That's very interesting. Maybe you are doing this the right way, and my concern as a math educator is for the people who may struggle to stay on the straight and narrow, or know what the straight and narrow is in this brave new world.

Where I see deficiencies is not so much in the calculations. When a problem class has a solution algorithm and 10,000 worked examples online, I'm not too surprised that the LLM generalizes pretty reliably to that problem class.

The problem I find is more when it's tricky, out-of-distribution, not entirely on the "happy path" of what the 10,000 examples are about. In that case, LLM responses quickly become irrelevant, illogical, and Pavlovian. It's the math version of messing up the surgeon riddle when presented with a minor variation that is logically very easy, but isn't the popular version everyone talks about [1].

[1] https://www.thealgorithmicbridge.com/p/openai-researchers-ha...

It's nice that you think it's clear and responsive, but I think it [1] needs to be validated by an expert in both the material and education. Or we need some way to show that people have actually learned the topic. People sometimes prefer explanations that are intuitive and familiar but not accurate.

Meanwhile, there are math education resources like iXL that maybe cost a little money but the lessons and practice problems are fully curated by human experts (AFAICT). I'm not saying these resources are perfect either, but as a mathematician who has experimented a lot with LLMs, including in supposed tutoring modes, they make a lot of mistakes and take a lot of shortcuts that should materially decrease their effectiveness as tutors.

[1] LLM-based tutoring (edit: footnote added to clarify)