"Robbins writes that universities "must decide which parts of known-known transfer need classroom time and which parts can be handled by cheaper, more flexible LLM systems." The framing treats AI-delivered instruction as a settled alternative whose effectiveness is established enough to anchor a resource reallocation strategy. That assumption is everywhere right now: in policy documents, in board presentations, in the pitches that edtech companies make to provosts."
"It is a genuinely useful framework that expands on her proposals that I have been reading about for some time. My own views on neuroscience and the cognitive architecture of meaning-making, and Robbins' structural lens maps onto that work in ways I find productive. She is asking where institutions should invest. I keep asking what human minds do that machines cannot. The two questions need each other."
Universities are reorganizing around claims that AI instruction is effective despite insufficient supporting evidence. A comprehensive re-analysis of the AI-and-learning literature reveals extreme publication bias, inflating reported effects. After applying bias corrections, measured learning gains attributed to AI shrink substantially and may be indistinguishable from zero. Proposals to reallocate resources toward AI-driven delivery treat AI as an established, effective alternative, appearing throughout policy documents, board presentations, and vendor pitches. The assumption that AI can replace classroom time for 'known-known' content underpins many restructuring plans but rests on weak empirical foundations.
Read at Psychology Today
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