On Mon, Feb 9, 2026 at 5:02 PM Ralf Gommers via NumPy-Discussion < [email protected]> wrote:
> > This also presumes that you, or we, are able to determine what usage of AI > tools helps or hinders learning. That is not possible at the level of > individuals: people can learn in very different ways, plus it will strongly > depend on how the tools are used. And even in the aggregate it's not > practically possible: most of the studies that have been referenced in this > and linked thread (a) are one-offs, and often inconsistent with each other, > and (b) already outdated, given how fast the field is developing. > On this point, I commend to everyone the writing and research of Dr Cat Hicks, a psychological scientist studying software teams and tech. One of the things I've noticed from her reading these papers in public is that the studies are typically (a) not designed by learning scientists, (b) uninformed by the basic phenomena of learning science (thus misattributing effects as novel or using mismatched instruments), and (c) underpowered. This is an emerging object of study. Each paper alone isn't going to establish anything about "AI"; they are adding to a body of knowledge that might some day, but after a lot of missteps while we work out the right way to measure these effects. Each one is interesting, but rarely is the headline-ification of the results going to hold water. https://www.fightforthehuman.com/cognitive-helmets-for-the-ai-bicycle-part-1/ -- Robert Kern
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