Detection is the wrong model.

Detection treats every student like a suspect. It scans for what looks like cheating after the work is done — and in the AI era, it’s a losing game. The “humanizers” already beat it, while honest students get false-flagged over a missing quote.

The model is backwards. You can’t police your way to academic integrity. You build it — by teaching the process, making the work transparent, and giving students tools that show their thinking instead of hiding it.

That’s the bet behind Wonders: every claim traces back to a real source, the process stays visible to supervisors, and students come out sharper — not just uncaught. Integrity by design, not by detection.

We go deeper on this in the AI Safety Center.