undefined

points

[-]

Actually, you're both right. Using AI as a supplementary learning aid -- i.e. students use AI as a personalized tutor but still do the assignments themselves -- produces better outcomes. But using AI as a crutch -- i.e. using it to do the assignments -- produces worse outcomes.

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...

by simonw3 hours ago|

prev|

[-]

This seems to me like one of those things where people go into it with widely different initial assumptions.

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.

by runarberg3 hours ago|

parent|

[-]

This is also a testable hypothesis. I would like to see usage statistics before making assumptions here but my gut feeling is that an overwhelming AI usage (like > 90%) would fall into your category 1.

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.

by simonw2 hours ago|

parent|

[-]

This is interesting and relevant: https://www.sciencedirect.com/science/article/pii/S095947522...

"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.

by jononor5 hours ago|

prev|

[-]

As a precondition I think we have to assume that the person in question 1) wants to learn and 2) is smart enough to absorb new info and apply it and 3) reflects enough to adjust their approach when hitting bottlenecks or making mistakes 4) has a drive to create. Without these, self driven learning is not viable - and that has very little to do with AI.

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.

by runarberg4 hours ago|

parent|

[-]

Who is talking about self driven learning? Every workplace teachers their juniors how to do their job, and how to become better at their jobs.

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).