Should our university run AI detection at all, or write a policy that works without it?

Secondary school, so different rules, but here is what happened when our district switched the indicator on. Referrals went up. Findings did not. What went up was the number of meetings in which I explained to a parent, with a shaken sixteen-year-old in the room, that the number on the screen was not evidence of anything. Twice I was explaining it to a colleague.

If I had a vote on your committee I would take option (b). But I teach fifteen-year-olds, and the people who got hurt were the careful kids, the ones who revise. Your undergraduates may be different. The dynamic I would bet is not.

TA perspective, and I have lived under something close to your option (c). Our department keeps the indicator on, but the written policy says a score may not be cited in an allegation and may not be mentioned to the student as a reason. It is a prompt for the instructor to look more closely: ask for drafts, look at version history, have a conversation about the argument.

Under that rule it has mostly worked. What has not worked is onboarding. Every term a new instructor sees a high percentage, has not read the policy, and writes the student up directly. So if you choose (c), the policy text matters less than how you make sure every person who can see the score has read the paragraph that constrains it. I tried a one-page summary attached to the course shell. It helped, a bit.

A policy can work without a detector, and several universities have chosen that route. If you keep one, vendor guidance and the research point the same way: a score cannot be the sole evidence, a human reviews it, and the student has a right to reply. Decide the process first, then whether a score has any place in it.

The clearest public example of option (b) is Vanderbilt's August 2023 notice, "Guidance on AI Detection and Why We're Disabling Turnitin's AI Detector". Their reasons: the feature arrived with less than 24 hours' notice and no way to switch it off, the vendor gave no detailed account of how it reached a determination, false accusations elsewhere were being reported, and the non-native English research had come out. They also raised a point your committee should not skip: sending student work to a third-party detector is a privacy question in its own right. Their alternative is your option (b): set expectations early, require disclosure and citation where AI use is allowed, redesign assessments toward in-class writing and course-specific topics, compare suspect work to the student's other writing, check for fabricated sources, and talk to the student.

OpenAI's educator FAQ, "How can educators respond to students presenting AI-generated content as their own?", is worth putting in front of anyone arguing for (a). Asked whether AI detectors work, it answers "not in our experience," reports that their own classifier labelled Shakespeare and the Declaration of Independence as AI-generated, notes indications of disproportionate impact on students learning English and on formulaic writers, and observes that small edits defeat detection anyway. Its alternatives are process-based: shared conversation logs and cited AI use.

If the committee lands on (c), write the restriction into the evidential standard rather than a usage note, since usage notes are what Priya's new instructors do not read. See the thread on which detectors colleges use and the false flag thread for how it looks from the student's chair.