University AI Policies After 3 Years – Are They Actually Working?

From an academic integrity coordinator perspective: we now have enough data to evaluate the first generation of Canadian university AI policies. The verdict is mixed.

Policies that worked: transparency-based frameworks that required disclosure and demonstrated understanding. These correlate with higher student satisfaction and lower formal integrity complaints.

Policies that didn’t work: blanket bans without enforcement mechanisms. These correlate with higher informal (undisclosed) AI use, lower disclosure rates, and no reduction in actual AI use.

Detection-based enforcement: the data is consistently poor. institutions that relied on detection as their primary enforcement mechanism have higher rates of contested integrity findings, more formal appeals, and more administrative burden than transparency-based institutions.

The surprise finding: policies co-developed with students perform significantly better across all metrics. This wasn’t obvious three years ago but it’s the clearest signal in our cross-institutional data now.