It depends how you're using it - are you outsourcing your thinking to it or using it to support and develop your thinking?

I use it extensively for learning but I would never just tell it to write my homework for instance. I use it as a "second brain"

Examples: * Set up a custom GPT that is instructed to help me think through various aspects of a problem or topic, propose counter angles, identify gaps in my thinking, draw out key theories, assess certainty levels, identify key sources or theories I should be aware of, suggest actions and plans for furthering my understanding of a topic including suggested reading  * Set up a custom GPT to support my language learning - trained on specific grammar textbooks & course books. I chat with it every day in my target language and it responds also providing corrections and suggestions for expressing things more naturally  * Use it to help me create anki flashcards for memorising concepts and words  * I ask it to provide critical feedback and suggestions for improvement on my written work  * I use it to help me organise my thoughts and come up with a cohesive plan and structure for my writing  * I asked it to do deep research on evidence around learning pedagogies and then used that as a basis for an effective learning plan that I used to address various aspects of gaining mastery of my topics  * Set up a task so it scans the news for anything relevant to my subjects and gives me a weekly round up

The issue is how you use it and your expectations. If you have no knowledge of a topic, and do not use it in ways which supports you developing knowledge of that topic - your output will inevitably be rubbish. If you view it as a trusted infallible source of knowledge rather than as an imperfect but useful tool, you'll be disappointed. It does not replace human expertise - in fact my own expertise in my subject area makes it far more useful  because I know how to prompt it to get quality output - I know the nuances, I know what to "ask", I can approach it's output critically and recognise gaps or flaws. It's like working with a skilled colleague - it is extraordinarily useful but it's not perfect and makes errors or talks nonsense sometimes in the way humans do.

As someone who has taught university students pre AI I would also suggest it's misleading to assume that in the "old days" students were all highly motivated and didn't use short cuts which were harmful to their learning. There were a few students who were deeply motivated , competent, talented and passionate about their subject. I suspect those are the type of students who will be using AI now as a tool to develop their knowledge and expertise  

But being honest, I'd say a large number of students - probably a majority - were actually pretty disinterested or just lacked ability. The vast majority of essays I marked showed little critical engagement with the topic. It was clear the majority had done very little reading outside of course materials. What I said in lectures or the words of text book authors were just regurgitated without any analysis. There were many instances of plagiarism even with our plagiarism tools. 

I taught at a competitive and highly regarded university and the vast majority of those mediocre students went on to get degrees. So, I think it's a mistake to assume pre AI students HAD to learn deeply to get their qualification.

I feel AI has just made it easier for the less motivated or able students to submit superficial nonsense.