University AI Detection Policy Checklist for 2026

Policy before enforcement

Universities should define AI-use expectations before disputes happen. A policy should explain allowed assistance, disclosure language, detection triggers, review steps, appeal options, and recordkeeping.

Pair this checklist with academic integrity workflows, AI disclosure guidance, and methodology.

Checklist

Define acceptable AI use. Explain when disclosure is required. Tell students what evidence may be reviewed. Train faculty on false positives. Give students a way to explain their writing process. Keep consistent records across departments.

FAQ

Should policy mention detectors by name?

Policies can mention approved tools, but they should also define how results are interpreted and reviewed.

What is the biggest risk?

The biggest risk is treating detection as punishment instead of a structured review process.

Implementation checklist

Publish the policy in student-facing language. Add examples of acceptable and unacceptable AI assistance. Train reviewers on false positives. Define who can see detection results. Create an appeal path. Review the policy every term as writing tools and institutional expectations change.

The best policy is usable during a real grading dispute, not only readable in a handbook.