AI is already in your classrooms. Your faculty were never prepared for it.

Artificial intelligence entered education through the students' door. They use it to draft, summarise, solve and prepare — fluently, constantly, and mostly without guidance. Meanwhile most faculty were never given a position on it: no shared understanding of what is acceptable, no practice in detecting machine-generated work, no vocabulary for discussing it honestly with a class. The technology arrived; the institution's judgement about it did not.

The typical response makes things worse in both directions. Blanket bans push use underground, where it continues without any learning. Uncritical encouragement turns in confident-sounding work that students cannot defend. Faculty, caught between the two, either pretend the problem does not exist or spend their energy policing instead of teaching. Both postures surrender the educational opportunity.

Tools change; judgement decides

The question institutions keep asking — which tools should we allow — is the wrong first question. Tools will change every semester. The durable capabilities are judgement-first: can a faculty member design an assignment worth doing in an AI-saturated world, evaluate work they suspect is machine-assisted fairly, and teach students when to trust an answer and when to challenge it?

Those are human capabilities applied to a technological situation: critical thinking, academic honesty as a lived practice, communication about expectations. Faculty do not primarily need tool demonstrations. They need a clear institutional position, practice in the hard conversations, and assessment designs that make genuine thinking visible.

The institutions that thrive will not be the ones with the best AI tools. They will be the ones whose people exercise the best judgement about them.

Start with one honest semester

Pick a small set of courses and redesign their assessments so that unaided thinking is observable: in-class reasoning, defended work, process documentation alongside final output. Support the faculty teaching them with peers, not just policies — a group working through real student cases together builds more readiness than any circular.

Then write down what the institution actually believes, in plain language students can read: where AI helps learning, where it bypasses it, and what happens in each case. A position nobody can find is not a position. Clarity, practised consistently for one semester, does more than any detection tool.