AI Didn't Break Higher Education. It Just Exposed the Expensive Scam We Were Already Paying For.

By the time artificial intelligence arrived on campus, higher education had already confused learning with credentials, students with customers, and academic achievement with a very expensive piece of paper.

There's something almost adorable about watching university administrators suddenly discover that artificial intelligence might destroy the integrity of higher education. These are the same institutions that spent decades raising tuition, expanding administrative bureaucracies, treating students like revenue streams, and convincing eighteen-year-olds that borrowing the equivalent of a modest mortgage was a perfectly reasonable way to discover their passion for medieval literature. Now, apparently, the educational apocalypse has arrived because a student can ask ChatGPT to write a five-page essay about Shakespeare in twelve seconds. Forgive me if I don't immediately reach for a box of tissues. Higher education was already standing in the middle of a burning building, discussing whether the curtains matched the furniture. Artificial intelligence didn't light the original fire. It simply walked through the front door carrying a flamethrower and asked why everyone was pretending the smoke was part of the curriculum.

A recent essay by Jeff Sparrow, published through The Conversation under the title "Friday essay: AI is devastating higher education, but the system was already broken," makes an argument that deserves considerably more attention than the usual academic hand-wringing. Sparrow suggests that the growing dependence on artificial intelligence isn't simply a technological problem. It's the predictable consequence of universities increasingly organized around efficiency, measurable output, commercial incentives, and institutional survival rather than actual intellectual development. In other words, the machines are thriving because we spent years redesigning education to behave like a machine. I find that observation both convincing and deeply uncomfortable. It also raises an even nastier question: If students can outsource their thinking to computers, professors can outsource their grading to computers, and universities can continue collecting tuition, exactly who is participating in the educational process anymore? https://www.inkl.com/news/friday-essay-ai-is-devastating-higher-education-but-the-system-was-already-broken

Welcome to the University of Whatever Gets You Through

I've always found the modern university's marketing language fascinating. Every institution promises transformation, opportunity, leadership, innovation, and lifelong discovery. You would think enrolling in college involved joining an ancient philosophical society dedicated to unlocking the secrets of human existence. Then you look at the actual experience and discover overcrowded classes, stressed instructors, overpriced textbooks, confusing requirements, and an administrative office that somehow needs three weeks to answer an email about a parking permit. The brochure sells intellectual enlightenment. The business model sells credit hours. Somewhere between those two realities, students are supposed to develop a profound appreciation for knowledge while figuring out how to afford groceries.

I'm not suggesting that universities provide nothing valuable. Some of the most important scientific discoveries, technological advances, and intellectual breakthroughs emerge from academic institutions. Dedicated professors change lives. Serious students encounter ideas that fundamentally alter their understanding of the world. Research universities contribute enormously to medicine, engineering, literature, and public understanding. Those achievements deserve recognition. My complaint is that higher education increasingly uses its genuine accomplishments to defend practices that have very little to do with them. The brilliant researcher developing a treatment for cancer becomes a convenient symbol for an institution that also employs an army of consultants to decide whether its next branding campaign should emphasize excellence or exceptional excellence.

The problem begins when education stops being understood primarily as a human experience and becomes a product that must be packaged, priced, measured, and delivered. Once students become customers, the entire relationship changes. Customers expect satisfaction. Institutions want retention. Administrators want enrollment. Financial departments want predictable revenue. Professors are still expected to maintain academic standards, but they operate inside organizations with strong incentives to keep tuition-paying students moving through the system. Nobody needs to announce that learning has become secondary. The structure communicates that message perfectly well without anyone saying it out loud.

And students aren't stupid. They notice.

They understand when a class seems designed around completing standardized assignments rather than exploring complicated ideas. They recognize when discussion boards exist primarily to satisfy participation requirements. They know the difference between an instructor who engages with their work and one who appears to be processing submissions on an assembly line. When a university treats education as a transaction, students eventually start treating their coursework the same way. The institution wants completed assignments. The student wants completed credits. Artificial intelligence offers to make the exchange faster. Everybody gets what the system appears to reward, except the inconvenient little detail called learning.

The $100,000 Receipt for Intellectual Development

Let's talk about money, because every conversation about the future of education eventually crashes into the financial reality that universities would prefer to decorate with inspirational banners.

American higher education has convinced generations of families that obtaining a degree can be worth enormous financial sacrifice. And for many graduates, college does improve career opportunities and lifetime earnings. I'm not interested in pretending otherwise. What bothers me is the casual assumption that because education can be valuable, almost any price attached to it must therefore be justified. Those are two very different propositions, yet the distinction often disappears somewhere between the admissions presentation and the financial aid paperwork.

I can imagine the absurdity of a student borrowing tens of thousands of dollars to attend an institution that assigns readings available online, requires essays generated by artificial intelligence, and evaluates those essays using another artificial intelligence system. At some point, the student deserves to ask what the tuition is actually purchasing. Access to information? Much of that already exists outside university walls. Feedback? That may increasingly arrive through automated systems. Credentials? Now we're getting closer to the uncomfortable center of the business model.

The degree remains enormously important because employers, professional institutions, and social expectations continue to treat credentials as evidence of competence. But a credential and an education aren't identical. One certifies that certain requirements were satisfied. The other means something meaningful happened inside a person's mind. Ideally, those experiences overlap. Increasingly, I worry that we're building a system sophisticated enough to distribute the first without reliably delivering the second.

Artificial intelligence makes that distinction impossible to ignore. A diploma cannot tell me whether a graduate wrestled with difficult ideas or simply learned how to produce acceptable academic output. Of course, that problem existed before generative AI. Students could memorize information temporarily, purchase essays, copy homework, or coast through undemanding courses. AI simply reduces the time, expense, and effort required to perform those familiar rituals. What once demanded at least some planning and deception can now be accomplished between scrolling social media and ordering dinner.

Everybody's Cheating, and Everybody's Pretending

One of the most revealing details in Sparrow's essay concerns how extensively students already use generative AI. He references research suggesting that large majorities of students in several countries use these tools, although the figures don't establish how many are cheating. That distinction matters. Asking a chatbot to explain calculus isn't the same as having it complete a calculus examination. Using technology to brainstorm ideas isn't automatically equivalent to submitting machine-generated work as personal achievement.

Still, I suspect anyone who has watched generative AI develop understands the temptation. Imagine you're a student working twenty hours a week, carrying a full course load, worrying about debt, and facing three assignments due within forty-eight hours. One option is spending the evening carefully developing an argument about a topic you may never encounter again. The other option is typing a request into a chatbot, editing the response, and submitting something that looks sufficiently academic to satisfy the rubric. I don't need to approve of the second option to understand why it appeals to someone trapped in that situation.

What fascinates me is the moral outrage that follows. Universities seem shocked that students are responding rationally to incentives universities helped create. For years, institutions have rewarded grades, deadlines, completion rates, credentials, and measurable performance. Students have internalized that system. Now a technology comes along promising to maximize those outcomes while minimizing effort, and administrators act as though an alien civilization introduced the concept of taking shortcuts.

The funniest part is that universities themselves are exploring artificial intelligence for administrative work, teaching support, and grading assistance. Apparently, automation becomes innovation when an institution uses it and academic misconduct when a student does. I understand that those activities are not necessarily equivalent. An instructor using AI to organize feedback while exercising professional judgment is different from a student submitting an essay they never wrote. But the broader contradiction is impossible to miss.

Both sides are being encouraged to do more with less time. Both face enormous productivity pressures. Both increasingly operate inside systems that measure completed tasks rather than the quality of the thinking behind them. The students simply discovered the same efficiency philosophy that administrators have been selling for years.

Congratulations, higher education. Your customers learned the lesson.

The Great Academic Artificial Intelligence Arms Race

Nothing better illustrates the absurdity of this situation than the emerging contest between AI-generated assignments and AI-detection software.

A student uses artificial intelligence to produce an essay. The university uses software to estimate whether the essay was written by artificial intelligence. The student uses another tool to rewrite the essay so it appears more human. The university considers additional technology to identify the rewritten material. Meanwhile, the original educational objective—learning how to construct a thoughtful argument—has disappeared beneath several layers of software subscriptions.

This isn't education. It's cybersecurity theater with student loans.

There's another problem: AI-detection systems can make mistakes. Human writing doesn't follow a single predictable pattern, and a confident-looking automated score isn't proof of misconduct. A student shouldn't face accusations simply because software thinks a paragraph looks suspicious. When institutions outsource judgment to detection algorithms, they risk replacing an academic integrity problem with a procedural fairness problem.

I find the entire arms race depressing because it reveals how little trust remains in the classroom. Professors wonder whether students are doing their own work. Students wonder whether professors are reading that work carefully. Administrators wonder whether the numbers will affect institutional reputation. Everyone becomes a potential suspect, and the university purchases another technology platform to manage the mistrust.

Here's a radical alternative: Design educational experiences in which professors actually know how students think.

That might involve smaller classes, sustained conversations, oral defenses, supervised writing, collaborative projects, and assignments connected to real questions instead of endlessly recycled prompts. It would require time, labor, expertise, and meaningful interaction. Naturally, those are precisely the things a cost-conscious institution may struggle to expand.

Technology promised to make education more efficient. We might discover that meaningful education is stubbornly resistant to being optimized.

The Professor Is Exhausted, Too

Before anyone assumes I'm blaming individual instructors for this disaster, I want to acknowledge that many professors are dealing with conditions they didn't create.

Faculty members often face significant pressures involving teaching loads, research expectations, administrative responsibilities, job insecurity, and student needs. Adjunct and other contingent instructors may have limited institutional support and comparatively little control over the larger educational structure. Even secure faculty positions can involve endless reporting requirements and demands to demonstrate productivity through whatever metrics administrators currently consider impressive.

It would be wonderfully convenient to believe that every student struggling to learn has a lazy professor somewhere collecting a comfortable paycheck. Reality is considerably more complicated. Many instructors care deeply about teaching and are trying to maintain intellectual standards inside institutions that increasingly reward activities other than thoughtful instruction.

I imagine the frustration of spending years mastering a discipline only to discover that students can submit polished-looking nonsense generated in seconds. Worse, the professor may lack enough time to investigate every questionable assignment thoroughly. Reading, assessing, and responding to complex student work requires attention. Multiply that attention across dozens or hundreds of students, and the workload becomes substantial.

Then some administrator arrives with a presentation about using artificial intelligence to improve faculty productivity.

I can almost hear the applause.

The irony is brutal. Universities need educators to preserve the meaning of education, yet institutional priorities can make genuine teaching more difficult. A professor might want to challenge a student's assumptions, identify weaknesses in an argument, and guide the student toward deeper understanding. The system may instead demand faster turnaround, standardized assessment, documented outcomes, and higher student satisfaction.

The professor becomes a production worker tasked with manufacturing evidence of learning. Artificial intelligence is simply the newest piece of equipment on the factory floor.

We Turned Knowledge Into a Spreadsheet

One of the strongest elements of Sparrow's argument is his criticism of the obsession with measurable academic output. Universities evaluate research through publications, citations, rankings, and other quantitative indicators. Those measurements can provide useful information, but they also create incentives to pursue what is counted rather than what matters.

I don't believe numbers are inherently evil. Measuring performance is necessary in complicated institutions, and public funding should involve accountability. The problem begins when a metric stops being evidence of achievement and becomes the achievement itself.

If researchers are rewarded primarily for producing publications, institutions shouldn't be surprised when publication counts increase without proportional improvements in intellectual value. If schools are evaluated according to graduation rates, they shouldn't be shocked when pressure develops to move students toward graduation. If student satisfaction becomes central to evaluating instruction, instructors may feel pressure to avoid experiences that make students uncomfortable, even when intellectual discomfort is essential to learning.

Artificial intelligence fits beautifully into this environment because machines are extremely good at producing measurable output. They can generate text, organize information, summarize articles, draft reports, and assist with routine processes at astonishing speed. But the appearance of productivity isn't the same as intellectual accomplishment.

A university can increase the number of essays submitted without increasing the number of ideas understood. It can accelerate feedback without improving comprehension. It can publish more material without advancing scholarship in meaningful ways.

At that point, the institution is not necessarily becoming more intelligent. It is becoming better at producing paperwork that resembles intelligence.

That distinction should terrify anyone who believes universities serve a purpose beyond their quarterly performance presentations.

Maybe Students Aren't Lazy. Maybe They're Completely Disillusioned.

It's fashionable to complain that younger generations lack discipline, intellectual curiosity, and a willingness to work hard. Every generation seems convinced that the next one is uniquely unprepared for civilization. I'm sure ancient philosophers complained that young people were ruining society by refusing to memorize sufficiently impressive speeches.

I don't buy the idea that students suddenly became incapable of serious thought when ChatGPT appeared.

I think many students are deeply aware of the contradictions surrounding them. They're told that education is essential, but also that degrees don't guarantee employment. They're encouraged to pursue passion, then warned that choosing the wrong major might produce years of financial regret. They're promised intellectual exploration while being reminded constantly that their education must translate into marketable skills.

That's a confusing message for anyone, especially someone still figuring out who they are.

When institutions present learning primarily as a financial investment, students naturally begin evaluating it according to financial returns. Every assignment becomes a cost. Every credit becomes a requirement. Every class becomes another obstacle between the student and the credential they're paying to obtain.

I think that's tragic because curiosity doesn't flourish under constant economic pressure. People learn most deeply when they can ask questions, experiment, make mistakes, and become interested in subjects without immediately calculating their usefulness.

If the central purpose of college becomes obtaining a credential efficiently, artificial intelligence starts to look less like an ethical violation and more like a productivity tool.

The saddest part is that students who rely on it too heavily may be surrendering something valuable without recognizing the loss. They're not just skipping assignments. They're missing opportunities to develop intellectual confidence, patience, judgment, and the ability to solve unfamiliar problems independently.

A machine can provide an answer. It cannot experience the personal growth that comes from struggling toward understanding.

The Diploma Isn't Supposed to Be a Participation Trophy

I don't want to romanticize academic difficulty for its own sake. There are plenty of assignments that deserve to disappear, particularly the kind that seem designed primarily to prove that a student spent several hours being miserable.

But there's a difference between eliminating pointless work and eliminating the work of thinking.

Writing a persuasive essay forces someone to evaluate evidence, organize competing ideas, anticipate objections, and decide what they actually believe. Solving a difficult mathematical problem develops habits of reasoning that extend beyond the original calculation. Reading challenging literature exposes people to experiences and perspectives they might otherwise never consider.

Those processes have value even when the final product isn't extraordinary.

I don't remember every assignment I've encountered, and I wouldn't expect anyone else to remember theirs. What matters is the mental development that comes from engaging seriously with demanding work. Education isn't merely the accumulation of correct answers. It's the gradual construction of a mind capable of evaluating answers.

That's what worries me about unrestricted AI dependence.

A student might graduate with an impressive portfolio of written assignments without developing the ability to write independently. Another might receive excellent grades while relying on automated explanations they never fully understand. Someone could become remarkably skilled at directing software without acquiring enough subject knowledge to recognize when the software is wrong.

Eventually, those weaknesses may become visible in workplaces, professional examinations, scientific research, or everyday decisions.

And when that happens, the consequences won't be limited to embarrassed graduates. Society depends on competent engineers, teachers, nurses, journalists, accountants, researchers, and countless other professionals whose decisions affect real people.

I don't particularly care whether someone used AI to draft a discussion-board response about an uninspiring textbook chapter. I care considerably more when credentials stop providing trustworthy evidence of professional preparation.

We Need to Stop Worshipping the Degree

Part of fixing higher education requires admitting that universities shouldn't be the default answer to every economic and social problem.

We've built a culture in which a bachelor's degree often functions as a basic screening mechanism for jobs that may not genuinely require four years of university study. Employers use degrees as convenient signals of persistence, communication ability, and general competence, sometimes without examining whether the credential meaningfully predicts performance in a particular role.

That creates an expensive cycle.

Employers demand credentials. Students pursue credentials. Universities expand their offerings. Families absorb the costs. Then employers raise expectations because more applicants possess the same qualifications.

The result can feel like an educational arms race in which individuals spend increasing amounts of money simply to remain competitive.

I would like to see employers become more serious about evaluating actual skills. Apprenticeships, technical education, professional certificates, community colleges, workplace training, and other learning pathways deserve far more respect than our status-conscious culture often grants them.

That doesn't mean universities are obsolete. It means they should stop being treated as the only respectable route toward a meaningful career.

I also think universities should be more transparent about what their programs cost, what students actually learn, how graduates fare, and how different fields connect to professional opportunities. Those discussions should include uncertainty rather than treating future earnings as guaranteed returns.

Students deserve informed choices, not expensive motivational speeches accompanied by glossy photographs of smiling graduates walking across perfectly maintained lawns.

AI Could Actually Improve Education—If We Stop Being Ridiculous

Despite everything I've said, I'm not interested in declaring artificial intelligence the enemy of human progress. That would be a spectacularly unconvincing position to take while discussing technology capable of helping people research, translate, calculate, create, and learn.

AI can be an extraordinary educational resource.

A student struggling with algebra can request explanations tailored to their level of understanding. Someone learning a foreign language can practice conversations without feeling embarrassed. A writer can receive suggestions about structure and clarity. A learner with limited access to tutoring can explore concepts at their own pace.

Those possibilities matter. They could make useful educational support more accessible, although access to reliable tools, devices, connectivity, and effective instruction will remain uneven.

What I reject is the assumption that technological capability automatically produces educational improvement.

An AI tutor can help a student understand a problem, or it can simply provide the answer. It can encourage someone to develop an argument, or it can generate the entire assignment. It can support a professor's preparation, or it can become a substitute for the instructor's professional judgment.

The difference isn't hidden inside the technology. It depends on how we choose to use it.

I'd rather see universities teach students how to challenge AI-generated answers than pretend those answers will disappear. Students should learn to verify claims, recognize fabricated citations, identify weak reasoning, protect sensitive information, and distinguish authentic knowledge from confident-sounding nonsense.

Those skills will matter far beyond the classroom.

At the same time, institutions should preserve opportunities for students to demonstrate what they can do without automated assistance. A person who uses a calculator still needs mathematical understanding. A person who uses a GPS should retain some ability to navigate. Someone who uses artificial intelligence to assist with writing ought to know how to construct a coherent argument independently.

Tools should extend human capability, not quietly replace the development of it.

Here's What I Would Change

If I had the authority to redesign higher education, I wouldn't begin with a new AI-detection platform or another committee tasked with producing a visionary statement about the future of learning.

I'd begin by asking what every student should be able to understand and accomplish upon completing a program. Then I'd ask whether the institution's classes, assignments, assessments, and teaching arrangements genuinely support those outcomes.

That sounds obvious, which is probably why it would require several years of meetings to implement.

I would reduce dependence on generic take-home assignments that can be generated without meaningful comprehension. I'd emphasize projects developed over time, oral explanations, practical demonstrations, research discussions, and assessments that reveal how students reach conclusions.

I'd also make responsible AI use an explicit part of the curriculum. Students should know when using AI is appropriate, when disclosure is required, and when independent work is essential. Ambiguous rules create confusion and encourage selective enforcement.

Faculty members would need sufficient time and institutional support to evaluate students fairly. Smaller classes might be necessary in some settings. Better instructor preparation would matter. Assessment design should be taken seriously rather than treated as an administrative afterthought.

And perhaps most controversially, I'd challenge the assumption that institutional growth is always desirable. A university that continually expands enrollment without maintaining the quality of teaching may improve its finances while weakening its educational mission.

Not every important improvement produces immediate revenue.

I know. Terrifying.

Universities should also reconsider how they reward professors. Good teaching deserves meaningful professional recognition alongside research, and evaluating research should involve more than counting publications and citations. Intellectual quality is harder to measure than output, but difficulty doesn't make it irrelevant.

Most importantly, I'd want institutions to recover a sense of honesty about their purpose. If a program primarily offers professional preparation, say so. If its mission is broader intellectual development, defend that mission and provide the conditions required to achieve it. Stop promising every possible benefit to every prospective student while making decisions according to financial convenience.

The Real Problem Is That We've Forgotten What Education Is For

The deeper issue isn't that artificial intelligence can write essays. It's that we've allowed the completion of essays to stand in for the purpose of writing them.

We've confused grades with knowledge, credentials with competence, institutional prestige with educational value, and productivity with progress.

The damage extends beyond universities because education shapes the way people participate in society. An educated population should be better equipped to evaluate political claims, question authority, understand evidence, navigate technological change, and recognize manipulation.

Those abilities are particularly important when artificial intelligence can manufacture convincing arguments, realistic images, synthetic audio, and enormous amounts of persuasive content.

If universities respond by teaching students merely how to produce more material faster, they'll be preparing people for the very information environment that most urgently requires independent judgment.

I want graduates who can disagree intelligently, recognize uncertainty, change their minds when evidence demands it, and explain why they believe something. I want people who understand that not every complicated question has a convenient answer.

I also want students to experience the satisfaction of discovering that they can do something difficult without outsourcing every uncomfortable moment of thought.

That experience matters because intellectual independence isn't just a professional skill. It's a form of personal freedom.

When someone develops the ability to reason, investigate, and evaluate evidence, they become less dependent on institutions and technologies telling them what to think. They become better equipped to challenge authority, including the authority of the university that issued their diploma.

Perhaps that's why genuine education is so much harder to package than credentials.

It produces people who ask inconvenient questions.

And Now for the Final Exam

I find it almost poetic that universities spent years chasing efficiency, standardization, measurable outcomes, and institutional growth, only to encounter technology that can imitate the products of that system faster and cheaper than the people producing them.

The institutions wanted productivity. They got productivity.

They wanted assignments submitted on time. The machines can handle that.

They wanted polished reports, structured essays, standardized feedback, and endless documentation. Artificial intelligence is delighted to help.

What it cannot guarantee is that anyone has actually become wiser.

That's the uncomfortable truth underneath all the anxiety about AI in higher education. We didn't suddenly discover that learning was vulnerable to shortcuts. We've always known that. What changed is that the shortcuts became extraordinarily powerful, widely accessible, and difficult to distinguish from legitimate academic work.

Artificial intelligence isn't innocent. It introduces serious challenges involving academic integrity, misinformation, intellectual dependence, and the reliability of qualifications. Pretending those concerns are exaggerated would be foolish.

But pretending the problems began with AI would be equally foolish.

We built universities increasingly dependent on revenue, prestige, rankings, and measurable output. We encouraged students to treat education as a financial transaction. We burdened instructors with competing demands. We allowed credentials to become increasingly disconnected from the experiences they were supposed to represent.

Then a chatbot appeared and demonstrated just how much of the process could be automated.

I don't think the appropriate response is to abandon higher education. I think it's to demand something better from it.

Let students use technology where it genuinely deepens understanding. Require them to demonstrate independent competence where that matters. Give professors the resources to teach. Stop treating students as walking tuition payments. Make universities accountable for the quality of education rather than merely the quantity of credentials.

And above all, remember that the purpose of education isn't to produce a document that looks intelligent.

It's to develop a human being who can think.

If that sounds hopelessly old-fashioned in the age of artificial intelligence, perhaps the problem isn't with the idea.

Perhaps the problem is that we've become so impressed by machines producing answers that we've forgotten why humans were supposed to ask questions in the first place.

My final verdict: AI didn't destroy the university. It walked into an institution already suffering from a serious identity crisis, completed the homework, polished the mission statement, and exposed the difference between an expensive education and an expensive transaction.

The university can either confront that reality or commission another strategic review explaining why everything is going wonderfully.

I have a feeling I know which option will look better on the spreadsheet.

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Source and further reading: Jeff Sparrow, "Friday essay: AI is devastating higher education, but the system was already broken," The Conversation, October 22, 2026.