You are also flagging a really basic architectural assumption: most institutional thinking quietly presumes students will interact with commercial, frontier models rather than institutionally governed or locally tuned systems. That assumption shapes everything—from privacy expectations to what kinds of “question-first” behavior we think are even possible, because frontier chatbots are optimized to answer, not to pause and redirect. If you imagine pushing back on that, what kinds of smaller, slower, maybe library-embedded AI spaces do you want to reserve specifically for helping students generate questions and locate human- and library-based resources before they ever touch the big commercial tools?

You have already named a key shift in that sentence about students starting with AI explanations: it positions AI as a replacement for discovery rather than a partner in discovery. If the norm became “AI helps me figure out what to ask and where to look,” not “AI tells me what is true,” how would that change the way you talk to colleagues about AI literacy or research readiness? What sort of guardrails or affordances would an institutional, question-centered AI need (or need to avoid) so that it reliably points students toward databases, librarians, and faculty instead of pulling them deeper into self-contained explanation worlds?