No, I don't support this. A statement I would support is: "Don't use LLMs, for anything where correctness or accuracy matters, period, and make sure you carefu

No, I don't support this. A statement I would support is: "Don't use LLMs, for anything where correctness or accuracy matters, period, and make sure you carefully check every statement they make against some more reliable source before relying on it. If you use LLMs for any purpose, make sure you have a good understanding of their limitations, some relevant domain experience, and are willing to accept that the output may be wrong in a wide variety of ways from subtle to total." There are many uses where accuracy may not matter: loose machine translation to get a basic sense of what topic some text is about; good-enough OCR or text to speech to make a keyword index for searching; generation of acceptably buggy code to do some basic data formatting for a non-essential purpose; low-fidelity summarization of long texts you don't have time to read; ... (or more ethically questionably, machine generating mediocre advertising copy / routine newspaper stories / professional correspondence / school essays / astroturf propaganda on social media / ...) But "tutoring naïve students" seems currently like a poor use case. It would be better to spend some time teaching those students to better find and critically examine other information sources, so they can effectively solve their own problems. Again, it's not only old theorems where LLMs make up nonsense, but also (examples I personally tried) etymologies, native plants, diseases, translations, biographies of moderately well known people, historical events, machines, engineering methods, chemical reactions, software APIs, ... Other people have complained about LLMs making stuff up about pop culture topics like songs, movies, and sports. > good understanding of the fundamentals This does not seem likely in general. But it would be worth doing some formal study.