AI Cheating Isn’t the Problem

What’s left of students’ motivation to learn when effort is no longer rewarded?

AI has made cheating in school faster, easier, and more polished — but the real crisis isn’t the cheating itself. It’s that students are losing their motivation and incentives to learn.

AI Makes Cheating Easy — But the Deeper Problem Is Motivation

AI is making it easier than ever for students to cheat. But here’s the truth: the real crisis isn’t that students are cheating. Cheating has always existed - as this 2010 article from The Washington Diplomat shows. The real problem is what students lose when they cheat — especially now that AI makes it so effortless, polished, and effective.

Let’s be honest: homework isn’t always fun, learning isn’t always exciting. Most students don’t power through a five-paragraph essay or their math homework out of sheer love for sentence structure or the Pythagorean theorem. Often, they find other reasons to complete their assignments: get a better grade, please their parents, compete with classmates. In short: they rely on extrinsic motivation.

But when AI can produce a better essay — in structure, style, and argument — in seconds, the reward can be claimed with almost no effort.

The student asking “Why should I bother?” isn’t being lazy. They’re being rational.

When the Effort-Reward Contract Breaks

This is the core issue. AI has broken the deal that school traditionally relies on: work hard, get rewarded. When students can skip the hard part and still get the reward, we’ve hollowed out the system.

And here’s the kicker: the point of writing an essay in school was never the essay itself. No one’s publishing it. No one cares about your freshman-year take on The Great Gatsby. What mattered is the process — the mental exercise, the creative struggle, the effort to organize thoughts, build arguments, and develop your voice. That’s where the learning happens.

Unfortunately, AI short-circuits that process by removing intellectual friction.

Even students who are intrinsically motivated to learn suffer. They realize that effort is optional, that results matter more than growth. Over time, even the most disciplined students will gravitate toward the easier path.

Solving the AI Problem Doesn’t Mean Banning It

Let’s be clear: I’m not against using AI in school. In fact, it’s essential that students learn how to work with these tools — at school now, and in the workplace later. As many have pointed out, most jobs won’t be replaced by AI, but they will be filled by people who know how to use it well. AI literacy is no longer optional — it’s foundational. (I explore this further in my piece on Gen Z and tech literacy.)

Recently, I spoke with a friend who’s a software engineer at a large company. When I asked if he used generative AI on the job, he said yes — in fact, he uses ChatGPT to code entire features in JavaScript, a language he’s never formally learned. On his own, he wouldn’t be able to write the code from scratch. But because he understands programming concepts and has experience in other languages, he can read and refine the AI’s output with the right prompts.

Here’s the key insight: he can do this only because he already put in the work. As Rebecca Winthrop points out in her conversation with Ezra Klein, older generations went through school without AI. Our brains were shaped by achieving demanding tasks ourselves — the kind that builds intellectual flexibility and resilience. We can now judge AI output and use it with discernment.

But what happens if you never built that foundation? What if you skipped that whole part?

Refocusing on What Matters: Meaningful Learning

The deeper issue isn’t the AI. It’s the incentive structure.

Our system rewards grades, not growth. That flaw has always existed — but AI has exposed just how fragile it really is.

We now need to rethink what school rewards, and why. If we can’t offer students fair, meaningful incentives for effort and learning, AI will continue to replace that effort. And learning will continue to erode.

We have to rebuild intrinsic motivation — the internal drive to learn, struggle, and create, not because of a grade, but because it means something.

Easier said than done, of course. But here are a few places to start:

• Design more personal assessments
  Oral defenses, real-world applications, and essays grounded in personal experience make cheating harder and learning more meaningful.

• Use AI as a collaborator, not a replacement
  Let students use AI for brainstorming, feedback, and structure — but require them to reflect on what the AI is doing and why.

• Reward process, not just output
  Ask students to submit drafts, revisions, and notes. Grade their thinking, not just the final result.

• Connect learning to real life
  Help students see how what they’re learning applies to their goals, interests, or communities.

• Shift the culture from performance to growth
  Celebrate effort, curiosity, and improvement — not just grades or polish.

Because here’s the paradox: even students who cheat and get good grades aren’t satisfied. They know, deep down, that they didn’t earn it. There’s no pride, no ownership, no growth.

Until we fix that — until we give students something real to strive for and a reason to believe that effort still matters — AI will keep dragging them away from the very experiences that make learning meaningful.