I think you're misunderstanding my point a bit, and I'll admit the calculator comparison was overly simplistic. I'm not saying LLMs are the same as calculators or that they don't have unique problems. I'm saying those problems don't automatically make the technology inherently detrimental to learning.

And I actually agree with you about people blindly trusting LLMs. I see this shit at work all the time in tech support. People will throw a question into Copilot, get a confidently incorrect answer, and then pass it along to other people without even attempting to verify it. It's incredibly frustrating, especially when they're doing a job where they really should be developing the knowledge to recognize when something is wrong.

But that's kind of my point. There's a massive difference between using an LLM to avoid thinking and using one to help you think, learn, and accomplish things.

The Chinese education study is genuinely concerning, I'm not dismissing it. But the 80% figure is based on behavior consistent with outsourcing homework, not the researchers somehow knowing that 80% of students consciously chose to stop thinking. And the students who continued putting comparable effort into their homework experienced much smaller learning losses. That seems like a pretty important distinction.

The other study shows a correlation between AI use, cognitive offloading, and lower critical thinking scores. Again, worth taking seriously, but that doesn't establish causation or prove that using LLMs prevents someone from developing expertise.

And Vending-Bench? Yeah, LLMs can do some absolutely stupid shit when left to operate autonomously. I don't disagree. But how does that invalidate using one as a tool under human direction? I'm not asking an LLM to independently run my life or make decisions without oversight.

I use LLMs constantly for development work. I've been a computer nerd since I was a kid, taught myself Linux, networking, messed around with programming, and all sorts of other things through documentation, forums, and tutorials. But programming was always something I struggled to actually do, despite understanding a lot of the underlying concepts.

LLMs have helped bridge that gap. I can now build software I literally couldn't have built before. I can investigate unfamiliar systems, ask questions about concepts I don't understand, explore different approaches, and actually put that knowledge to use.

And no, that doesn't make me an expert. I'm well aware of the gaps in my knowledge, which is why I use things like SonarQube, CodeQL, testing, and experienced developers to help catch things I might miss.

But I am learning, and I'm accomplishing things I couldn't before. I don't see how that's somehow a negative.

I also agree that the corporate AI hype is ridiculous, that these companies oversell what their models can do, and that people treating LLMs like oracles is a serious problem. You'll get no argument from me there.

Where I disagree is jumping from "a lot of people use these tools poorly, and that can harm learning" to "these tools will inevitably reduce the number of experts and prevent people from becoming proficient."

That's a much broader claim than I think these studies actually support.

And that's really what frustrates me about the AI discourse on Lemmy. There's plenty of legitimate criticism to be made, but it so often turns into this blanket dismissal of anyone who uses AI, like we're all just mindlessly copying whatever ChatGPT spits out.

I'm not defending OpenAI or Altman or any of these corporations. I'm defending the idea that these tools can be genuinely useful for people who know their limitations and actually want to learn.

Those aren't the same thing.