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How University Students Use Claude

(www.anthropic.com)
395 points pseudolus | 2 comments | 09 Apr 25 15:41 UTC | HN request time: 0.415s | source
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dtnewman ◴[09 Apr 25 16:23 UTC] No.43633873[source]▶
> A common question is: “how much are students using AI to cheat?” That’s hard to answer, especially as we don’t know the specific educational context where each of Claude’s responses is being used.

I built a popular product that helps teachers with this problem.

Yes, it's "hard to answer", but let's be honest... it's a very very widespread problem. I've talked to hundreds of teachers about this and it's a ubiquitous issue. For many students, it's literally "let me paste the assignment into ChatGPT and see what it spits out, change a few words and submit that".

I think the issue is that it's so tempting to lean on AI. I remember long nights struggling to implement complex data structures in CS classes. I'd work on something for an hour before I'd have an epiphany and figure out what was wrong. But that struggling was ultimately necessary to really learn the concepts. With AI, I can simply copy/paste my code and say "hey, what's wrong with this code?" and it'll often spot it (nevermind the fact that I can just ask ChatGPT "create a b-tree in C" and it'll do it). That's amazing in a sense, but also hurts the learning process.

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1. whatever1 ◴[10 Apr 25 01:02 UTC] No.43639628[source]▶
The counter argument is that now you can skip boilerplate code and focus on the overall design and the few points that brainpower is really needed.

The amount of visualizations that i have made after chat gpt was released has increased exponentially. I loath looking the documentation again and again to make a slightly non standard graph. Now all of the friction is gone! Graphs and visuals are everywhere in my code!

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2. hansvm ◴[10 Apr 25 01:18 UTC] No.43639707[source]▶
> focus on [...] the few points that brainpower is really needed

The person you're responding to is talking about it from an educational perspective though. If your fundamentals aren't solid, you won't know that exponentially smoothed reservoir sampling backed by a splay tree is optimal for your problem, and ChatGPT has no clue either. Trying things, struggling, and failing is crucial to efficient learning.

Not to mention, you need enough brain power or expertise to know when it's bullshitting you. Just today it was telling me that a packed array was better than my proposed solution, confidently explaining why, and not once saying anything correct. No prompt changes could fix it (whether restarting or replying), and anyone who tried to use less brainpower there would be up a creek when their solution sucked.

Mind you, I use LLMs a lot, including for code-adjacent tasks and occasionally for code itself. It's a neat tool. It has its place though, and it must be used correctly.