Evaluate AI feedback tools within academic policy

An AI feedback discussion should begin with the learning activity, the human reviewer, and the institution's rules. An account executive cannot settle academic integrity policy or promise every response is accurate. A controlled evaluation lets faculty determine where the tool belongs and where it does not.

Director Salma says, "Will this make students cheat, and can you guarantee the feedback is right?" Account executive Rowan answers, "Your academic integrity policy defines permitted student use, and feedback should have appropriate human review. Which assignments are you considering, and which programs already prohibit outside tools?" Salma notes that clinical simulations have restrictions and the policy is still being drafted. Rowan says, "We can include those boundaries in an evaluation plan. A session with faculty, integrity staff, and privacy can identify a small set of assignments, review sample output, and decide what guidance students receive."

Rowan gives the institution control over academic decisions. He treats output review as work for the people who teach and assess learning. The proposed evaluation makes the unanswered policy question visible rather than hiding it in a product claim.

Select one assignment and write its permitted use in plain language. Roleplay a faculty concern about an inaccurate response. End by naming the reviewer who would inspect that response and the policy owner who decides the boundary.