This is key, I think, and gets overshadowed by people being offended by seeing bad vibecode or claims of 10x speeds, etc.
The most important learning that happens is not when we ask and get the answer to our question right away. It's when we stretch ourselves to seek out the answer, fail a few times, think deeply, then perhaps after a nap, solve the problem. That kind of knowledge is priceless because it not only gets you an answer it gets you some errant paths you can use to avoid problems in future problem solving as well as getting you increased trust in your own thinking.
If the next generations skip this step, they'll always think answers are supposed to be easy to find and will find themselves more and more dependent on AI and less and less confident in their own brains.
This seems like a very polite way of saying they will become less intelligent and less capable
You don't learn by reading, you learn by doing.
In this case, simply reading the output of an LLM isn't going to substantially educate you.
Classy.
> if you think reading code isn’t worth it.
I didn't say that.
With anything you learn, sure, you need to read it, but you haven't actually learned it until you try to do it.
As millions of teenagers find out in high school, it is not possible to "learn" trigonometry or calculus by reading the problems; they actually have to drill problems to pass.
> Do you think novelists just write novels from nothing? They read books.
Excellent example! Even with novelists, and professional authors, they only get better by writing. Face it - millions of people read just as much as (even more) than best selling authors, and yet those millions are unable to produce anything of note.
> When was the last time you read the code for the best open source software in your industry?
All the time; how else would I know that simply reading is not sufficient to learn something?
I'm surprised that this point is even in contention; it is almost common knowledge that you can't learn from reading alone; it's the practice that results in learning, not the reading.
I’m not seeing this. And based on what we’re seeing at the university level, I’m not expecting to.
(The preliminary research so far supports this: using AI to do the hard assignments produces poor learning outcomes, but using AI as a tutor, or even just for help with the hard assignments, produces slightly better learning outcomes.)
I think what you're seeing is the effect of the incentives of the system. The system uses simplistic numbers like grades as proxies for actual learning, and these grades heavily influence students' job prospects, and so you're simply seeing Goodhart's Law in action. Given how easy current methods of skill assessment are to game with AI, my guess is the entire system has to be overhauled.
Source? The few people I’ve seen try to do this wind up with a terrible understanding of the material, with large knowledge gaps and one or two fundamental fuckups. In every case, an introductory textbook would have been better. (It would also have been harder.)
The analogy is unlimited typing in Gmail won’t make you a better writer or typesetter on its own.
I've seen this work well at a job when there's a feedback loop for juniors that incentivized them to learn with more scope and compensation
If anything it allows to be as lazy as possible. I have not seen anyone digging deeper with the AI tools.
This is a testable hypotheses with severe lack of citations. Intuition would argue the opposite. We learn by using our brains, if we offload the thinking to a machine and copy their output we don‘t learn. A child does not learn multiplication by using a calculator, and a language learner will not learn a new language by machine translating every sentence. In both cases all they’ve learnt is using a tool to do what they skipped learning.
1. AI is for cheating and doing the work for you. Obviously it won't help you learn faster because you won't have to do any thinking at all.
2. AI is an always-available question answering machine. It's like having a teaching assistant who you can ask about anything at any time. This means you can greatly accelerate the process of learning new things.
I'm in team 2, but given how many people are in team 1 (and may not even acknowledge team 2 as even being a possibility) I suspect there may be some core values or different-types-of-people factors at play here.
But even with category 2. I think that still does not absolve AI as a cheating machine. Doing research is a skill and if you ask AI to do the research for you that is a skill a junior developer simply never learns.
"The expertise reversal effect is present when instructional assistance leads to increased learning gains in novices, but decreased learning gains in experts."
There's a whole lot of depth to the question of how AI tools support or atrophy learning for different levels of expertise.
There is even preliminary research evidence for this, e.g. https://www.mdpi.com/2076-3417/14/10/4115 and https://www.sciencedirect.com/science/article/pii/S2666920X2...
So your first study actually concludes the opposite. It concluded that all AI users performed worse, but the effect was smaller for students which used AI as a tutor.
The second meta analysis I don‘t quite understand. I understand they conclude that using AI tutor shows significant improvement, but I don‘t understand the methodology. I may be misunderstanding but it seems to simply count papers which shows positive outcomes and reaches conclusion that way. I think that methodology is deeply flawed as it will amplify whichever biases are present in the studies it uses. I also think the lack of control groups is a major issues. If we are comparing AI tutor to nothing, off course the AI tutor is gonna perform better. We need to compare to traditional methods. And this is especially relevant in our discussion because junior developers usually have excellent access to senior developers (via peer review, pair programing, etc.), much better then student’s access to tutors for that matter.
So out of the meta-analysis I picked the paper with the strongest claim (trying to steel-man it) which is this one: https://online-journal.unja.ac.id/JIITUJ/article/view/34809/...
It claims the following in the abstract:
> The results indicated that students employing AI tutors shown significant improvements in problem-solving and personalized learning compared to the control group.
Now when I look at the control group it claims this (also in the abstract):
> Participants were allocated to a control group receiving conventional training and an experimental group utilizing AI technology,
But when I look into the methodology section I see this:
> The researchers classified the patients into two groups: MathGPT and Flexi 2.0
MathGPT and Flexi 2.0 are both AI tutors. Now I am confused, where is the control group and how was this “conventional training conducted”?
The methodology section actually tells a different story from the abstract:
> This research utilized a quantitative methodology via a quasi-experimental design.
By quasi-experimental design they mean that they tested the same students before and after AI intervention. And concluded that the AI tutor helped them improve. Now this is not what control group means, so the researchers are actually lying by omission in the abstract. This is a spectacularly bad experimental design and I wonder how it would pass peer review, so I look at the publisher Jurnal Ilmiah Ilmu Terapan Universitas Jambi. So not exactly a reputable journal.
I still stand by my no evidence for a testable hypotheses. I suspect that your first link is actually correct in that AI is bad for students and just less bad if it is used as a tutor.
For such a person, I believe AI can be very empowering for learning. Like Google, wikipedia and stack overflow, Arxiv before it - AI tools give access to a lot of information. It allows to quickly dig deep into any topic you can imagine. And yes, the quality is variable - so one needs to find ways to filter and synthesize from imperfect info. But that was also the case before. Furthermore AI tools can be used to find holes in arguments or a paper. And by coding one can use it to test out things in practice. These are also powerful (albeit imperfect) learning tools. But they will not apply themselves.
And as we are talking about junior developers it is safe to assume your conditions (1), (2), and (4) are all true, if any of them are false, then why did that person apply for and get a job as a junior developer? As for condition (3), all workplaces eventually hires a person who does not fulfill this, then they either fire that person, or they give them a talk and the developer grows out of it and changes their behavior to fulfill that condition.
Aside: you listed 4 conditions for learning. I am not sure these are actually conditions recognized as such by behavior science. In fact, I doubt they are and that these conditions are just your opinions (man).
Companies with AI will move faster than those without.
AI itself could subsume what we collectively consider as Engineering Taste.
AI is faster at what it does. So even if a junior costs less on his own than AI. Paying extra for AI means gaining first mover advantage.
Only if AI feeds on more taste than garbage.
This is a contradictory statement imo.
Digging deep still takes the same amount of time it used to. AI accelerates the surface level (badly, tbh), it doesn't accelerate digging deep. Becoming an expert still takes time and effort, there really aren't shortcuts.
To torture the Iron Man metaphor a bit. If you're not an expert without the AI, then you're not an expert with it.