| There is a bit of oversimplification here. Understanding if the student has actually learned is a competency piece, in math it’s mostly show your work and/or did you have the right answer. The continued top down attempts to boil the whole sea with LLMs is part of the current problem. It’s getting pretty good though for focused tutoring. For students, models setup to tutor too often are trying to boil a sea (all education) instead of a kiddie pool. The reality is that more and more seems like k-6 if not k-12 students can be supported. If we look at the EdTech space from the bottom up, namely learner-centric, there is both a real need and opportunity. For school age students, math largely has not changed in hundreds of years, and doesn’t change often. Either you understand it or have to put in the work. There’s no shortage of human created written teaching resources. A teacher could create their own touring assistant based off their explanations. Alternatively, an open source textbook could be inputted. There’s a reason why training or fine tuning on books has caused lawsuits - it can increase accuracy many fold. Teachers are burdened with repetitive marking, there’s def a place for personalized marking tools. We know LLMs respond differently to different input. Their superpower is being able to regenerate an input as many different many different ways, which can include personalization. Just because one has experimented with LLMs doesn’t mean there isn’t a way to get a result from them just because we haven’t been able to understand how. If examples of the chat logs or prompts can be provided of what did or didn’t work it helps have a conversation without the subjectivity. Mathematics is a great lens to see that folks are trying to get non-deterministic software to behave like all the deterministic software we’ve had before, instead of finding the places where non-deterministic strengths can shine. It’s not all or nothing, or one or the other. |