I was teaching a lot of stuff to students: physics, math, statistics (during my university times) now I teach programming and Machine Learning.
I am torn between instructional based approach, which has this advantage that gives people a set of minimal skills to start doing stuff by themselves and the project-based approach, which is probably more interesting, but is very hard to squeeze in a relatively short classes time and also might left gaps, even in the base areas, as there is no time to cover everything end-to-end (think of teaching people about for loop, as it helps working with lists, but do not mention a while loop).
So, there should be some ideal holy grail in between both ways of teaching: show them everything versus let them explore and invent everything by themselves.
The crux is that instructional-based approach works great if it is well tuned to the student's needs. The problem is that every student has different needs and capabilities, so it is hard to do something that will work for everyone. So something is too difficult for some people, while being too easy for others.
That's why we have Bloom's 2 sigma problem - 1:1 learning works orders of magnitude better than in-class learning.
Now, LLM AI enters the scene, as the article is mentioning - individualized instruction could be finally achievable and I am much less skeptical about that than the author, as I tested that on myself, the good thing is I can ask and ask for more and more details if I am not able to grok something and my "teacher" is always patient, has as much time as I need.
It does not mean that teachers are not needed, just the opposite, because the key problem is to know what to learn, LLM will just do what you ask for, nothing more, so one need to know what to ask about. But once someone is on the specific topic and problem, you can really go quite far with LLM as a tutor.