We'll see how I feel in the morning, but for now i seem to have convinced myself to actually read that fuckin anthropic paper
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@seanwbruno @jenniferplusplus
Will "is peer reviewed" change validity/or-lack of the paper?
Should it?@mikalai @seanwbruno @jenniferplusplus the thing that is a positive signal is that it *survived* peer review, which implies that there are multiple, knowledgeable, independent scientists in the area of study of the paper that read it and came to the conclusion, "the conclusions stated by this paper are supported by the data and arguments presented in the paper".
This paper would not survive peer review.
It is a flawed system but it is not worthless.
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I just
I'm not actually in the habit of reading academic research papers like this. Is it normal to begin these things by confidently asserting your priors as fact, unsupported by anything in the study?
I suppose I should do the same, because there's no way it's not going to inform my read on this
@jenniferplusplus no, usually academic studies have a null hypothesis of "the effect we're trying to study does not exist" and are required to provide evidence sufficient to reject that hypothesis
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"AI" is not actually a technology, in the way people would commonly understand that term.
If you're feeling extremely generous, you could say that AI is a marketing term for a loose and shifting bundle of technologies that have specific useful applications.
I am not feeling so generous.
AI is a technocratic political project for the purpose of industrializing knowledge work. The details of how it works are a distant secondary concern to the effect it has, which is to enclose and capture all knowledge work and make it dependent on capital.
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And now for a short break
I have eaten. I may be _slightly_ less cranky.
Ok! The results section! For the paper "How AI Impacts Skill Formation"
> we design a coding task and evaluation around a relatively new asynchronous Python library and conduct randomized experiments to understand the impact
of AI assistance on task completion time and skill development...
Task completion time. Right. So, unless the difference is large enough that it could change whether or not people can learn things at all in a given practice or instructional period, I don't know why we're concerned with task completion time.
Well, I mean, I have a theory. It's because "AI makes you more productive" is the central justification behind the political project, and this is largely a political document.
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I have eaten. I may be _slightly_ less cranky.
Ok! The results section! For the paper "How AI Impacts Skill Formation"
> we design a coding task and evaluation around a relatively new asynchronous Python library and conduct randomized experiments to understand the impact
of AI assistance on task completion time and skill development...
Task completion time. Right. So, unless the difference is large enough that it could change whether or not people can learn things at all in a given practice or instructional period, I don't know why we're concerned with task completion time.
Well, I mean, I have a theory. It's because "AI makes you more productive" is the central justification behind the political project, and this is largely a political document.
> We find that using AI assistance to complete
tasks that involve this new library resulted in a reduction in the evaluation score by 17% or two grade
points (Cohen’s d = 0.738, p = 0.010). Meanwhile, we did not find a statistically significant acceleration in
completion time with AI assistance.I mean, that's an enormous effect. I'm very interested in the methods section, now.
> Through an in-depth qualitative analysis where we watch the screen recordings of every participant in our
main study, we explain the lack of AI productivity improvement through the additional time some participants
invested in interacting with the AI assistant....
Is this about learning, or is it about productivity!? God.
> We attribute the gains in skill development of the control group to the process of encountering and subsequently resolving errors independently
Hm. Learning with instruction is generally more effective than learning through struggle. A surface level read would suggest that the stochastic chatbot actually has a counter-instructional effect. But again, we'll see what the methods actually are.
Edit: I should say, doing things with feedback from an instructor generally has better learning outcomes than doing things in isolation. I phrased that badly.
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> We find that using AI assistance to complete
tasks that involve this new library resulted in a reduction in the evaluation score by 17% or two grade
points (Cohen’s d = 0.738, p = 0.010). Meanwhile, we did not find a statistically significant acceleration in
completion time with AI assistance.I mean, that's an enormous effect. I'm very interested in the methods section, now.
> Through an in-depth qualitative analysis where we watch the screen recordings of every participant in our
main study, we explain the lack of AI productivity improvement through the additional time some participants
invested in interacting with the AI assistant....
Is this about learning, or is it about productivity!? God.
> We attribute the gains in skill development of the control group to the process of encountering and subsequently resolving errors independently
Hm. Learning with instruction is generally more effective than learning through struggle. A surface level read would suggest that the stochastic chatbot actually has a counter-instructional effect. But again, we'll see what the methods actually are.
Edit: I should say, doing things with feedback from an instructor generally has better learning outcomes than doing things in isolation. I phrased that badly.
They reference these figures a lot, so I'll make sure to include them here.
> Figure 1: Overview of results: (Left) We find a significant decrease in library-specific skills (conceptual
understanding, code reading, and debugging) among workers using AI assistance for completing tasks with a
new python library. (Right) We categorize AI usage patterns and found three high skill development patterns
where participants stay cognitively engaged when using AI assistance -
> We find that using AI assistance to complete
tasks that involve this new library resulted in a reduction in the evaluation score by 17% or two grade
points (Cohen’s d = 0.738, p = 0.010). Meanwhile, we did not find a statistically significant acceleration in
completion time with AI assistance.I mean, that's an enormous effect. I'm very interested in the methods section, now.
> Through an in-depth qualitative analysis where we watch the screen recordings of every participant in our
main study, we explain the lack of AI productivity improvement through the additional time some participants
invested in interacting with the AI assistant....
Is this about learning, or is it about productivity!? God.
> We attribute the gains in skill development of the control group to the process of encountering and subsequently resolving errors independently
Hm. Learning with instruction is generally more effective than learning through struggle. A surface level read would suggest that the stochastic chatbot actually has a counter-instructional effect. But again, we'll see what the methods actually are.
Edit: I should say, doing things with feedback from an instructor generally has better learning outcomes than doing things in isolation. I phrased that badly.
> Learning with instruction is generally more effective than learning through struggle.
I don’t think this is necessarily a true statement? Guided learning beats unproductive struggle, but learning through struggle that eventually succeed produces far better retention etc than guided learning that becomes passive/receptive. There’s a huge literature on this that I’m not up on at all, but I’m pretty sure it doesn’t break cleanly along that particular line.
(I don’t think my quibble derails your larger train of thought here)
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They reference these figures a lot, so I'll make sure to include them here.
> Figure 1: Overview of results: (Left) We find a significant decrease in library-specific skills (conceptual
understanding, code reading, and debugging) among workers using AI assistance for completing tasks with a
new python library. (Right) We categorize AI usage patterns and found three high skill development patterns
where participants stay cognitively engaged when using AI assistance@jenniferplusplus
Should title read there:
Impact of not forming mental, due to trusting and outsourcing thinking to AI in this case. -
> Learning with instruction is generally more effective than learning through struggle.
I don’t think this is necessarily a true statement? Guided learning beats unproductive struggle, but learning through struggle that eventually succeed produces far better retention etc than guided learning that becomes passive/receptive. There’s a huge literature on this that I’m not up on at all, but I’m pretty sure it doesn’t break cleanly along that particular line.
(I don’t think my quibble derails your larger train of thought here)
@inthehands @jenniferplusplus I would say that regardless whether guided learning from an entity that actually knows the material or independent learning tested against reality both best working with jumped-up autocorrect. The machine will tell you that you’re doing great things while spitting out garbage—counter-instructional is certainly one way to put it.
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They reference these figures a lot, so I'll make sure to include them here.
> Figure 1: Overview of results: (Left) We find a significant decrease in library-specific skills (conceptual
understanding, code reading, and debugging) among workers using AI assistance for completing tasks with a
new python library. (Right) We categorize AI usage patterns and found three high skill development patterns
where participants stay cognitively engaged when using AI assistance> As AI development progresses, the problem of supervising more and more capable AI systems becomes more difficult if humans have weaker abilities to understand code [Bowman et al., 2022]. When complex software tasks require human-AI collaboration,
humans still need to understand the basic concepts of code development even if their software skills are
complementary to the strengths of AI [Wang et al., 2020].Right, sure. Except, there is actually a third option. But it's one that seems inconceivable to the authors. That is to not use AI in this context. I'm not even necessarily arguing* that's better. But if this is supposed to be sincere scholarship, how is that not even under consideration?
*well, I am arguing that, in the context of AI as a political project. If you had similar programs that were developed and deployed in a way that empowers people, rather than disempowers them, this would be a very different conversation. Of course, I would also argue that very same political project is why it's inconceivable to the authors, soooo
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@inthehands @jenniferplusplus I would say that regardless whether guided learning from an entity that actually knows the material or independent learning tested against reality both best working with jumped-up autocorrect. The machine will tell you that you’re doing great things while spitting out garbage—counter-instructional is certainly one way to put it.
@c0dec0dec0de @inthehands @jenniferplusplus I think the problem is actually *engagement* - as well as correct challenge, learning requires active engagement with material (and effort to internalise it). Getting an LLM etc to "help" tends to reward disengagement (as well as potentially allowing you to "reduce the challenge" to the point where you're not actually doing anything hard yourself).
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> Learning with instruction is generally more effective than learning through struggle.
I don’t think this is necessarily a true statement? Guided learning beats unproductive struggle, but learning through struggle that eventually succeed produces far better retention etc than guided learning that becomes passive/receptive. There’s a huge literature on this that I’m not up on at all, but I’m pretty sure it doesn’t break cleanly along that particular line.
(I don’t think my quibble derails your larger train of thought here)
@inthehands @jenniferplusplus One of my personal hesitance to use the LLM tools much (despite incredible professional pressure to do so) is that my use of it (again, under professional necessity) has re-enforced my pre-existing belief that struggling through a problem, debugging and digging through source and so on has been CRITICAL to my skill development. It is something I have for (uh) 15+ years told less experienced software developers is critical to getting better / faster!
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@inthehands @jenniferplusplus One of my personal hesitance to use the LLM tools much (despite incredible professional pressure to do so) is that my use of it (again, under professional necessity) has re-enforced my pre-existing belief that struggling through a problem, debugging and digging through source and so on has been CRITICAL to my skill development. It is something I have for (uh) 15+ years told less experienced software developers is critical to getting better / faster!
@inthehands @jenniferplusplus Maybe there is a way to use things like Claude Code in ways that don’t disrupt this struggle learning pattern. This is one thing I’ve been trying to work out for myself! But so far I’ve not seen much about this concern or how the tools could be used in a way that results in the equivalent learning.
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> Learning with instruction is generally more effective than learning through struggle.
I don’t think this is necessarily a true statement? Guided learning beats unproductive struggle, but learning through struggle that eventually succeed produces far better retention etc than guided learning that becomes passive/receptive. There’s a huge literature on this that I’m not up on at all, but I’m pretty sure it doesn’t break cleanly along that particular line.
(I don’t think my quibble derails your larger train of thought here)
@inthehands Right, it's not universally the case. There are bad instructors and bad instructional contexts.
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I have eaten. I may be _slightly_ less cranky.
Ok! The results section! For the paper "How AI Impacts Skill Formation"
> we design a coding task and evaluation around a relatively new asynchronous Python library and conduct randomized experiments to understand the impact
of AI assistance on task completion time and skill development...
Task completion time. Right. So, unless the difference is large enough that it could change whether or not people can learn things at all in a given practice or instructional period, I don't know why we're concerned with task completion time.
Well, I mean, I have a theory. It's because "AI makes you more productive" is the central justification behind the political project, and this is largely a political document.
@jenniferplusplus you have inspired me to read it as well (over beer and pizza) and .. yeah, what she said. I think i gave up before the results section. i did feel that the prep-work to calibrate the experiment (e.g the local item dependence in the quiz) was pretty well done, but i will defer to any sociologist who says otherwise.
Why is all the so-called productivity in the paper at all?
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So, back to the paper.
"How AI Impacts Skill Formation"
https://arxiv.org/abs/2601.20245The very first sentence of the abstract:
> AI assistance produces significant productivity gains across professional domains, particularly for novice workers.
1. The evidence for this is mixed, and the effect is small.
2. That's not even the purpose of this study. The design of the study doesn't support drawing conclusions in this area.Of course, the authors will repeat this claim frequently. Which brings us back to MY priors, which is that this is largely a political document.
@jenniferplusplus It's less a claim and more an intentionally-unsubstantiated background premise which the supposed research will treat as an assumed truth.
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@inthehands Right, it's not universally the case. There are bad instructors and bad instructional contexts.
@jenniferplusplus
…and good struggles, which are what good instructors help create -
@jenniferplusplus It's less a claim and more an intentionally-unsubstantiated background premise which the supposed research will treat as an assumed truth.
@dalias Honestly, yes. I suspect the purpose of this paper is to reinforce that production is a correct and necessary factor to consider when making decisions about AI.
And secondarily, I suspect it's establishing justification for blaming workers for undesirable outcomes; it's our fault for choosing to learn badly.
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> As AI development progresses, the problem of supervising more and more capable AI systems becomes more difficult if humans have weaker abilities to understand code [Bowman et al., 2022]. When complex software tasks require human-AI collaboration,
humans still need to understand the basic concepts of code development even if their software skills are
complementary to the strengths of AI [Wang et al., 2020].Right, sure. Except, there is actually a third option. But it's one that seems inconceivable to the authors. That is to not use AI in this context. I'm not even necessarily arguing* that's better. But if this is supposed to be sincere scholarship, how is that not even under consideration?
*well, I am arguing that, in the context of AI as a political project. If you had similar programs that were developed and deployed in a way that empowers people, rather than disempowers them, this would be a very different conversation. Of course, I would also argue that very same political project is why it's inconceivable to the authors, soooo
And then we switch back to background context. We get a 11 sentences of AI = productivity. Then 3 sentences on "cognitive offloading". 4 sentences on skill retention. And 4 on "over reliance". So, fully 50% of the background section of the "AI Impacts on Skill Formation" paper is about productivity.
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"AI" is not actually a technology, in the way people would commonly understand that term.
If you're feeling extremely generous, you could say that AI is a marketing term for a loose and shifting bundle of technologies that have specific useful applications.
I am not feeling so generous.
AI is a technocratic political project for the purpose of industrializing knowledge work. The details of how it works are a distant secondary concern to the effect it has, which is to enclose and capture all knowledge work and make it dependent on capital.
@jenniferplusplus
bookmarked for future reference, boosting is not enough
