Transparency Over Policing

We keep asking: “How do we stop students from using AI?” But I’m not sure that’s the right question anymore. Because the reality is: You cannot out-police AI use.

The tools are evolving too quickly. Detection systems are inconsistent. And even the companies behind many AI detectors often acknowledge limitations, false positives, and uncertainty in their own documentation.

That creates a dangerous situation in education: We’re trying to enforce rules around tools that are increasingly difficult to reliably identify. And when policies are unclear, inconsistent, or built around detection alone, students don’t learn accountability.

• They learn confusion.

• What counts as acceptable help?

• Brainstorming?

• Outlining?

• Grammar support?

• Explaining concepts?

• Rewriting?

• Studying?

In many courses, students genuinely don’t know where the line is. So instead of creating clarity, we create anxiety. And sometimes we create adversarial relationships around learning itself.

I keep coming back to this shift:

• From: “How do we catch AI use?”

• To: “How do we create transparency around AI use?”

Because transparency changes the conversation. When expectations are clear:

• Students can make informed decisions

• Faculty can evaluate work more fairly

• AI use becomes discussable instead of hidden

• The focus shifts from surveillance → learning

And importantly, transparency applies to instructors too. If we expect students to disclose meaningful AI use, we should also be willing to model what responsible and transparent use looks like ourselves. Not because AI use is inherently wrong. But because transparency helps preserve trust.

I don’t think the future of AI in education is built on perfect enforcement. I think it’s built on clearer expectations, better assignment design, and honest conversations about what learning actually looks like in an AI-rich environment.

Because the goal was never simply to stop tool use. The goal is still learning.