Cinemorphosis

Tag: education

  • How Real Writing Survives in the Age of ChatGPT

    How Real Writing Survives in the Age of ChatGPT

    AI-Resistant Pedagogy is an instructional approach that accepts the existence of generative AI without surrendering the core work of learning to it. Rather than relying on bans, surveillance, or moral panic, it redesigns courses so that thinking must occur in places machines cannot fully inhabit: live classrooms, oral exchanges, process-based writing, personal reflection, and sustained human presence. This pedagogy emphasizes how ideas are formed—not just what is submitted—by foregrounding drafting, revision, discussion, and decision-making as observable acts. It is not AI-proof, nor does it pretend to be; instead, it makes indiscriminate outsourcing cognitively unrewarding and pedagogically hollow. In doing so, AI-resistant pedagogy treats technology as a background condition rather than the organizing principle of education, restoring friction, accountability, and intellectual agency as non-negotiable features of learning.

    ***

    Carlo Rotella, an English writing instructor at Boston College, refuses to go the way of the dinosaurs in the Age of AI Machines. In his essay “I’m a Professor. A.I. Has Changed My Classroom, but Not for the Worse,” he explains that he doesn’t lecture much at all. Instead, he talks with his students—an endangered pedagogical practice—and discovers something that flatly contradicts the prevailing moral panic: his students are not freeloading intellectual mercenaries itching to outsource their brains to robot overlords. They are curious. They want to learn how to write. They want to understand how tools work and how thinking happens. This alone punctures the apocalyptic story line that today’s students will inevitably cheat their way through college with AI while instructors helplessly clutch their blue books like rosary beads.

    Rotella is not naïve. He admits that any instructor who continues teaching on autopilot is “sleepwalking in a minefield.” Faced with Big Tech’s frictionless temptations—and humanity’s reliable preference for shortcuts—he argues that teachers must adapt or become irrelevant. But adaptation doesn’t mean surrender. It means recommitting to purposeful reading and writing, dialing back technological dependence, and restoring face-to-face intellectual community. His key distinction is surgical and useful: good teaching isn’t AI-proof; it’s AI-resistant. Resistance comes from three old-school but surprisingly radical moves—pen-and-paper and oral exams, teaching the writing process rather than just collecting finished products, and placing real weight on what happens inside the classroom. In practice, that means in-class quizzes, short handwritten essays, scaffolded drafting, and collaborative discussion—students learning how to build arguments brick by brick instead of passively absorbing a two-hour lecture like academic soup.

    Personal narrative becomes another line of defense. As Mark Edmundson notes, even when students lean on AI, reflective writing forces them to feed the machine something dangerously human: their own experience. That act alone creates friction. In my own courses, students write a six-page research paper on whether online entertainment sharpens or corrodes critical thinking. The opening paragraph is a 300-word confession about a habitual screen indulgence—YouTube, TikTok, a favorite creator—and an honest reckoning with whether it educates or anesthetizes. The conclusion demands a final verdict about their own personal viewing habits: intellectual growth or cognitive decay? To further discourage lazy outsourcing, I show them AI-generated examples in all their hollow, bloodless glory—perfectly grammatical, utterly vacant. Call it AI-shaming if you like. I call it a public service. Nothing cures overreliance on machines faster than seeing what they produce when no human soul is involved.

  • Everyone in Education Wants Authenticity–Just Not for Themselves

    Everyone in Education Wants Authenticity–Just Not for Themselves

    Reciprocal Authenticity Deadlock names the breakdown of trust that occurs when students and instructors simultaneously demand human originality, effort, and intellectual presence from one another while privately relying on AI to perform that very labor for themselves. In this condition, authenticity becomes a weapon rather than a value: students resent instructors whose materials feel AI-polished and hollow, while instructors distrust students whose work appears frictionless and synthetic. Each side believes the other is cheating the educational contract, even as both quietly violate it. The result is not merely hypocrisy but a structural impasse in which sincerity is expected but not modeled, and education collapses into mutual surveillance—less a shared pursuit of understanding than a standoff over who is still doing the “real work.”

    ***

    If you are a college student today, you are standing in the middle of an undeclared war over AI, with no neutral ground and no clean rules of engagement. Your classmates are using AI in wildly different ways: some are gaming the system with surgical efficiency, some are quietly hollowing out their own education, and others are treating it like a boot camp for future CEOhood. From your desk, you can see every outcome at once. And then there’s the other surprise—your instructors. A growing number of them are now producing course materials that carry the unmistakable scent of machine polish: prose that is smooth but bloodless, competent but lifeless, stuffed with clichés and drained of voice. Students are taking to Rate My Professors to lodge the very same complaints teachers have hurled at student essays for years. The irony is exquisite. The tables haven’t just turned; they’ve flipped.

    What emerges is a slow-motion authenticity crisis. Teachers worry that AI will dilute student learning into something pre-chewed and nutrient-poor, while students worry that their education is being outsourced to the same machines. In the worst version of this standoff, each side wants authenticity only from the other. Students demand human presence, originality, and intellectual risk from their professors—while reserving the right to use AI for speed and convenience. Professors, meanwhile, embrace AI as a labor-saving miracle for themselves while insisting that students do the “real work” the hard way. Both camps believe they are acting reasonably. Both are convinced the other is cutting corners. The result is not collaboration but a deadlock: a classroom defined less by learning than by a mutual suspicion over who is still doing the work that education is supposed to require.

  • The Automated Pedagogy Loop Could Threaten the Very Existence of College

    The Automated Pedagogy Loop Could Threaten the Very Existence of College

    Automated Pedagogy Loop
    noun

    A closed educational system in which artificial intelligence generates student work and artificial intelligence evaluates it, leaving human authorship and judgment functionally absent. Within this loop, instructors act as system administrators rather than teachers, and students become prompt operators rather than thinkers. The process sustains the appearance of instruction—assignments are submitted, feedback is returned, grades are issued—without producing learning, insight, or intellectual growth. Because the loop rewards speed, compliance, and efficiency over struggle and understanding, it deepens academic nihilism rather than resolving it, normalizing a machine-to-machine exchange that quietly empties education of meaning.

    The darker implication is that the automated pedagogy loop aligns disturbingly well with the economic logic of higher education as a business. Colleges are under constant pressure to scale, reduce labor costs, standardize outcomes, and minimize friction for “customers.” A system in which machines generate coursework and machines evaluate it is not a bug in that model but a feature: it promises efficiency, throughput, and administrative neatness. Human judgment is expensive, slow, and legally risky; AI is fast, consistent, and endlessly patient. Once education is framed as a service to be delivered rather than a formation to be endured, the automated pedagogy loop becomes difficult to dislodge, not because it works educationally, but because it works financially. Breaking the loop would require institutions to reassert values—depth, difficulty, human presence—that resist optimization and cannot be neatly monetized. And that is a hard sell in a system that increasingly rewards anything that looks like learning as long as it can be scaled, automated, and invoiced.

    If colleges allow themselves to slide from places that cultivate intellect into credential factories issuing increasingly fraudulent degrees, their embrace of the automated pedagogy loop may ultimately hasten their collapse rather than secure their future. Degrees derive their value not from the efficiency of their production but from the difficulty and transformation they once signified. When employers, graduate programs, and the public begin to recognize that coursework is written by machines and evaluated by machines, the credential loses its signaling power. What remains is a costly piece of paper detached from demonstrated ability. In capitulating to automation, institutions risk hollowing out the very scarcity that justifies their existence. A university that no longer insists on human thought, struggle, and judgment offers nothing that cannot be replicated more cheaply elsewhere. In that scenario, AI does not merely disrupt higher education—it exposes its emptiness, and markets are ruthless with empty products.

  • How to Resist Academic Nihilism

    How to Resist Academic Nihilism

    Academic Nihilism and Academic Rejuvenation

    Academic Nihilism names the moment when college instructors recognize—often with a sinking feeling—that the conditions students need to thrive are perfectly misaligned with the conditions they actually inhabit. Students need solitude, friction, deep reading and writing, and the slow burn of intellectual curiosity. What they get instead is a reward system that celebrates the surrender of agency to AI machines; peer pressure to eliminate effort; and a hypercompetitive, zero-sum academic culture where survival matters more than understanding. Time scarcity all but forces students to offload thinking to tools that generate pages while quietly draining cognitive stamina. Add years of screen-saturated distraction and a near-total deprivation of deep reading during formative stages, and you end up with students who lack the literacy baseline to engage meaningfully with writing prompts—or even to use AI well. When instructors capitulate to this reality, they cease being teachers in any meaningful sense. They become functionaries who comply with institutional “AI literacy policies,” which increasingly translate to a white-flag admission: we give up. Students submit AI-generated work; instructors “assess” it with AI tools; and the loop closes in a fog of futility. The emptiness of the exchange doesn’t resolve Academic Nihilism—it seals it shut.

    The only alternative is resistance—something closer to Academic Rejuvenation. That resistance begins with a deliberate reintroduction of friction. Instructors must design moments that demand full human presence: oral presentations, performances, and live writing tasks that deny students the luxury of hiding behind a machine. Solitude must be treated as a scarce but essential resource, to be rationed intentionally—sometimes as little as a protected half-hour of in-class writing can feel revolutionary. Curiosity must be reawakened by tethering coursework to the human condition itself. And here the line is bright: if you believe life is a low-stakes, nihilistic affair summed up by a faded 1980s slogan—“Life’s a bitch; then you die”—you are probably in the wrong profession. But if you believe human lives can either wither into Gollumification or rise toward higher purpose, and you are willing to let that belief inform your teaching, then Academic Rejuvenation is still possible. Even in the age of AI machines.

  • Why College Writing Instructors Must Teach the Self-Interrogation Principle

    Why College Writing Instructors Must Teach the Self-Interrogation Principle

    Self-Interrogation Principle

    noun

    The Self-Interrogation Principle holds that serious writing inevitably becomes a moral act because precise language exposes self-deception and forces individuals to confront their own motives, evasions, and contradictions. Rather than treating personal narrative as therapeutic indulgence or sentimental “enrichment,” this principle treats it as an instrument of clarity: when students articulate their behavior accurately, dysfunctional patterns lose their charm and become difficult to sustain. The aim is not confession for its own sake, nor a classroom turned talk show, but disciplined self-examination that collapses euphemism and replaces clever rationalization with honest reckoning. In this view, education cannot operate in a moral vacuum; teaching students how to think, argue, and write necessarily involves teaching them how to see themselves clearly. In the AI Age—when both cognitive labor and moral discomfort can be outsourced—the Self-Interrogation Principle insists that growth requires personal presence, linguistic precision, and the courage to endure what one discovers once illusion gives way to understanding.

    ***

    Thirty years ago, I assigned what now feels like a reckless little time bomb: a five-page extended definition essay on the term passive-aggressive. Students had to begin with a single, unsparing sentence—passive-aggressive behavior as an immature, cowardly, indirect way of expressing hostility—then unpack four or five defining traits and, finally, illustrate the concept with a personal chronicle. The goal was not linguistic finesse. It was exposure. I wanted students to stop admiring passive aggression as coy, clever, or emotionally sophisticated and see it instead for what it is: dysfunction with good PR.

    One essay has stayed with me for three decades. It came from a stunning nineteen-year-old who could have easily assembled a respectable boyfriend the way most people order coffee. Instead, she chose the town slob. He was twenty-six, unemployed by conviction, and committed to the craft of professional bumming. He was proudly unwashed, insufferably pungent, and permanently horizontal. He spent his days in her parents’ living room—drinking her father’s favorite beer, eating his snacks, parking himself in his favorite chair, and monopolizing the television like a hostile takeover. He belched. He cackled. He stank. And all the while, his girlfriend watched with satisfaction as her father’s misery fermented. She resented her father—another strong-willed soul who refused to bend—and rather than confront him directly, she opted for a scorched-earth tactic: ruin her own romantic prospects to punish him. Bite my nose to spite your face, weaponized.

    I remember her sitting across from me in my office as I read the essay, half-imagining it as a dark sitcom pilot. But there was nothing cute about it. When we talked, she told me that writing the essay forced her to see the ugliness of what she was doing with unbearable clarity. The realization filled her with such self-disgust that she ejected the boyfriend from her parents’ house and attempted, awkwardly but honestly, to confront her father directly. The assignment did two things no rubric could measure. It made her interrogate her own character, and it precipitated a real, irreversible change in her life.

    Thirty years later, I’m still unsure what to make of that. I’m gratified, of course—but uneasy. Is it my job to turn a writing class into a daytime talk show, where students inventory their neuroses and emerge “healed”? Is moral reckoning an accidental side effect of good pedagogy, or an unavoidable one?

    My answer, uncomfortable though it may be, is that a writing class cannot exist in a moral vacuum. Character matters. The courage to examine one’s own failures matters. Writing things down with enough precision that self-deception collapses under its own weight matters. Whether I like it or not, I have to endorse what I now call the Self-Interrogation Principle. Students do not come to class as blank slates hungry only for skills. They arrive starved for moral clarity—about the world and about themselves. And when language sharpens perception, perception sometimes demands change.

    I’m reminded of a department meeting in the early nineties where faculty were arguing over the value of assigning personal narratives. One professor defended them by saying they led to “personal enrichment.” A colleague—an infamous alcoholic, who sulked at meetings in his black leather jacket, appeared to be drunk at the table—exploded. “Personal enrichment? What the hell does that even mean?” he shouted as his spittle flew across the room. “Just another woeful cliché. Are you not ashamed?” The woman shrank into her chair, the meeting moved on, and the words personal enrichment was quietly banished. Today, in the AI Age, I will defend it without apology. That student’s essay was enriching in the only sense that matters: it helped a young adult grow up.

    I am not proposing that every assignment resemble an episode of Oprah. But one or two assignments that force honest self-examination have enormous value. They remind us that writing is not merely a transferable skill or a vocational tool. It is a means of moral reckoning. You cannot outsource that reckoning to a machine, and you cannot teach writing while pretending it doesn’t exist. If we are serious about education, we have to teach the Total Person—or admit we are doing something else entirely.

  • Why Student Learning Outcomes Should be Replaced with Moral Learning Outcomes

    Why Student Learning Outcomes Should be Replaced with Moral Learning Outcomes

    Moral Learning Outcomes

    noun

    Moral Learning Outcomes name a shift from evaluating what students produce to evaluating how they conduct themselves as thinkers in an age when cognition can be cheaply outsourced. Rather than measuring surface competencies—polished arguments, tidy paragraphs, or competent source integration—Moral Learning Outcomes assess intellectual integrity: the willingness to seek truth rather than confirmation, to engage opposing views fairly, to revise or abandon a thesis when evidence demands it, and to tolerate complexity instead of retreating into binary claims. These outcomes privilege forms of engagement AI cannot convincingly fake—oral defense, personal narrative anchored in lived experience, and transparent decision-making—because they require the full presence of the Total Person. In this framework, writing is not merely a technical skill but a moral practice, and education succeeds not when students sound intelligent, but when they demonstrate judgment, accountability, and the courage to think without hiding behind a machine.

    ***

    My college writing courses come packaged, like all respectable institutions, with a list of Student Learning Outcomes—the official criteria by which I grade essays and assign final marks. They vary slightly from class to class, but the core remains familiar: sustain a thoughtful argument over an entire essay; engage counterarguments and rebuttals to achieve intellectual rigor; integrate multiple sources to arrive at an informed position; demonstrate logical paragraph structure and competent sentences. In the Pre-AI Age, these outcomes made sense. They assumed that if a student produced an essay exhibiting these traits, the student had actually performed the thinking. In the AI Age, that assumption is no longer defensible. We now have to proceed from the opposite premise: that many students are outsourcing those cognitive tasks to a machine that can simulate rigor without ever practicing it.

    If that is true—and it is—then the outcomes themselves must change. To test thinking, we have to demand what AI cannot plausibly supply. This is why I recommend an oral presentation of the essay, not read aloud like a hostage statement, but delivered as a fifteen-minute speech supported by a one-page outline. AI can generate arguments; it cannot stand in a room, hold an audience, respond to presence, and make a persuasive case grounded in credibility (ethos), logic (logos), and shared human feeling (pathos). A speech requires the full human organism. Outsourcing collapses under that weight.

    The written essay, meanwhile, is scaffolded in pieces—what I call building blocks—each requiring personal narrative or reflection that must connect explicitly to the argument’s theme. If the class is writing about weight management and free will in the GLP-1 age, students write a 400-word narrative about a real struggle with weight—their own or someone close to them—and link that experience to the larger claim. If they are debating whether Frederick Douglass was “self-made,” they reflect on someone they know whose success can be read in two conflicting ways: rugged individualism on one hand, communal support on the other. If they are arguing about whether social media leads to “stupidification,” they must profile someone they know whose online life either deepened their intelligence or turned them into a dopamine-soaked attention addict. These are not confessional stunts. They are cognitive anchors.

    It would be naïve to call these assignments AI-proof. At best, they are AI-resistant. But more importantly, the work required to transform those narratives into a coherent essay and then into a live oral defense demands a level of engagement that can be measured reliably. When students stand up and defend their arguments—grounded in lived experience, research, and reflection—they are participating in education as Total Persons, not as prompt engineers.

    The Total Person is not a mystical ideal. It is someone who reads widely enough to form an informed view, and who arrives at a thesis through trial, error, and revision rather than starting with a conclusion and cherry-picking evidence to flatter it. That process requires something many instructors hesitate to name: moral integrity. Truth-seeking is not a neutral skill. It is a moral stance in a culture that rewards confirmation, outrage, and self-congratulation. Writing instructors are misfits precisely because we insist that counterarguments matter, that rebuttals must be fair, and that changing one’s mind in the face of evidence is not weakness but discipline.

    Which is why, in the AI Age, it makes sense to demote Student Learning Outcomes and elevate Student Moral Outcomes instead. Did the student explore both sides of an argument with equal seriousness? Were they willing to defend a thesis—and just as willing to abandon it when the evidence demanded? Did they resist black-and-white thinking in favor of complication and nuance? Could they stand before an audience, fully present, and deliver an argument that integrated ethos, logos, and pathos without hiding behind a machine?

    AI has forced instructors to confront what we have been doing all along. Assigning work that can be painlessly outsourced is a pedagogical failure. Developing the Total Person is not. And doing so requires admitting an uncomfortable truth: you cannot teach credible argumentation without teaching moral integrity. The two have always been inseparable. AI has simply made that fact impossible to ignore.

  • The New Role of the College Instructor: Disruption Interpreter

    The New Role of the College Instructor: Disruption Interpreter

    Disruption Interpreter

    noun

    A Disruption Interpreter is a teacher who does not pretend the AI storm will pass quickly, nor claim to possess a laminated map out of the wreckage. Instead, this instructor helps students read the weather. A Disruption Interpreter names what is happening, explains why it feels destabilizing, and teaches students how to think inside systems that no longer reward certainty or obedience. In the age of AI, this role replaces the old fantasy of professorial authority with something more durable: interpretive judgment under pressure. The Disruption Interpreter does not sell reassurance. He sells literacy in chaos.

    ***

    In his essay “The World Still Hasn’t Made Sense of ChatGPT,” Charlie Warzel describes OpenAI as a “chaos machine,” and the phrase lands because it captures the feeling precisely. These systems are still young, still mutating, constantly retraining themselves to score higher on benchmarks, sound more fluent, and edge out competitors like Gemini. They are not stabilizing forces; they are accelerants. The result is not progress so much as disruption.

    That disruption is palpable on college campuses. Faculty and administrators are not merely unsure about policy; they are unsure about identity. What is a teacher now? What is an exam? What is learning when language itself can be summoned instantly, convincingly, and without understanding? Lurking beneath those questions is a darker one: is the institution itself becoming an endangered species, headed quietly toward white-rhino status?

    Warzel has written that one of AI’s enduring impacts is to make people feel as if they are losing their grip, confronted with what he calls a “paradigm-shifting, society-remaking superintelligence.” That feeling of disorientation is not a side effect; it is the main event. We now live in the Age of Precariousness—a world perpetually waiting for a shoe to drop. Students have no clear sense of what to study when career paths evaporate mid-degree. Older generations watch familiar structures dissolve and struggle to recognize the world they helped build. Even the economy feels suspended between extremes. Will the AI bubble burst and drag markets down with it? Or will it continue inflating the NASDAQ while hollowing out everything beneath it?

    Amid this turbulence, Warzel reminds us of something both obvious and unsettling: technology has never really been about usefulness. It has been about selling transformation. A toothbrush is useful, but it will never dominate markets or colonize minds. Build something, however, that makes professors wonder if they will still have jobs, persuades millions to confide in chatbots instead of therapists, hijacks attention, rearranges spreadsheets, and rewires expectations—and you are no longer making a tool. You are remaking reality.

    In a moment where disruption matters more than solutions, college instructors cannot credibly wear the old costume of authority and claim to know where this all ends. We do not have a clean exit strategy or a proven syllabus that leads safely out of the jungle. We are more like Special Ops units cut off from command, scavenging parts, building and dismantling experimental aircraft while under fire, hoping the thing flies before it catches fire. Students are not passengers on this flight; they are co-builders. This is why the role of the Disruption Interpreter matters. It names the condition honestly. It helps students translate chaos machines into intelligible frameworks without pretending the risks are smaller than they are or the answers more settled than they feel.

    In a college writing class, this shift has immediate consequences. A Disruption Interpreter redesigns the course around friction, transparency, and judgment rather than polished output. Assignments that reward surface-level fluency are replaced with ones that expose thinking: oral defenses, annotated drafts, revision histories, in-class writing. These structures make it difficult to silently outsource cognition to AI without consequence. The instructor also teaches students how AI functions rhetorically, treating large language models not as neutral helpers but as persuasive systems that generate plausible language without understanding. Students must analyze and revise AI-generated prose, learning to spot its evasions, its false confidence, and its tendency to sound authoritative while saying very little.

    Most importantly, evaluation itself is recalibrated. Correctness becomes secondary to agency. Students are graded on the quality of their decisions: what they chose to argue, what they rejected, what they revised, and why. Writing becomes less about producing clean text and more about demonstrating authorship in an age where text is cheap and judgment is scarce. One concrete example is the Decision Rationale Portfolio. Alongside an argumentative essay, students submit a short dossier documenting five deliberate choices: a claim abandoned after research, a source rejected and justified, a paragraph cut or reworked, a moment when they overruled an AI suggestion, and a risk that made the essay less safe but more honest. A mechanically polished essay paired with thin reasoning earns less credit than a rougher piece supported by clear, defensible decisions. The grade reflects discernment, not sheen.

    The Disruption Interpreter does not rescue students from uncertainty; he teaches them how to function inside it. In an era defined by chaos machines, precarious futures, and seductive shortcuts, the task of education is no longer to transmit stable knowledge but to cultivate judgment under unstable conditions. Writing classes, reimagined this way, become training grounds for intellectual agency rather than production lines for compliant prose. AI can assist with language, speed, and simulation, but it cannot supply discernment. That remains stubbornly human. The Disruption Interpreter’s job is to make that fact unavoidable, visible, and finally—inescapable.

  • Transactional Transformation Fallacy

    Transactional Transformation Fallacy

    noun

    The Transactional Transformation Fallacy is the belief that personal change can be purchased rather than practiced. It treats growth as a commercial exchange: pay the fee, swipe the card, enroll in the program, and improvement will arrive as a deliverable. Effort becomes optional, discipline a quaint accessory. In this logic, money substitutes for resolve, proximity replaces participation, and the hard interior work of becoming someone else is quietly delegated to a service provider. It is a comforting fantasy, and a profitable one, because it promises results without inconvenience.

    ***

    I once had a student who worked as a personal trainer. She earned decent money, but she disliked the job for reasons that had nothing to do with exercise science and everything to do with human nature. Her clients were not untrained so much as uncommitted. She gave them solid programs, explained the movements, laid out sensible menus, and checked in faithfully. Then she watched them vanish between sessions. They skipped workouts on non-training days. They treated nutrition guidelines as aspirational literature. They arrived at the gym exhaling whiskey and nicotine, their pores broadcasting last night’s bad decisions like a public service announcement. They paid her, showed up once or twice a week, and mistook attendance for effort. Many were lonely. Others liked telling friends they “had a trainer,” as if that phrase itself conferred seriousness, discipline, or physical virtue. They believed that money applied to a problem was the same thing as resolve applied to a life.

    The analogy to college is unavoidable. If a student enters higher education with the same mindset—pay tuition, outsource thinking to AI, submit algorithmically polished assignments, and expect to emerge transformed—they are operating squarely within the Transactional Transformation Fallacy. They imagine education as a vending machine: insert payment, press degree, receive wisdom. Like the Scarecrow awaiting his brain from the Wizard of Oz, they expect character and intelligence to be bestowed rather than built. This fantasy has always haunted consumer culture, but AI supercharges it by making the illusion briefly convincing. The greatest challenge facing higher education in the years ahead will not be cheating per se, but this deeper delusion: the belief that knowledge, discipline, and selfhood can be bought wholesale, without friction, struggle, or sustained effort.

  • Gollumification

    Gollumification

    Gollumification

    noun

    Gollumification names the slow moral and cognitive decay that occurs when a person repeatedly chooses convenience over effort and optimization over growth. It is what happens when tools designed to assist quietly replace the very capacities they were meant to strengthen. Like Tolkien’s Gollum, the subject does not collapse all at once; he withers incrementally, outsourcing judgment, agency, and struggle until what remains is a hunched creature guarding shortcuts and muttering justifications. Gollumification is not a story about evil intentions. It is a story about small evasions practiced daily until the self grows thin, brittle, and dependent.

    ***

    Washington Post writer Joanna Slater reports in “Professors Are Turning to This Old-School Method to Stop AI Use on Exams” that some instructors are abandoning written exams in favor of oral ones, forcing students to demonstrate what they actually know without the benefit of algorithmic ventriloquism. At the University of Wyoming, religious studies professor Catherine Hartmann now seats students in her office and questions them directly, Socratic-style, with no digital intermediaries to run interference. Her rationale is blunt and bracing. Using AI on exams, she tells students, is like bringing a forklift to the gym when your goal is to build muscle. “The classroom is a gymnasium,” she explains. “I am your personal trainer. I want you to lift the weights.” Hartmann is not being punitive; she is being realistic about human psychology. Given a way to cheat ourselves out of effort—or out of a meaningful life—we will take it, not because we are corrupt, but because we are wired to conserve energy. That instinct once helped us survive. Now it quietly betrays us. A cheated education becomes a squandered one, and a squandered life does not merely stagnate; it decays. This is how Gollumification begins: not with villainy, but with avoidance.

    I agree entirely with Hartmann’s impulse, even if my method would differ. I would require students to make a fifteen-minute YouTube video in which they deliver their argument as a formal speech. I know from experience that translating a written argument into an oral one exposes every hollow sentence and every borrowed idea. The mind has nowhere to hide when it must speak coherently, in sequence, under the pressure of time and presence. Oral essays force students to metabolize their thinking instead of laundering it through a machine. They are a way of banning forklifts from the gym—not out of nostalgia, but out of respect for the human organism. If education is meant to strengthen rather than simulate intelligence, then forcing students to lift their own cognitive weight is not cruelty. It is preventive medicine against the slow, tragic, and all-too-modern disease of Gollumification.

  • Hyper-Efficiency Intoxication Will Change Higher Learning Forever

    Hyper-Efficiency Intoxication Will Change Higher Learning Forever

    Hyper-Efficiency Intoxication

    noun

    The dopamine-laced rush that occurs when AI collapses hours of cognitive labor into seconds, training the brain to mistake speed for intelligence and output for understanding. Hyper-Efficiency Intoxication sets in when the immediate relief of reclaimed time—skipped readings, instant summaries, frictionless drafts—feels so rewarding that slow thinking begins to register as needless suffering. What hooks the user is not insight but velocity: the sense of winning back life from effort itself. Over time, this chemical high reshapes judgment, making sustained attention feel punitive, depth feel inefficient, and authorship feel optional. Under its influence, students do not stop working; they subtly downgrade their role—from thinker to coordinator, from writer to project manager—until thinking itself fades into oversight. Hyper-Efficiency Intoxication does not announce itself as decline; it arrives disguised as optimization, quietly hollowing out the very capacities education once existed to build.

    ***

    No sane college instructor assigns an essay anymore under the illusion that you’ll heroically wrestle with ideas while AI politely waits in the hallway. We all know what happens: a prompt goes in, a glossy corpse comes out. The charade has become so blatant that even professors who once treated AI like a passing fad are now rubbing their eyes and admitting the obvious. Hua Hsu names the moment plainly in his essay “What Happens After A.I. Destroys College Writing?”: the traditional take-home essay is circling the drain, and higher education is being forced to explain—perhaps for the first time in decades—what it’s actually for.

    The problem isn’t that students are morally bankrupt. It’s that they’re brutally rational. The real difference between “doing the assignment” and “using AI” isn’t ethics; it’s time. Time is the most honest currency in your life. Ten hours grinding through a biography means ten hours you’re not at a party, a game, a date, or a job. Ten minutes with an AI summary buys you your evening back. Faced with that math, almost everyone chooses the shortcut—not because they’re dishonest, but because they live in the real world. This isn’t cheating; it’s survival economics.

    Then there’s the arms race. Your classmates are using AI. All of them. Competing against them without AI is like entering a bodybuilding contest while everyone else is juiced to the gills and you’re proudly “all natural.” You won’t be virtuous; you’ll be humiliated. Fairness collapses the moment one side upgrades, and pretending otherwise is naïve at best.

    AI also hooks you. Hsu admits that after a few uses of ChatGPT, he felt the “intoxication of hyper-efficiency.” That’s not a metaphor—it’s a chemical event. When a machine collapses hours of effort into seconds, your brain lights up like it just won a small lottery. The rush isn’t insight; it’s velocity. And once you’ve tasted that speed, slowness starts to feel like punishment.

    Writing instructors, finally awake, are adapting. Take-home essays are being replaced by in-class writing, blue books, and passage identification exams—formats designed to drag thinking back into the room and away from the cloud. These methods reward students who’ve spent years reading and writing the hard way. But for students who entered high school in 2022 or later—students raised on AI scaffolding—this shift feels like being dropped into deep water without a life vest. Many respond rationally: they avoid instructors who demand in-class thinking.

    Over time, something subtle happens. You don’t stop working; you change roles. You become, in Hsu’s phrase, a project manager—someone who coordinates machines rather than generating ideas. You collaborate, prompt, tweak, and oversee. And at some point, no one—not you, not your professor—can say precisely when the thinking stopped being yours. There is no clean border crossing, only a gradual fade.

    Institutions are paralyzed by this reality. Do they accept the transformation and train students to be elite project managers of knowledge? Or do they try to resurrect an older model of literacy, pretending that time, incentives, and technology haven’t changed? Neither option is comfortable, and both expose how fragile the old justifications for college have become.

    From the educator’s chair, the nightmare scenario is obvious. If AI can train competent project managers for coding, nursing, physical therapy, or business, why not skip college altogether? Why not certify skills directly? Why not let employers handle training in-house? It would be faster, cheaper, and brutally efficient.

    And efficiency always wins. When speed, convenience, and cost savings line up, they don’t politely coexist with tradition—they bulldoze it. AI doesn’t argue with the old vision of education. It replaces it. The question is no longer whether college will change, but whether it can explain why learning should be slower, harder, and less efficient than the machines insist it needs to be.