Someone else on HN pointed out this great quote from here (my memory might have altered it slightly)
https://nautil.us/how-i-rewired-my-brain-to-become-fluent-in...
Written by the person who wrote the book "a mind for numbers"
Anyway, this idea about teaching how to use AI is backwards: Good luck debugging code you aren't skilled enough to write.
"Students don't need to learn grammar and syntax and really any vocabulary. AI assistance lets them understand higher level German concepts without needing to know how to conjugate for gender and number or remember if a noun uses der, die, or das."
You see a lot of examples of things that seem absurd when applied to non-technical topics, but as soon as you apply them to computers all skepticism goes out the window. If I told you I have a person who speaks perfect English but frequently lies and outright makes things up, you'd say that's a politician and not to be trusted. But if I say I have a computer with those same properties, suddenly it's the most amazing thing in the world, and we should implicitly trust it to make our decisions and educate our children.
And yet, it seems that kind of politician is common and seemingly rules the world at this point. I'll let you decide what that means for the future of humanity.
To say that, as the article says, "Professors are shifting away from syntax and emphasizing higher-level skills" essentially means "Professors have stopped teaching programing languages and have started teaching how to turn natural language problems into AI prompts."
Higher-level problem solving skills are important, but I am skeptical of eschewing a lower-level foundation in favor of only teaching higher-level problem solving skills.
But as another commenter said, the analogy isn't 1:1. The example would be "Students don't need to learn German, they can just use Google translate for their time in Germany (and be hated for it by the locals)"
Are you maybe just in tech circles, not academic language circles?
Because a computer is a tool and a politician deems you a tool.
Anyway machine learning will change the way languages are taught. It did change how people learn games like Chess and Go. Having a conversation (with voice-to-text) with a bot in your second/third language will be an important part of language acquisition. It's honestly hard to imagine how it's not the case for me.
With an LLM it is possible to write code that's too "clever", thus you won't be able to debug it. And in many cases neither can the LLM.
We already have this problem, even without the LLM-generated code. I see younger developers pull out all sorts of tiny libraries because a) they think it will save them time, b) they are insecure about the correctness of their code, and c) they often have no idea where to start when problems start to happen (solving problem is usually writing a github ticket or blindly trying another library).
I think LLMs will exacerbate the problem. These days I rarely see engineering spirit in figuring out how things work, willing to understand deeply OS or compiler internals, or even trying some crazy ideas, like "let's write a JIT to speed up our DSL".
I mean, at the end of the day I am not a coder, therefore any code I debug I'm probably not good enough to write, yet I've found numerous bugs in both open source and proprietary code with suggestions on how to fix it.
Edit: unless the the bug fixes were trivial