Agree with this and the framing here is considerably better than what we see elsewhere—particularly, as you note, with the intersection between K-12 and AI. (I also agree that college is the "marginal space" where more exploration and application-centered approaches seem more useful.)
However, I quick follow-up to this point: "But the output of an amplifier is only as good as the input that goes into it."
My wondering right now is if extended use of the amplifier (AI) decreases the quality of the input (human skill) itself?
I think this is a very careful and polite way of saying what I think is (finally) starting to cut through the hype cycle(s)- that the actual, ethical, successful, useful set of use cases for actually-existing-LLMs-as-AI is damn small once everyone has worn out the novelty. It's looming large in education because the work that students produce and that LLMs have proven successful in producing *is not actually a product for discerning consumers.* Student papers are expected to have mistakes! The references attached to them (that LLMs fabricate) are not necessarily there to be checked by an overwhelmed TA looking to follow their noses through the maze of scholarship, but to *practice producing references for a time when that's important.* Meanwhile, it makes coders who feel like they are going faster go slower, and all the copywriters I know have more work than ever (that they make without LLMs after the inevitable flirtations).
So while I think you're spot-on that there's not a huge place for these tools in educational environments where they screw up the traditional assessment tools and supplant skill development, I think the notion that they get to graduate to using them in all their power might be overselling the endgame.
Agree with this and the framing here is considerably better than what we see elsewhere—particularly, as you note, with the intersection between K-12 and AI. (I also agree that college is the "marginal space" where more exploration and application-centered approaches seem more useful.)
However, I quick follow-up to this point: "But the output of an amplifier is only as good as the input that goes into it."
My wondering right now is if extended use of the amplifier (AI) decreases the quality of the input (human skill) itself?
Of course all research is inevitably new and ongoing, but cases like the doctors who became less skilled after relying on AI seems to present a conundrum, particularly when thinking about it with education: https://www.npr.org/sections/shots-health-news/2025/08/19/nx-s1-5506292/doctors-ai-artificial-intelligence-dependent-colonoscopy
Short-term amplification but long-term erosion feels like a bad equation, right?
I think this is a very careful and polite way of saying what I think is (finally) starting to cut through the hype cycle(s)- that the actual, ethical, successful, useful set of use cases for actually-existing-LLMs-as-AI is damn small once everyone has worn out the novelty. It's looming large in education because the work that students produce and that LLMs have proven successful in producing *is not actually a product for discerning consumers.* Student papers are expected to have mistakes! The references attached to them (that LLMs fabricate) are not necessarily there to be checked by an overwhelmed TA looking to follow their noses through the maze of scholarship, but to *practice producing references for a time when that's important.* Meanwhile, it makes coders who feel like they are going faster go slower, and all the copywriters I know have more work than ever (that they make without LLMs after the inevitable flirtations).
So while I think you're spot-on that there's not a huge place for these tools in educational environments where they screw up the traditional assessment tools and supplant skill development, I think the notion that they get to graduate to using them in all their power might be overselling the endgame.