Cinemorphosis

Tag: artificial-intelligence

  • Forty Years Later, I Finally Learned How to Teach Writing

    Forty Years Later, I Finally Learned How to Teach Writing

    This morning was bittersweet. Assuming I don’t substantially revise my curriculum for one final spring semester, I may have written my last syllabus after forty years of teaching college composition.

    My freshmen will spend the semester wrestling with subjects that would have been unimaginable when I began teaching in the 1980s. They will analyze the epidemic of sports betting in a culture of loneliness, examine how algorithmic platforms reshape identity and ambition, and challenge the mythology of the self-made man through the life and writings of Frederick Douglass.

    As I finished organizing my notes, an uncomfortable realization settled over me: I am retiring just as I feel I have finally learned how to do this job well.

    I have always been a slow learner. It took me four decades to discover how to design a writing course that feels intellectually coherent rather than merely competent. Only now do I feel that the readings, assignments, and classroom conversations reinforce one another with genuine purpose. Just as the picture has come into focus, I find myself stepping off the stage.

    I also have mixed feelings about OpenAI platforms. On one hand, they have created an endless stream of grading headaches. Every semester brings hundreds of essays that bear the unmistakable fingerprints of AI splatter—competent-looking prose that collapses under scrutiny because no real mind seems to inhabit it. I return many of these papers with one instruction: start over.

    Yet these same tools have also made teaching writing more intellectually exciting than it has been in years. I have used them to sharpen essay prompts, generate competing arguments, refine classroom activities, and demonstrate to students how AI can become an editorial collaborator rather than an intellectual substitute. I realize that sounds almost sacrilegious coming from a writing professor, but adapting to these technologies has rekindled my curiosity about cognition, literacy, learning, and composition. We do not yet know where this technological revolution is taking us, and I doubt anyone can fully control its direction. But the questions it raises are among the most fascinating of my career.

    My greatest concern is not artificial intelligence itself. It is the widening gap between students who possess deep literacy and those who do not. Students who have spent years reading demanding books can interrogate AI, challenge its assumptions, recognize its mistakes, and use it to strengthen their own thinking. Students raised primarily on TikTok and algorithmically curated snippets often lack that foundation. For them, AI becomes less a tool than a surrogate mind—a magical genie that produces respectable-looking mediocrity while quietly encouraging them to outsource their judgment, curiosity, and intellectual will. The danger is not that AI will make students less intelligent. The danger is that it will make them comfortable never developing the habits of mind that intelligence requires.

    Teaching students to acquire the literacy necessary to use these tools wisely may become the defining challenge of higher education over the next generation. That is one reason retirement feels bittersweet. Just as this frontier is opening, I am leaving the profession.

    Looking back over forty years, however, gratitude overwhelms regret. I was fortunate to spend my working life in a profession that demanded perpetual intellectual growth. Every semester required me to become a slightly better reader, writer, and thinker. I never tired of teaching argumentation, counterarguments, rebuttals, rhetorical strategy, or the mysterious alchemy that transforms an elephantine sentence into something lean, graceful, and memorable.

    Writing has always fascinated me because most of it disappears the moment it is read. Nearly everything we encounter is competent enough, clear enough, forgettable enough. Then, every so often, a sentence possesses that rare quality I call salience. It arrests your attention. It feels inevitable, as though no other arrangement of words could possibly exist. Discovering how writers achieve that effect—how they impose order, precision, and beauty upon the chaos of language—has remained one of the enduring pleasures of my life.

    Perhaps my final lecture should not be about grammar, thesis statements, or MLA format. Perhaps I should gather these reflections into a Google Slides presentation and offer my students a panoramic view of why writing classes matter more today than at any other moment in my career. Artificial intelligence has not diminished the value of literacy. It has elevated it. In an age when machines can imitate competent prose, the truly educated writer will not be the one who produces the most words. It will be the one who possesses enough judgment, imagination, and intellectual character to know which words are worth writing in the first place.

  • Why Mike Pesca Chose Algorithmic Exile

    Why Mike Pesca Chose Algorithmic Exile

    For more than a decade, Mike Pesca built one of the most respected podcasts in the business. His conversations were thoughtful rather than performative, his interviews nuanced rather than sensational, and his political analysis resisted the tribal certainty that dominates so much online discourse. By conventional standards, he had already succeeded.

    Yet success in today’s media ecosystem is no longer enough. To keep growing, creators must increasingly submit themselves to the demands of opaque algorithms that reward relentless engagement, perpetual self-promotion, and an endless stream of optimized content. Pesca looked at that landscape and concluded that the price of further growth was too high.

    It was not that he lacked the ability to master the algorithm. Rather, he had no desire to become the sort of person whose professional life revolved around pleasing it. The daily rituals required to stimulate engagement struck him as alienating. They demanded a temperament and set of priorities that no longer resembled the person he wanted to be.

    Standing at this fork in the road, Pesca faced two paths. One led toward greater scale by embracing whatever the algorithm demanded. The other required walking away from a successful enterprise he had spent years building. He chose the latter, entering what might be called algorithmic exile: the voluntary withdrawal from a profession or platform because its algorithmic incentives have become incompatible with one’s identity, values, or conception of meaningful work.

    In doing so, Pesca honored what might be called the Identity Preservation Threshold Principle: the point at which further professional success requires so much adaptation to external incentives that a person refuses to proceed in order to preserve a coherent sense of self. His departure was therefore not an admission of defeat. It was a declaration that there are forms of success not worth pursuing because they demand the slow erosion of the very character that made the work worth doing in the first place.

  • Can Philosophers Keep Their Souls in Silicon Valley?

    Can Philosophers Keep Their Souls in Silicon Valley?

    In “Someone Finally Wants to Hire Philosophers,” Lila Shroff reports what would have sounded like a punchline only a decade ago: philosophy majors may finally be getting the last laugh. For years, philosophy occupied an awkward place in the public imagination—a discipline associated with coffee-shop debates, existential handwringing, and the noble art of explaining to relatives why you were unemployed. At best, the philosopher was a thoughtful gadfly. At worst, a professional overthinker. But the rise of artificial intelligence has suddenly transformed philosophy from an intellectual curiosity into a marketable skill. Major technology companies are hiring philosophers. Universities are recruiting scholars who specialize in both AI and philosophy. The old joke about philosophy leading nowhere is beginning to age badly.

    As Shroff notes, this development should not surprise us. Philosophers have been wrestling with questions about intelligence, consciousness, morality, and the possibility of artificial minds for centuries. Long before Silicon Valley executives promised to change the world, philosophers were already asking whether a machine could think, reason, or possess something resembling a mind. Today, thinkers such as Nick Bostrom have become influential voices in the AI conversation. His book Superintelligence warned more than a decade ago that humanity might create machines whose capabilities outstrip our ability to control them. What once sounded like speculative science fiction now reads more like a boardroom agenda.

    The marriage between AI and philosophy arises from a practical concern. Technology companies want their products to appear ethical, trustworthy, and safe. A machine that accidentally promotes fraud, discrimination, or social chaos is difficult to market. Consumers are more likely to embrace AI systems that project wisdom, fairness, and restraint. In the increasingly crowded AI marketplace, virtue has become a product feature. Safety, ethics, and responsibility are not merely moral concerns; they are branding opportunities.

    Yet Shroff’s essay leaves several uncomfortable questions lingering in the air.

    First, philosophers disagree about nearly everything. That is practically the job description. If ethical questions routinely produce competing schools of thought, which philosophers do AI companies choose to hire? A utilitarian, a virtue ethicist, a libertarian, and a nihilist might evaluate the same problem and arrive at wildly different conclusions. When an AI company claims to be guided by philosophy, whose philosophy is it talking about?

    Second, corporations do not operate in a vacuum. They pursue growth, market share, influence, and profit. Given those incentives, it seems unlikely that technology companies will eagerly recruit philosophers whose views fundamentally conflict with corporate objectives. The philosopher who questions the legitimacy of the enterprise may not receive the same warm welcome as the philosopher who helps polish its public image.

    Third, what happens to philosophy itself when it becomes a lucrative career path? If technology firms reward certain ethical frameworks and ignore others, philosophers may gradually adapt their views to become more employable. Intellectual independence has always been easier to defend when no one is writing the check. Once prestige, influence, and six-figure salaries enter the picture, even the most principled thinkers may find themselves sanding off inconvenient beliefs.

    This is why I remain skeptical of any celebration of philosophy’s new status in the AI economy. There is no such thing as pure philosophy floating above human ambition. There are only human beings, complete with incentives, blind spots, loyalties, and self-interest. The partnership between AI and philosophy may produce genuinely useful ethical guidance. Or it may become an elaborate exercise in corporate virtue theater—a dazzling display of moral concern performed beneath bright lights while the machinery of profit hums steadily backstage. Whether philosophers become the conscience of artificial intelligence or merely its public relations department remains an open question.

  • Prove You Offer Something AI Cannot, or Become Obsolete 

    Prove You Offer Something AI Cannot, or Become Obsolete 

    After nearly forty years teaching college writing, I can say this without ceremony: AI didn’t knock—it walked in and rearranged the furniture. We weren’t ready. Now we’re scrambling to answer basic questions we once took for granted: What is our role? What, exactly, is our job? How do we evaluate a student’s work when the student can outsource the thinking? And how do we defend the value of higher education—especially the Humanities—to a public that is already drifting away?

    The backdrop isn’t kind. We face a culture that reads less and skims more, chasing quick hits of stimulation instead of sustained thought. The cost of college keeps rising while the promise of stable, predictable employment grows less certain. In that context, debates about AI feel less like speculation and more like triage. The question isn’t whether AI will reshape higher education; it’s how deeply and how quickly.

    In that vein, I read Jay Caspian Kang’s essay “Why the Future of College Could Look Like OnlyFans” with particular interest. Kang highlights the argument of Hollis Robbins, who offers a blunt standard for survival: universities justify their existence only if they provide access to faculty whose expertise exceeds what AI can deliver. Strip away the academic language, and the message is stark: prove you offer something AI cannot, or become obsolete.

    Robbins pushes the point further. In a classroom saturated with generative AI, she predicts a severe contraction—potentially eliminating a large share of faculty positions. Courses built around foundational, repeatable skills—the kind often taught in the first two years—are the most vulnerable. Specialized fields, particularly those grounded in rare knowledge or deep cultural expertise, are far less exposed. The more interchangeable the instruction, the easier it is to replace.

    That leaves instructors like me in an uneasy position. I’ve spent decades teaching literacy and critical thinking—the very skills that AI now performs with unnerving competence. If Robbins is right, those of us in broad, introductory disciplines face the sharpest edge of change. The specialists may endure. The rest of us may be folded into what she envisions as a rapid and unforgiving contraction.

    This isn’t a distant possibility. It’s a present demand. Adapt—or be edited out.

  • The Day I Logged Off the AI Panic Machine and Walked at the Beach

    The Day I Logged Off the AI Panic Machine and Walked at the Beach

    I teach college writing, which means I’ve spent the last four years staring at the AI question the way a man stares at a fire he suspects might jump the fence. When ChatGPT arrived, it didn’t knock politely. It crashed into the room like a UFO and rearranged the furniture. Since then, I’ve read what feels like a small library’s worth of essays—predictions, warnings, elegies for the essay itself—and contributed a few of my own, because that’s what we do: we metabolize disruption by writing about it.

    But there comes a point when the analysis stops clarifying and starts echoing.

    I’ve reached that point. My brain has filed a quiet injunction: no more. Not just a break from AI, but a break from reading about how exhausted everyone else is by AI. The discourse has become a hall of mirrors—each reflection slightly more fatigued than the last.

    I’ve been here before. In 2010, I had newborn twins, which is another way of saying I was living inside a low-grade emergency. The market offered guidance—books, podcasts, earnest experts—but I wanted none of it. I was already doing the job. Additional commentary felt like a second shift. Experience was loud enough; analysis was just noise layered on top.

    Both episodes point to the same condition: Applied Reality Rejection—the refusal to consume secondary discourse when you’re already neck-deep in the primary experience. When you’re in it, more talk about it doesn’t help. It dilutes.

    And here’s the part the essays rarely admit: reading about AI doesn’t soothe AI anxiety. It compounds it. Each think piece arrives like a fresh weather report announcing the same storm in slightly different prose.

    So I’m choosing friction of a better kind. I play the piano until my attention steadies. I pick up kettlebells and let gravity argue with me for a while. I walk the beach and let the horizon do what no article can—put scale back into the day. The analog world doesn’t theorize; it recalibrates.

    That was the remedy with the twins, too. Not another podcast on sleep training, but a walk, a dumb TV binge, a sweaty hour in the garage. Relief came from stepping out of the commentary loop, not diving deeper into it.

    Which is why, when I see another AI essay queued up from The Atlantic or The New Yorker, I feel a familiar tightening—and then I close the tab. Not out of contempt, but out of preservation.

    I’ve heard enough echoes. It’s time to drive two miles to Catalina Avenue and take a walk at the beach.

  • How It Feels to Grade 60 Original Essays Edited by AI

    How It Feels to Grade 60 Original Essays Edited by AI

    I assigned my students an essay that asked them to describe a place both ugly and formative—a crucible that hurt them and, in the same breath, made them. The submissions came back like a map of pressure points: a high school classroom that felt like a courtroom, a gym that smelled of rubber and dread, a mental health ward lit like an aquarium, a pre-op room where the clock ticked louder than courage, a soccer field that taught hierarchy and grace, a family home in El Salvador, a Korean farm where labor spoke in blisters. The content was theirs—specific, unborrowed, alive. But the sentences often arrived wearing a suspicious polish, the prose lacquered to a showroom shine. You could feel the editor in the room, invisible and tireless.

    I keep returning to a metaphor I can’t shake: AI is like a bodybuilder taking steroids for writing. Go in “natty,” and you present a muscular physique that is honest–well defined, maybe even impressive. Add the chemical assist and you step onstage thirty percent larger, veins penciled in, every line exaggerated into spectacle. 

    After sixty of these eye-popping essays, I felt the same deadening I get at a bodybuilding show. At first you admire the craft; then the sameness creeps in. The poses change; the effect doesn’t. Everything looks like everything else.

    This is my ambivalence, and it refuses to resolve. On one hand, AI hands students a language upgrade that would make a New York editor nod—clarity, rhythm, a vocabulary that lands. It’s as if they’ve been fast-tracked to a professional register. On the other hand, that very upgrade dilutes the experience. When strong language grows out of a human mind, it carries the friction of effort—the faint grit that makes it feel earned, inhabited. When it arrives laundered through a machine—the “stochastic parrot” Emily M. Bender warned us about—it can be dazzling and hollow at once, a chandelier with no wiring. The sentences glitter; the room stays dark.

    I’ve graded hundreds of essays for years and thought I knew the terrain—the tells of struggle, the leap from draft to draft, the moment a voice becomes unmistakably its own. Now I’m reading in a new jurisdiction with no settled law. I’m less a judge than a border agent, inspecting passports that all look freshly printed. Welcome to the literary Wild West: the gold is real, the essays are suspect, and every nugget asks the same question—where did you get this?

  • The Rise of the Cyborg Student and the Collapse of Learning

    The Rise of the Cyborg Student and the Collapse of Learning

    In her Atlantic essay “Is Schoolwork Optional Now?”, Lila Shroff describes a classroom that has quietly slipped its friction. Students entering high school around 2024 have discovered that schoolwork—once a slog of half-formed ideas, crossed-out sentences, and mild despair—can now be outsourced with the elegance of a corporate merger. With tools like Claude Code, they recline while a digital understudy attends class on their behalf, taking quizzes, drafting lab reports, and assembling PowerPoints with the glossy finish of a mid-level consultant angling for a promotion.

    Teachers respond with variety, as if novelty could outpace automation. More assignments, different formats, new prompts. It doesn’t matter. The students simply retrain their AI to shapeshift into whatever species of learner is required: the earnest analyst, the reflective humanist, the data-savvy pragmatist. The submissions arrive immaculate—coherent, polished, and suspiciously free of the small humiliations that once marked actual thinking.

    The problem is not that the work gets done. It’s that no one is being worked on. The transformation has shifted from mind to method. Students aren’t learning the material; they’re learning how to manage a machine that can impersonate someone who did.

    If that weren’t enough, the next escalation has arrived with a name designed to soothe your nerves: Einstein. This AI agent claims it can log into platforms like Canvas and complete an entire semester’s workload in a single day. It doesn’t just skim the surface. It watches lectures, digests readings, writes essays, posts discussion comments, submits assignments, and takes exams—leaving behind a digital paper trail so competent it borders on smug.

    Shroff decided to test the promise. She enrolled in an online statistics course and turned Einstein loose. Within an hour, it had completed the entire semester of work: eight modules and seven quizzes. She earned a perfect score. She also learned, by her own account, almost nothing. The grade was real. The education was imaginary.

    Einstein’s creator, Advait Paliwal, is a 22-year-old who speaks with the calm inevitability of someone announcing the weather. His argument is simple: this is a warning. Adapt or become decorative. Educators have responded with lawsuits and cease-and-desist letters, which he treats as polite acknowledgments that the problem is larger than any one person. If he hadn’t built it, someone else would have. And if you find Einstein alarming, he assures us, you should pace yourself—this is the beta version of the apocalypse. “There’s more to come.”

    Meanwhile, Silicon Valley is not retreating. It is accelerating, pouring resources into embedding AI deeper into the educational bloodstream. The irony is almost too clean: educators are losing control not only because the technology can’t be contained, but because they use it themselves. AI grades papers, drafts materials, streamlines feedback. It makes the job more efficient. It also quietly rewrites what the job is.

    The endgame is already visible. It has a name that sounds like a software feature but reads like a verdict: the Fully Automated Loop. AI generates the assignments. AI completes them. AI grades them. The student, once the point of the enterprise, becomes a spectator to a closed circuit of competence.

    We used to worry about students not doing the work. Now the work does itself.

    And when that loop closes, education doesn’t collapse in a dramatic heap. It hums. It functions. It produces results.

    It just stops producing people.

  • Acid-Washed Jeans and Artificial Intelligence: The Rise and Fall of Instant Cool

    Acid-Washed Jeans and Artificial Intelligence: The Rise and Fall of Instant Cool

    I have a confession that belongs in the Museum of Bad Decisions: I wore acid-washed jeans in the 80s. Not casually. Not ironically. I wore them to teach college writing at twenty-four, convinced I was the cool professor—the kind of man who could annotate a thesis statement and headline a Duran Duran video without changing outfits.

    The problem, of course, is that everyone thought they were that guy. Acid-washed jeans thrived because they delivered instant mythology. You looked like you had lived—hard, fast, dangerously—when in reality you had simply survived a trip to the mall. They were rebellion by chemical treatment, authenticity by rinse cycle. For a brief, glittering moment, that illusion worked. But illusions collapse under mass adoption. When everyone looks distressed, no one looks interesting. The jeans had nowhere to go; they began at maximum volume and stayed there, screaming. Eventually, the culture regained its hearing, glanced downward, and realized it had dressed itself like survivors of a denim-related explosion. Acid wash didn’t fade—it was exiled.

    I think about that rise and fall when I look at my students’ shifting attitude toward AI. In 2022, AI arrived like those jeans: a miracle fabric promising salvation from drudgery, writer’s block, and the existential dread of the blank page. It offered pre-fabricated brilliance—the intellectual version of showing up to the gym already sweating. Students embraced it with the same breathless certainty that this time, finally, the shortcut would make them exceptional.

    Now? They roll their eyes. They call it cringey.

    What changed is not the technology but the perception of authenticity. Factory-installed insight, like factory-installed distress, has become suspect. My students are not naïve; they have finely tuned detectors for fraud. They live in a world saturated with performance—the influencer selling a life they don’t live, the hollow expert recycling borrowed ideas, the unprepared instructor filling class time by sharing his dreams and domestic dramas while they politely tune him out and read Tolstoy’s War and Peace or the entire oeuvre of J.K. Rowling. 

    AI, at its worst, slots neatly into that ecosystem. It produces language that sounds like thinking without the inconvenience of actually thinking. And my students can hear the hollowness.

    This does not mean AI is useless. At its best, it belongs alongside Word, Google Docs, and Grammarly—a tool, not a personality. But tools do not build a self. They do not generate voice, conviction, or the slow accumulation of insight that makes writing worth reading. Lean on them too heavily, and the result isn’t mastery—it’s dependency dressed up as efficiency.

    My students understand this. That’s why the fever has broken. The early hype—the belief that AI would function as a kind of intellectual superpower—has lost its grip. The spell didn’t shatter because AI failed. It shattered because people learned to recognize the difference between something that helps you think and something that pretends to think for you.

    Acid-washed jeans didn’t disappear because denim stopped working. They disappeared because people grew embarrassed of the shortcut.

    AI isn’t going anywhere.

    But the illusion that it can make you interesting just by wearing it?

    That’s already out of style.

  • The Semester When Students Got Tired of AI Slop

    The Semester When Students Got Tired of AI Slop

    My critical thinking class this spring has produced something I have not seen in several years: essays that sound like they were written by human beings.

    The first two mini-essays show almost no signs of AI cheating. Students wrote about the theme of “optimization without integration” in the Black Mirror episode “Joan Is Awful,” and about toxic positivity and infantilization in “Rachel, Jack, and Ashley Too.” These are not easy concepts. Yet the writing has been thoughtful, uneven in places, occasionally clumsy—in other words, unmistakably human.

    Part of the explanation lies in the design of the assignments. I structured them as hybrids. Students begin with a single analytical paragraph about the episode itself. Then they pivot and connect the theme to their own lives. The second step is the key. AI can summarize television episodes all day long, but it has a harder time fabricating the peculiar messiness of someone’s actual life.

    But the assignments alone do not explain the shift.

    Conversations with students suggest something more interesting is happening: they are tired of AI. Not ethically troubled, not philosophically conflicted—simply exhausted. They complain about what they call AI slop: bloated paragraphs that say everything and mean nothing, prose that sounds like a motivational speaker trapped inside a thesaurus.

    They are burned out on the smooth, inflated voice of the machine.

    What they seem to want instead is something refreshingly primitive—authentic expression. The Black Mirror episodes help. The themes are sharp, strange, and slightly disturbing, which gives students something real to react to. They also appreciate that the assignments are short—well under 1,000 words. These essays function as warm-ups before the larger research papers later in the semester.

    The result, at least so far, is encouraging.

    After four years of watching AI creep into every corner of student writing, I may be seeing the beginning of a recalibration. Students appear to be treating AI less like a magic genie that produces instant essays and more like what it actually works best as: a tool for editing and cleanup.

    I could be misreading the moment. Trends in education are famous for evaporating the second you start feeling optimistic.

    But for now, the classroom sounds different.

    The paragraphs have fingerprints on them again.

  • The Sweet Tooth Age: How We Traded Depth for Dopamine

    The Sweet Tooth Age: How We Traded Depth for Dopamine

    In “The Orality Theory of Everything,” Derek Thompson makes a striking observation about human progress. One of civilization’s great turning points was the shift from orality to literacy. In oral cultures, knowledge traveled through speech, storytelling, and shared memory. Communication was social, flexible, and immediate. Literacy changed everything. Once ideas could be recorded, people could think alone, think slowly, and think deeply. Writing made possible the abstract systems—calculus, physics, modern biology, quantum mechanics—that underpin the technological world. The move from orality to literacy didn’t just change communication. It changed the human mind.

    Now the concern is that we may be drifting in the opposite direction.

    As social media expands, sustained reading declines. Attention fragments. Communication becomes faster, louder, and more performative. Thompson explored this shift in a conversation with Joe Weisenthal of the Odd Lots podcast, who draws heavily on the work of Walter Ong, the Jesuit scholar who wrote Orality and Literacy. Ong’s insight was simple but profound: when ideas are not recorded and preserved, people think differently. They rely on improvisation, memory shortcuts, and conversational instinct. But when ideas live in texts—books, essays, archives—people develop interiority: the capacity for reflection, precision, and layered analysis.

    It would be too simple to say we now live in a post-literate society. We still read. We still write. But the cognitive environment has changed. Our brains increasingly gravitate toward information that is fast, simplified, and emotionally stimulating. The habits required for what Cal Newport calls “deep work” now feel unnatural, even burdensome.

    A useful analogy is food. Literacy is like preparing a slow, nutritious meal. It requires time, effort, and attention, but the nourishment is real and lasting. The current media environment offers something else entirely: intellectual candy. Quick hits. Bright packaging. Strong flavor. Minimal substance. We have entered what might be called the Sweet Tooth Age—a culture that prefers pre-digested, entertaining fragments of ideas over sustained, solitary engagement. The concepts may sound serious, but they arrive in baby-food form: softened, sweetened, and stripped of complexity.

    After forty years of teaching college writing, I’ve watched this shift unfold in real time. In the past six years especially, many instructors have adjusted their expectations. Reading loads have shrunk. Full books are assigned less often. In an effort to get authentic, non-AI responses, more teachers rely on in-class writing. Some have abandoned homework entirely and grade only what students produce under supervision.

    This strategy has practical advantages. It guarantees original work. It keeps students accountable. But it also reflects a quiet surrender to the Sweet Tooth Age. The modern workplace—the environment our students are entering—runs on the same quick-cycle attention economy. Their exposure to slow thinking may be brief and largely confined to the classroom. When they transition to their careers, they may find that on-demand writing is no longer required or relevant. 

    Not just education but politics and culture are being swept by this new age of dopamine cravings. The Sweet Tooth Age carries a cost, and the bill will come due.

    The content that wins in the attention economy is not the most accurate or thoughtful. It is the most stimulating. It is colorful, simplified, emotionally charged, and designed to produce a quick surge of interest—what the brain experiences as a dopamine reward. But reacting to stimulation is not the same as thinking. Performance is not analysis.

    Performance, in fact, is the preferred tool of the demagogue.

    When audiences lose the habit of slow reading and critical evaluation, they become vulnerable to what might be called Kayfabe personalities—figures who are larger than life, theatrical, and emotionally compelling, but who operate more like entertainers than honest brokers. The message matters less than the performance. Complexity disappears. Nuance becomes weakness. Certainty, outrage, and spectacle take center stage.

    In such an environment, critical thinking doesn’t merely decline. It becomes a competitive disadvantage.

    This is why the Sweet Tooth Age is more than an educational concern. It is a political and cultural risk. A public trained to consume stimulation rather than evaluate evidence becomes easy to mobilize and difficult to inform. Emotion outruns judgment. Identity replaces analysis. The center—built on patience, evidence, and compromise—struggles to hold.

    When literacy weakens, the consequences do not remain confined to the classroom.

    They spread outward—into public discourse, institutional trust, and civic stability. The shift back toward orality is not simply a change in media habits. It is a shift toward immediacy over reflection, reaction over reasoning, spectacle over substance.

    And when a culture begins to prefer performance to thought, chaos is not an accident.

    It is the logical outcome.