Has AI already broken higher education?

Occasionally I amuse myself, and perhaps others, by posting homilies which are generated by the free versions of the Artificial Intelligence (AI) services ChatGPT and CoPilot.  The group consensus here seems to be that the AI-generated homilies, at least when considered as reading material, are better, tighter and briefer than the offerings of our stressed-out, tired and distracted local clergy.     

Over at the Free Press, Tyler Cowan, a professor of economics at George Mason University, observes that when it comes to higher education, the AI revolution isn't the future, it's the present:

There’s an epidemic of cheating in American education right now. The cheaters are the students of course, but the names of the cheating aids might be familiar to you: ChatGPT, Claude, Gemini, Grok, and Llama.

These characters are capable of doing extraordinary things: writing your persuasive essay in under a minute; knowing virtually all of history; and performing first-rate synthetic analyses of complicated questions. They are not yet geared up to do mathematics, but the best of these programs already can pass medical and bar exams. Oh, and they can do it for millions of students all at once, even sometimes all the way up to graduate-level work.

I’m talking, of course, about large language models (LLMs).

Accurate data is hard to come by, but one estimate suggests that up to 90 percent of college students have used ChatGPT to do their homework. Rather than debating the number, professors and teachers simply ought to assume (and I do) that your students have an invisible, very high-quality helper. As current norms weaken further, more students learn about AI, and the competitive pressures get tougher, I expect the practice to spread to virtually everyone.

This state of affairs has set off a crisis among educators, parents, and students. There has been a flurry of recent stories capturing how the cheating is done, how hard it is to catch, and how it is wrecking a lot of our educational standards.

[...]

The first problem the LLMs expose is that our evaluation systems are broken, inefficient at sorting, and also unfair. If one student gets an A and the other a B, do we know that reflects anything other than a differential willingness to use LLMs? We never will, yet decisions for fellowships, graduate school admissions, and jobs all will be made on this basis. It stinks.

This isn’t just a modest problem. It is an out-of-control one and it will only get worse.

[...]

And the list of problems does not stop with the students. It includes professors. Including me.

Lately I have been using the o3 model from OpenAI to give my PhD students comments on their papers and dissertations. I am sufficiently modest to notice that it gives keener, smarter, and more thorough suggestions than I do. One student submitted a dissertation on the economics of pyramid-, tomb-, and monument-building in ancient Egypt, a topic about which I know virtually zero. The o3 model had plenty of suggestions. How about: “Table 6.5’s interaction term ‘% north × no-export’ is significant in model 3 but not 4. Explain why adding period FE erodes significance; maybe too few clusters? Provide wild-bootstrap p-values.” Of course I would have noticed that point as well.

Maybe they are not all on-target—how would I know?!—but the student, who has studied the topic extensively, can judge that for himself. In any case the feedback was almost certainly much better than anything I might come up with.

Suddenly we are realizing that the skills we trained our faculty for are also, to some degree, obsolete. Would it be so crazy to put o3, or some other advanced AI model, on the dissertation committee, in lieu of the traditional “outside reader”?

In this scenario, it seems entirely possible that a professor used an LLM to provide feedback on a student's dissertation which also may have been largely composed by an LLM.

Cowan's advice, which I don't think is entirely tongue-in-cheek, is not to fight these developments, but rather raise the white flag of surrender.  There is no practical way for faculty members to detect nor prevent students from cheating on papers and similar assignments; and Cowan questions the value of having students go through the labor of writing a paper the old-fashioned way,, because as soon as they step into the real world, they'll all be using LLMs for the writing, analysis and other tasks that used to require smart people but which now can be done by artificial engines which already are smarter than the smartest of us.  

As an example of what goes on nowadays in the world of post-college (or instead-of-college) gainful employment, he reports this:

Not long ago I had lunch with a friend of mine in Mexico. He is in his early 20s, and did not go to college. He picked up five different jobs as a software engineer, using the o3 model from OpenAI to do most of the work. He shows up for meetings for the different jobs, when that is required. He is earning a very comfortable living, and still has plenty of time to read and explore the world. Am I supposed to believe he would have done better by sticking it out and getting an MBA? Instead, you could say he has a self-taught PhD in multitasking, programming, and of course using AI models.

That preceding paragraph is a little ambiguous, but I take the passage to mean that his friend has been hired for five different jobs *simultaneously*, which he juggles by keeping a calendar of meetings and showing up when/where he is supposed to.

Back to higher education: here is his outlook for the (very) short-term future:

Maybe the system will ultimately blow up. Or perhaps it will just slouch on this way, becoming increasingly unfair and pathetic. Employers will learn to disregard grades. Graduate schools will rely all the more on letters of recommendation. Personal networking will continue to rise in importance.

Higher education will persist as a dating service, a way of leaving the house, and a chance to party and go see some football games. It also will become all the more important as a path to building out a personal network of peers.

The ostensible mission of college—learning—will become ever more optional. Many students will seize the opportunity to study with their AI models, liberated from the onerous demands of having to write all those “A quality” papers themselves . A few “rebels” will do their classwork on their own, but everyone else will wonder what exactly they are planning on doing with the writing skills they develop.

Enrollments will shrink, and conditions for faculty will deteriorate. 

Loyola didn't have a football team, but "dating service, leaving the house, and a chance to party" is as good a description of my undergraduate career as any.  Admittedly, I did find my way to class more often than not, learned a few useful skills, and many other things which aren't particularly translatable to earnings but which I wouldn't trade for anything.  So while I fear he's right, I hope he's wrong.