As an ancient one (graduated college 1981), my use of AI is very conservative: look things up. Generate code I can read and understand in less than 30 minutes. This is working well for me, because when the AI botches the answer, I know quickly. It either works or fails fast: there's no importable function by that name, that keyword isn't in the language, that only works in a different version of the OS. I never ask it to do something I couldn't do myself in 10x the time (spent fixing typos or missing punctuation). If I ask it to do something I don't know how to do, I create tests - usually informal - to ensure that I understand what the code is doing. If the syntax is unfamiliar, I make it explain what it's doing, and then I informally test that explanation (usually toy examples at the command line). You must learn to do these things regardless of where the answers come from - the Internet, a journal, a book, a colleague. Otherwise >>when<< it fails, you will not be able to reason about the causes for the failure and how to find a correction.
I personally refuse to not heavily use any revolutionary technology as it comes out - the old man who says he never touches AI because it cannot be trusted is not the vision of who I want to be! Use it heavily. Understand it. Or lest be confused by its take over and success.
We need to seriously (or at least try to) make changes to our pedagogical processes.
Yea, struggling, is one way, but there are others like optimizing for spaced reptition, visualization, etc.
The shift should be from "grind these problems so the pain sticks with you", to "create a mini logic board in minecraft to blow up that mountain". Or, "build mini simulations to show how forces work, and tie them to an interactive applet".
Education isn't just the acquisition of facts or problem-solving skills. There are fundamental skills like reading that have their own specific pedagogies that must be used, because those skills are taught so early on.
The discourse surrounding education is mostly a discourse of spectators. The voices who actually do the work of teaching are the quietest.
Doing only interesting things tends to leave students with serious knowledge gaps. Anyone that's an autodidact or has interviewed/trained one will be familiar with this.
Discipline and grit are perhaps the greatest predictors for success. High intelligence and the foresight to know what to work on, per Hamming, will get you much farther, but with discipline and grit alone your chances of failing completely and ending up in the poor house are much reduced.
The guys who empty our bins have discipline and grit. The same ones turn out regularly whatever the weather doing back breaking work. They work far harder than I do.
Think ‘all is well’ now while ‘struggling’ generations are still alive and working. When they go away I’m more concerned. We may have to intentionally suppress tool access in education eg like certain levels of calculators being permitted for math classes, limit llm assistance similarly.
For me personally, most of skills that I managed to acquire (including coding) came from satisfying my curiosity and messing around with things to see how they work.
So, I don't think that struggle-based learning is the only way of learning or even the most efficient way of learning.
I think that this idea is more of a social ritual, than an actually useful method.
I was talking about being curious, how something works and figuring it out and being curious why something is done one way and not the other and figuring it out.
The “figuring out” is the struggle IMHO. I don’t know if struggle is the perfect term but that’s the part that IS the active learning. Doing a query and getting the response is very passive and not engaging. Trying to actively understand how the thing work is the process of learning that is slowly being replaced by AI. As the author says, it is pretty hard to continue to do that process when you know you could just query Claude and have something. But something is lost here, you didn’t actually acquire the knowledge to the same extent. In the worst case you learnt nothing.
You can implement a full HTTP server from scratch without learning one bit of the HTTP spec by just asking the AI tool you’re using to correct itself until tests pass. At the end you have an HTTP server, you didn’t grow doing so.
HN front page has a story about Bun being rewritten to Rust. How much of rust did the author of that PR learn by doing that process? I would say very little. If they were doing that process without AI they would very likely be Rust expert once done given the complexity and size of the codebase
Curious and fun intellectual exploration is my modus operandi too, but there is something to be said about struggling for weeks on a single idea and experiencing an Aha! moment which surely elevates your intellect in a way fun exploration does not.
> "But I do think it has become increasingly difficult to struggle for prolonged periods of time on a problem, now that we know the answer is often a few keystrokes away."
There's always struggle. Technology just moves the point at where it starts. Your decision is if you want to venture into those areas or are you content in staying in areas that are comfortable. But that has always been the case. Most people didn't struggle with differential equations, because they never decided to go there.
I have read and heard takes similar to OP's probably 50+ times from different people in the last few months (and years, now), and I agree mostly.
But I can't get over the myopic nature of this perspective. Technological advances often change the nature of work, and therefore change the nature (or location) of the "struggle".
I can imagine some hunter-gatherers probably admonishing early farmers at the dawn of agriculture for losing the "struggle" of hunting and foraging for their own food. It's much easier to drive a car than tame, train, and ride a horse. And so on throughout time.
So now with AI, some things that were hard before are now easy. So we move on to the next hard thing that maybe before was impossible or unimaginable. There is still hard work to be done.
Yea, struggling, is one way, but there are others like optimizing for spaced reptition, visualization, etc.
The shift should be from "grind these problems so the pain sticks with you", to "create a mini logic board in minecraft to blow up that mountain". Or, "build mini simulations to show how forces work, and tie them to an interactive applet".
The discourse surrounding education is mostly a discourse of spectators. The voices who actually do the work of teaching are the quietest.
So, I don't think that struggle-based learning is the only way of learning or even the most efficient way of learning.
I think that this idea is more of a social ritual, than an actually useful method.
One can travel from one end to the other.
I was talking about being curious, how something works and figuring it out and being curious why something is done one way and not the other and figuring it out.
You can implement a full HTTP server from scratch without learning one bit of the HTTP spec by just asking the AI tool you’re using to correct itself until tests pass. At the end you have an HTTP server, you didn’t grow doing so.
HN front page has a story about Bun being rewritten to Rust. How much of rust did the author of that PR learn by doing that process? I would say very little. If they were doing that process without AI they would very likely be Rust expert once done given the complexity and size of the codebase
Yeah this has been my experience too.
I have read and heard takes similar to OP's probably 50+ times from different people in the last few months (and years, now), and I agree mostly.
But I can't get over the myopic nature of this perspective. Technological advances often change the nature of work, and therefore change the nature (or location) of the "struggle".
I can imagine some hunter-gatherers probably admonishing early farmers at the dawn of agriculture for losing the "struggle" of hunting and foraging for their own food. It's much easier to drive a car than tame, train, and ride a horse. And so on throughout time.
So now with AI, some things that were hard before are now easy. So we move on to the next hard thing that maybe before was impossible or unimaginable. There is still hard work to be done.