Now you’re just cherry picking some surface-level similarities.
You can see the difference in the process in the results, for example in how some generated pictures will contain something like a signature in the corner, simply because it resembles the training data - even though there is no meaning to it. Or how it is at least possible to get the model to output something extremely close to the training data - gizmodo.com/ai-art-generators-ai-copyright-stable….
That at least proves that the process is quite different to the process of human learning.
The question is how much those differences matter, and which similarities you want to focus on.
Human learning is similar in some ways, but greatly differs in other ways.
The fact that you’re picking and choosing which similarities matter and which don’t is just your arbitrary choice.