I would say that an LLM could count as a "teacher" of sorts, so it would be more than a 0:1 ratio. But, if we're comparing LLM-teacher to human-teacher, I don'

I would say that an LLM could count as a "teacher" of sorts, so it would be more than a 0:1 ratio. But, if we're comparing LLM-teacher to human-teacher, I don't think it reaches full 1:1. I dunno, I'd say maybe 0.5:1 or 0.25:1 would be more accurate, given that the thing doesn't even produce correct information a certain percentage of the time? Based on my experience with ChatGPT, it does really well as a "rubber duck." [0] Sometimes, it even gives back useful suggestions. Sometimes, it's just so far off base, I wonder what planet it's getting its advice from. If I were the professor in this case, I think I'd suggest that students use it as a supplement: try LLM-TA first, look into its suggestions, and if it helps, great! If not, then try a few more things, maybe consulting LLM-TA a few more times. If that doesn't get you to working code and an understanding of the problem, then try human-TA. I think this represents something close to the optimal workflow for this tool, given the known limitations of the underlying technology. --- [0]: https://en.wikipedia.org/wiki/Rubber_duck_debugging https://en.wikipedia.org/wiki/Rubber_duck_debugging