Sometimes learning means understanding, aka a deep dive on the domain. Only a few domains are worth that. For the others, it's only about placing landmark so you can quickly recognize a problem and find the relevant information before solving it. I believe the best use case of LLMs is when you have recognized the problem and know the general shape of the solution, but have no time to wrangle the specifics of the implementation. So you can provide the context and its constraint in order to guide the LLM's generation, as well as recognize wrong outputs.

But that's not learning or even problem's solving. It's just a time saving trick. And one that's not reliable.

And the fact is that there's a lot of information about pretty much anything. But I see people trying to skip the foundation (not glamorous enough, maybe) and go straight for the complicated stuff. And LLMs are good for providing the illusion that it can be the right workflow.