Everyone Is Cheating and No One Agrees What That Means

An educator quoted in a piece circulating through teacher communities this week asked, with apparent sincerity: *"If AI is writing the work and AI is reading the work, do we even need to be there at all?"* That's not a rhetorical flourish. It's where the education conversation has actually arrived — past the detection tools and honor-code debates, into something much harder to legislate away.

The coverage split is almost surgical in how cleanly it divides its audiences. The Bulwark's piece arguing colleges should just let students cheat with ChatGPT found its audience among readers who'd already decided the enforcement apparatus was the real problem — people who consume AI as productivity news and read proctoring software the way they'd read a buggy-whip manufacturer arguing for mandatory horse lanes. Fortune's warning that students can no longer reason, and Slate's quieter claim that something more important than skills is being lost, landed somewhere else entirely: in communities where teachers are watching students struggle to construct an argument without a machine doing the scaffolding, and where the stakes feel less like a policy question than a professional grief. South Korea's mass cheating scandal, reported in Times Higher Education, slid into this conversation like a grenade, because it reframes the whole thing — not as a moral failure by individual students but as a structural failure of assessment design at institutional scale. If the test was always gameable, who exactly cheated?

What's gone missing in both conversations is the student perspective, which keeps getting discussed rather than heard. The New York Times came down in favor of surveillance tools that students, in nearly every forum where they're speaking for themselves, describe with contempt — not because they want to cheat but because they experience the tools as presumptively accusatory. The New York Magazine piece declaring that everyone is cheating treats this as sociology when students are treating it as a survival strategy inside a credential system whose pricing hasn't moved despite the fact that a language model can now pass most of its exams.

The education conversation matters beyond its own borders because it's the place where the abstraction of AI discourse meets the specific. Parents, teachers, and students aren't arguing about model weights or compute costs; they're asking what human effort is worth when a machine can produce something indistinguishable from it on demand. That question lives underneath every other AI argument happening right now — the labor debates, the copyright fights, the open-source control anxieties. Higher education is just the arena where it's least possible to defer the answer, because the semester ends, the grade posts, and someone has to decide what it was all for.