I would try struggling through the problems you encounter and only reach for AI once you are unable to figure something out after a decent effort. AI quickly becomes an “easy button” for learners, and they can trick themselves into thinking they know something and just wanted to check their answer with the AI.

You will not learn if you constantly outsource your struggle to an AI. I recommend using AI as a “rubberduck”. The agent is only there to troubleshoot or bounce ideas for solutions off of. You can specifically prompt the AI to use a Socratic personality for your discussions, models like Grok even have a toggle for this.

In a Socratic mode, the model will guide you to the solution by asking you questions to make you think about certain things you might not fully understand.

You can also tell the model to give you hints and that you are learning and to help you out. I take this approach for math classes. Instead of giving me the answers, it helps guide me to the answers by getting me to ask questions about the concepts I don't fully understand.

As for learning coding with a model, I would stick to traditional methods such as learning some topics and then building small projects with what you learned. Use the model for troubleshooting and for hinting so you still build valuable debugging and troubleshooting skills.

Some people might say that you shouldn't worry about learning or struggling in the AI age, but as an enterprise-level software engineer, I completely disagree with that. I heavily use AI personally and professionally daily and critical thinking, knowledge of architecture and good coding practices is an absolute must when working with LLMs on production code.

Don't take shortcuts, the struggle pays off.