. Here is the report:

Executive Summary

The period between late 2022 and late 2025 has witnessed the most significant disruption to the epistemology of higher education since the advent of the internet. While administrative bodies and educational technology firms have coalesced mainly around a narrative of “adaptation,” “optimization,” and “future-proofing,” a distinct and increasingly vociferous counter-narrative has emerged from the faculty, the student body, and the class of cultural critics. This report, commissioned to examine the “destruction” of the university, synthesizes thousands of pages of opinion pieces, essays, and survey data to present a detailed taxonomy of this crisis.

The core argument of the “destruction” thesis is that Generative Artificial Intelligence (GenAI) does not merely automate tasks; it automates the struggle of cognition that is the defining feature of human learning. By severing the link between effort and output, AI threatens to reduce the university to a credentialing mechanism for a “post-truth” economy. This report explores the metaphysical arguments against “soulless” machinery, the pedagogical collapse of the essay, the toxic “arms race” of surveillance between students and faculty, and the fracturing of the academic community into “techno-optimist” and “humanist-resistance” camps.

Part I: The Metaphysical and Philosophical Indictment

1.1 The Apotheosis of the “Soulless” Machine

The most searing critiques of the last two years have not been technical, but spiritual and philosophical. They argue that the integration of AI into the creative and intellectual life of the university represents a fundamental error in understanding what it means to be human.

In his landmark essay for Harper’s Magazine, “The Gods of Logic,” Benjamin Labatut frames the rise of AI not as a technological breakthrough but as a dangerous “apotheosis”—a hubristic attempt by humanity to construct a “new mathematical construct” that mimics the divine.1 Labatut draws upon the Vedic myth of the “Altar of Fire,” where bricks were laid in precise mathematical proportions to attain immortality. He parallels this to the modern construction of neural networks, arguing that we are building a “soulless copy” of our own minds. The danger, Labatut warns, is not merely that these machines will replace us, but that they lack “tapas”—the heat, the ardor, and the suffering from which true art and existence emerge. By handing over our thinking to “mathematical creatures, these beings with no soul or sympathy,” the university risks becoming a temple to a false god, where the “product” of intelligence is valued over the “fire” of its creation.1

This theme of “soullessness” is echoed by the science fiction writer and critic Ted Chiang. In his essay for The New Yorker and subsequent lectures at Princeton University’s “Humanities for AI” series, Chiang argues that “art” is defined by the multitude of micro-choices a human makes during creation. Generative AI, by contrast, is a probabilistic engine that makes no choices, only statistical predictions. Chiang posits that the “Humanities for AI” movement is fundamentally an oxymoron; the humanities are the study of human interiority, while AI is the extraction of economic value from the “mean” of human output. He warns that the university’s embrace of these tools is an attempt to “wrest the technology from the hands of capitalism,” but one that is likely doomed to fail because the technology itself is designed to erase the human subject.2

1.2 The “Blandness” of the Algorithmic Average

If Labatut and Chiang attack the origin of AI, Professors Leo McCann and Simon Sweeney attack its output. In a widely circulated and scathing letter to The Guardian, they argue that the widespread use of Large Language Models (LLMs) is creating an intellectual environment characterized by “blandness.” Because LLMs are designed to predict the most likely next word, they act as engines of conformity, smoothing out the jagged edges of original thought. McCann and Sweeney describe student work produced with these tools as “generic, dull, and often factually incorrect.” They argue that the university is being “destroyed” not by a bang, but by a whimper of mediocrity. The “sabotage” of student learning occurs because the student learns to accept the “average” probability of the machine as the standard for truth, replacing critical analysis with a passive consumption of “slop”.3

Manu Bosteels, writing in the Yale Daily News, reinforces this “anti-creativity” argument. Bosteels describes LLMs as “fundamentally, by design, uncreative.” When a student uses AI to “brainstorm” or “edit,” they are not collaborating with a digital mind; they are constraining their own thoughts to the statistical mean of the internet. Bosteels warns that the liberal arts education, which prides itself on fostering unique voices, is being diluted by “self-serving marketing fluff” that conflates “productivity” with “creativity.” The result is a campus of “uninspired monotony,” where papers are technically proficient but intellectually dead.4

1.3 The Crisis of the Humanities

Louis Menand, in The New Yorker, asks the existential question: “Will the Humanities Survive Artificial Intelligence?” His analysis suggests that the crisis is one of definition. For decades, the humanities have justified their existence through the production of critical writing—the essay. If a machine can now produce a competent essay on The Great Gatsby or the French Revolution, the “product” of the humanities has been devalued to near zero. Menand argues that if the university cannot articulate why the process of human writing matters—independent of the result—then the humanities will cease to be a viable discipline. The “destruction” here is the loss of the human claim to be the sole proprietors of meaningful text.5

Part II: The Pedagogical Collapse – “The Essay is Dead”

2.1 The Decoupling of Thinking and Writing

The most practically damaging impact of AI, according to the “destruction” literature, is the severance of the link between writing and thinking. This argument is championed most vigorously by John Warner, author of More Than Words and a columnist for Inside Higher Ed.

Warner’s central thesis, developed across dozens of columns in 2024 and 2025, is that writing is not a way to record thoughts that have already occurred; writing is the act of thinking itself. The “struggle” to find the right word, to structure a paragraph, to connect two disparate ideas—this is the cognitive gym where intelligence is built. When a student uses ChatGPT to generate a draft or even to “polish” their prose, they are bypassing the very neural circuitry that education is meant to develop. Warner calls this the “outsourcing of the mind.” He argues that the current crisis is not that students are “cheating,” but that they are depriving themselves of the only mechanism the university has to teach them how to think.7

Matteo Wong, writing for The Atlantic, provides the cognitive science to back Warner’s pedagogical intuition. Wong cites research indicating that while AI “eases the cognitive load” for students, this ease comes at a terrible price. The “friction” of learning—the confusion, the frustration, the dead ends—is essential for neuroplasticity and memory formation. By smoothing away this friction, AI tools transform the student from an active “creator” into a passive “editor” or “prompt engineer.” Wong warns that we are raising a generation of students who can “operate” text but cannot “generate” thought, leading to a long-term atrophy of critical faculties.10

2.2 The “Pedagogical Debt” Revealed

However, not all critics place the blame solely on the technology. In a pivotal conversation published in The Atlantic, titled “AI is a Breaking Point,” professor Ian Bogost and writer Lila Shroff argue that AI has merely exposed a pre-existing “pedagogical debt.”

Bogost argues that the university was “destroyed” long before ChatGPT; it was destroyed by massification, efficiency-seeking, and the “widgetization” of the degree. For decades, universities relied on assessments (the five-paragraph essay, the multiple-choice exam, the discussion board post) that were mechanistic and performative. These assignments were “easy to grade” but “low on learning.” When AI arrived, it easily automated these tasks because they were already robotic. Bogost suggests that the “panic” of the faculty is actually guilt—the realization that they have been assigning work that a machine can do. The “breaking point” is the moment where the university must admit that its assessment model was fundamentally broken.12

2.3 The “Finesse” and the End of Merit

The student perspective on this pedagogical collapse is often framed in terms of “finessing.” A math professor, writing on Reddit and cited in Current Affairs, describes a new archetype of student: one who “blitzes through homework in record time” using AI, submitting perfect problem sets, only to fail spectacularly on face-to-face exams. This creates a “bimodal” distribution of competence. On one side are the students who use AI to “finesse” the system, maximizing grades while minimizing effort. On the other are the students who do the work. The tragedy, the professor notes, is that the “finesse” often works in the short term—students get the degree—but it destroys the long-term value of the education. The university becomes a place where students pay to avoid learning.13

Part III: The Student Experience – Optimization, Paranoia, and Alienation

3.1 The “Prisoner’s Dilemma” of the Classroom

By late 2025, the ethical landscape of the student body has shifted from “integrity” to “survival.” An IZA study reveals that nearly 90% of students now use generative AI, with a significant portion using it for “automation” (doing the work for them).15 This ubiquity has created a classic Prisoner’s Dilemma, eloquently articulated by Andrew Shlomchik in The Harvard Crimson.

In his op-ed, “The Problem With Psets,” Shlomchik argues that when 90% of the class is using AI to solve problem sets, the curve is destroyed. The student who chooses to “spend hours toiling in Lamont Library” is statistically punished, while the student who uses AI is rewarded with higher grades and more free time for “consulting club slides.” Shlomchik argues that as long as universities grade on accuracy for take-home work, they are actively incentivizing cheating. He proposes a radical solution: stop grading accuracy altogether. Grade on “completion” or “effort,” because the “arms race” of grading is already lost. This represents a profound capitulation: the admission that the university can no longer verify the intellectual work of its students outside of a surveillance environment.16

3.2 The Terror of the “False Positive”

If the “cheaters” are optimizing, the honest students are living in terror. Will Coldwell’s investigation in The Guardian, “I received a first but it felt tainted,” documents the rise of a surveillance culture that is poisoning the student-teacher relationship. Coldwell tells the story of “Albert,” a high-achieving student accused of using AI because his essay contained “signpost phrases” like “in addition to.”

The reliance on AI detection tools—which companies like Turnitin and OpenAI have admitted are unreliable—has created a “guilty until proven innocent” standard. Students describe “dumbing down” their own writing, inserting grammatical errors, or avoiding sophisticated vocabulary just to bypass the “AI detector.” This is the ultimate irony: the fear of being accused of using a machine is forcing humans to write less like humans and more like broken machines. The “destruction” here is the destruction of trust; the classroom is no longer a community of inquiry but a panopticon of suspicion.17

3.3 The Loneliness of the “Study Mode”

Beyond grades and integrity, there is a deepening crisis of isolation. The Chronicle of Higher Education reports on the “loneliness” of the AI-augmented campus. Features like ChatGPT’s “Study Mode” allow students to interact with documents and concepts without ever speaking to a peer or a professor. The “social undertaking” of learning—the late-night study groups, the arguments in the hallway, the awkward office hours—is being replaced by the frictionless efficiency of the chatbot.

Sandhya Kumar, writing in the Harvard Crimson, reflects on her “year with ChatGPT” as a tutor. While efficient, she notes that her education became “less personal.” The AI is a sycophant; it agrees, it encourages, and it never pushes back in the way a human peer does. The “confrontational truth-seeking” that defines the seminar room is lost. Students are retreating into digital silos, “optimizing” their education at the cost of the human connection that gives it meaning.18

Part IV: The Governance Crisis – The Schism Between Admin and Faculty

4.1 The Administrative Narrative: “Future-Proofing”

While faculty and students grapple with existential dread, the administrative layer of the university has largely embraced a narrative of “techno-optimism.” Surveys by The Chronicle show a widening gap: while 60% of faculty view AI as a threat, 78% of administrators view it as a positive tool for “efficiency” and “retention”.20

This “adaptation” strategy is exemplified by the “Making AI Generative for Higher Education” project, a consortium of elite universities (Yale, Princeton, CMU) led by Ithaka S+R. The language of this project is corporate and operational. It speaks of “leveraging” AI for “scaling” education, “streamlining” research, and “automating” administrative tasks. For administrators facing budget cuts and the “demographic cliff,” AI is a godsend—a way to deliver “personalized learning” without the expense of hiring more faculty.22

4.2 The Faculty Resistance: “Not Luddites, but Humanists”

Kevin Gannon, in his widely shared Chronicle opinion piece “Sometimes We Resist AI for Good Reasons,” has become the de facto leader of the faculty opposition. Gannon argues that the administrative push for “AI integration” often ignores the “epistemic integrity” of the university. He warns that the rush to adopt these tools is driven by a “fear of missing out” (FOMO) and donor pressure, rather than pedagogical evidence.

Gannon and others in the “resistance” argue that:

1. Truth Matters: In an era of “hallucinations,” where AI confidently invents facts, the university has a moral obligation to be a bastion of verified truth. Integrating “post-truth” machines into the core of the curriculum undermines this mission.24
2. Labor Rights: The California Faculty Association (CFA) has filed unfair labor practice charges, arguing that “AI initiatives” are often Trojan horses for increasing class sizes and reducing faculty oversight. The fear is the “Uberization” of the professor—reduced to a gig-worker who manages the AI that does the actual teaching.26
3. The “Slow School” Movement: Echoing Bogost, faculty are increasingly calling for a “Slow AI” approach. This means rejecting the “efficiency” logic of the administration and deliberately designing courses that are “inefficient”—that require handwritten work, oral defense, and physical presence. This is a battle for the “soul” of the university against the “speed” of the market.12

4.3 The Policy Vacuum

Despite the high stakes, governance remains chaotic. The Office for Students (OfS) in the UK and various US bodies have refused to issue strict bans or mandates, favoring a “principles-based” approach. This has led to a patchwork of policies that confuses students. One professor bans AI; the next requires it. This inconsistency exacerbates student anxiety and creates an environment where “the rules” are arbitrary and constantly shifting.27

Part V: Discipline-Specific Fractures

5.1 Computer Science: “The End of the Junior Developer”

The crisis is not limited to the humanities. Yale Daily News reports on a revolt among Computer Science professors who view AI as “self-sabotage.” The argument is that while AI is excellent at generating code, it denies students the “struggle” of debugging. “Thinking about stuff is fun,” writes one professor. “Why would I outsource that?” The fear is that the university is producing a generation of “architects” who cannot lay bricks—engineers who can design high-level systems but lack the fundamental understanding of the code to fix them when they break. This threatens the long-term stability of the software infrastructure.29

5.2 Medicine: The Dangerous Doctor

In high-stakes fields like medicine, the “hallucination” problem is a matter of life and death. The Guardian reports on the “taboo to tool” shift in UK medicine, but warns that students are using AI to cheat on basic science requirements. A student who “finesses” their way through organic chemistry or anatomy using AI may eventually become a doctor who relies on a chatbot for diagnosis. The report highlights cases where AI confused “shingles with Lyme disease,” underscoring the terrified realization that the “AI-augmented” doctor might be an incompetent one.31

5.3 The Arts: The End of “Human” Creation

In the arts, the “destruction” is viewed as an act of theft. Students and faculty are grappling with the ethics of using tools trained on the stolen work of living artists. The Yale Daily News reports on the “mixed” sentiment among student artists—some see a tool, others see the end of their profession. The consensus, however, is that AI generates “slop”—content that mimics the form of art without the intent of the artist. The university art school, once a place of expression, risks becoming a training ground for “content generation”.32

Part VI: The Future of Assessment – The “Post-Plagiarism” Era

6.1 The Return to Oral Culture

There is a near-universal consensus in the literature: the take-home essay is dead. “The College Essay Is Dead” narrative has solidified into fact. To save the university, critics argue for a return to the pre-industrial past.

Ian Pace, writing in Times Higher Education, argues that universities must be willing to accept “lower student satisfaction” to restore rigor. He advocates for the oral exam (viva voce) as the primary mode of assessment. You cannot fake an oral defense. You cannot “prompt engineer” a conversation in real-time. This view is supported by students like Dolan in the Crimson, who calls for “in-class essays” and “one-on-one” defenses as the only way to restore integrity.28

6.2 The Bifurcation of Higher Education

The data suggests a grim future: a bifurcation of the university system.

• The Elite Model: Institutions like Harvard and Yale will likely move toward the “Human-Centric” model—small seminars, oral exams, and “device-free” spaces. This education will be expensive, exclusive, and “AI-proof” by virtue of its intimacy.
• The Mass Model: State schools and online programs, facing budget constraints, will likely embrace the “AI-Augmented” model. Here, AI tutors, AI graders, and AI-generated curriculum will become the norm. The “destruction” of the university will thus be unequally distributed: the rich will get teachers; the poor will get chatbots.23

6.3 “Human Literacy” as the New Premium

Finally, a new concept is emerging in the critical literature: “Human Literacy.” Eryk Salvaggio and others argue that in a world flooded with AI-generated content, the ability to recognize, value, and produce human work will become the ultimate premium skill. The university’s new mission, according to this view, is not to teach students how to use AI, but how to be the “human in the loop”—the one who can discern the “soul” from the “simulation”.35

Conclusion: The Fork in the Road

The relevant opinion pieces of the last two years present a stark picture. The narrative of “destruction” is not hyperbole; it is a description of a structural collapse. The “transactional” university—the one that sells credentials in exchange for tuition—has been rendered obsolete by a technology that can perform the transaction instantly and for free.

The university stands at a fork in the road.

Path A is the path of “Optimization.” It accepts AI, integrates it, and transforms the university into a high-efficiency training ground for the AI economy. This saves the institution (budgets, enrollment) but destroys the idea (human formation).

Path B is the path of “Resistance.” It rejects the logic of the machine. It slows down. It values the difficult, the inefficient, and the human. It saves the idea, but it may be too expensive for the institution to sustain.

As the Class of 2025 graduates—the first generation of the “AI Era”—the verdict is still out. But the warning from the faculty resistance is clear: “Sometimes we resist AI for good reasons.” The best reason, they argue, is that once the fire of human thought is extinguished by the ease of the algorithm, it may be impossible to reignite.

Appendix: Data Analysis and Key Sources

Table 1: The “Destruction” Taxonomy – Key Critical Themes (2024-2025)

Table 2: The Adoption Gap – Chronicle vs. IZA Data

Table 3: Evolution of the Crisis Narrative

Works cited

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