How University Students Use Claude

How can I, as a student, avoid hindering my learning with language models?

I use Claude, a lot. I’ll upload the slides and ask questions. I’ve talked to Claude for hours trying to break down a problem. I think I’m learning more. But what I think might not be what’s happening.

In one of my machine learning classes, cheating is a huge issue. People are using LMs to answer multiple choice questions on quizzes that are on the computer. The professors somehow found out students would close their laptops without submitting, go out into the hallway, and use a LM on their phone to answer the questions. I’ve been doing worse in the class and chalked it up to it being grad level, but now I think it’s the cheating.

I would never do cheat like that, but when I’m stuck and use Claude for a hint on the HW am I loosing neurons? The other day I used Claude to check my work on a graded HW question (breaking down a binary packet) and it caught an error. I did it on my own before and developed some intuition but would I have learned more if I submitted that and felt the pain of losing points?

I believe conversation is a one of the best ways to really learn a topic, so long as it is used deliberately.

My folk theory of education is that there is a sequence you need to complete to truly master a topic.

Step 1: You start with receptive learning where you take in information provided to you by a teacher, book, AI or other resource. This doesn't have to be totally passive. For examble, it could take the form of Socratic questioning to guide you towards an understanding.

Step 2: Then you digest the material. You connect it to what you already know. You play with the ideas. This can happen in an internal monologue as you read a textbook, in a question and answer period after a lecture, in a study group conversation, when you review your notes, or as you complete homework questions.

Step 3: Finally, you practice applying the knowledge. At this stage, you are testing the understanding and intuition you developed during digestion. This is where homework assignments, quizes, and tests are key.

This cycle can occur over a full semester, but it can also occur as you read a single textbook paragraph. First, you read (step 1). Then you stop and think about what this means and how it connects to what you previously read. You make up an imaginary situation and think about what it implies (step 2). Then you work out a practice problem (step 3).

Note that it is iterative. If you discover in step 3 a misunderstanding, you may repeat the loop with an emphasis on your confusion.

I think AI can be extremely helpful in all three stages of learning--in particular, for steps 2 and 3. It's invaluable to have quick feedback at step 3 to understand if you are on the right trail. It doesn't make sense to wait for feedback until a teacher's aid gets around to grading your HW if you can get feedback right now with AI.

The danger is if you don't give yourself a chance to struggle through step 3 before getting feedback. The amount of struggle that is appropriate will vary and is a subtle question.

Philosophers, mathematicians, and physicists in training obviously need to learn to be comfortable finding their way through hairy problems without any external source of truth to guide them. But this is a useful muscle that arguably everyone should exercise to some extent. On the other hand, the majority of learning for the majority of students is arguably more about mastering a body of knowledge than developing sheer brain power.

Ultimately, you have to take charge of your own learning. AI is a wonderful learning tool if used thoughtfully and with discipline.