“Was it always the case that half of our students would cheat if it were easy enough?”

This article's central claim is that generative AI has created not just a cheating problem, but an existential crisis for university teaching. Jay Caspian Kang argues that many professors feel they're losing faith in the educational process itself because AI allows students to bypass the intellectual work that education is meant to cultivate. 

Here are the main arguments:

The crisis is about learning, not just plagiarism

The professors Kang interviews are less concerned with catching cheaters than with a deeper question: if students can outsource reading, writing, and thinking to AI, what exactly are they learning? The traditional model of higher education assumes that struggling through difficult material develops judgment and intellectual maturity. AI threatens to short-circuit that process.

Writing is valuable because it is thinking

The article repeatedly returns to the idea that essays are not simply a way to demonstrate knowledge—they are how students clarify, test, and develop their own ideas. If an AI produces the prose, students may receive a polished product without having gone through the cognitive work that gives writing its educational value.

Professors are grieving the loss of authentic engagement

Many instructors describe an emotional response that goes beyond frustration. They feel that conversations with students have become less genuine because they cannot be sure whether submitted work reflects the student's own thinking. Several describe a sense of mourning for the kind of mentorship and intellectual discovery that originally drew them into academia. 

Current solutions don't scale well

Professors have experimented with: oral examinations, handwritten assignments, in-class writing, highly personalized projects, redesigned assessments.

But these approaches are often feasible only in small seminars, not in large lecture courses with hundreds of students. The article argues that AI has exposed structural constraints in higher education, especially large class sizes and heavy teaching loads. 

AI exposes existing weaknesses in higher education：Kang suggests AI didn't create every problem. Universities had already become increasingly: credential-focused,  transactional, driven by efficiency, dependent on standardized assessments.

AI amplifies these trends by making it easier for students who primarily want the credential to complete assignments with minimal intellectual engagement.

Not all professors reject AI outright：The article is more nuanced than a blanket anti-AI argument. Some faculty members: use AI productively in their own work, believe students will need AI professionally, are experimenting with incorporating it into teaching.

Even these professors, however, worry about students becoming dependent on AI before they've developed their own analytical abilities. 

The deeper question is the purpose of university education：The article ends by asking whether higher education can still justify traditional assignments—and perhaps even its broader mission—if machines can produce competent essays instantly. Rather than predicting the end of universities, Kang portrays professors as searching for a new conception of education in which human intellectual development remains central.

The article's underlying thesis

The essay is ultimately less about AI technology than about what education is for. It assumes that the point of college is not merely to produce correct answers or polished writing, but to cultivate habits of thought through effort, uncertainty, and revision. AI challenges that assumption by making the product of thinking available without necessarily requiring the process of thinking. That tension, the article argues, explains why so many professors describe the current moment not simply as a technological disruption but as a crisis of purpose. 

（26.5.2026 The New Yourker， The Despair of the Professor in the Age of A.I.By Jay Caspian Kang Chinese Translation）