Refrain, then Amplify: A Curriculum Framework for Sequencing Generative AI to Form Professional Judgement
Universities are having three separate conversations about the same question: one about student devices, another about generative artificial intelligence (GenAI) in assessed work, and a third, unspoken, that leaves instructors to decide both at their own professional risk. The question underneath: does the technology serve the formation of a capacity, or do the work in the student’s place? This paper holds the three as one, at the level where curricular design happens: the programme. On a refrain-then-amplify design, a programme withholds a generative tool while a capacity is forming, then restores it to amplify that capacity once the student can direct it, judge what it returns, and answer for it. Devices are allowed where they support engaged work, excluded where they drain attention. Both sit in a single floor beneath every course, the first governed by a forming-versus-offloading criterion: whether a stretch of work forms a capacity or puts it through the tool. The programme fixes outcomes and integrity, reserving teaching method to the instructor, and places a hard-to-fake checkpoint at each refrain-to-amplify hinge. Each element has published precedent: the withholding, the taught restoration, the test. None of that work owns the movement at programme level, with a verified transition.