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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@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 -
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 it's not a great lit review/paper in terms of connecting to broader literature; that is however typical for software research (not for more empirical fields like psychology imho)
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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.
@jenniferplusplus "Learning with instruction is generally more effective than learning through struggle"
I'm not sure I agree! Desirable difficulties literature and metacognition lit both agree short term failures can lead to better long term retention (people's lack of belief in this is often pointed to as a reason we engage in inefficient problem solving). That is one reason project based learning can sometimes beat sage on a stage lectures
Eg classic lit here: https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork_RBjork_2011.pdf
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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.
Chapter 3. Framework.
Finally.
Paraphrasing a little: "the learning by doing" philosphy connects completing real world tasks with learning new concepts and developing new skills. Experiental learning has also been explored to mimic solving real world problems. We focus on settings where workers must acquire new skills to complete tasks. We seek to understand both the impact of AI on productivity
and skill formation. We ask whether AI assistance presents a tradeoff between immediate productivity and longer-term skill development or if AI assistance presents a shortcut to enhance both.Right. There it is again: productivity. Even within this framing, there are at least 3 more possibilities. That AI does not actually increase productivity; that AI has no effect at all; or that AI improves learning only. I think it's very telling that the authors don't even conceive of these options. Particularly the last one.
But I'm becoming more and more convinced that the framing of productivity as an essential factor to measure and judge by is itself the whole purpose of this paper. And, specifically, productivity as defined by production output. But maybe I'm getting ahead of myself.
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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.
I love how the introduction already frames the industrial revolution all wrong. Product lines were all about de-skilling people, by turning artisans into factory workers.
Then comparing factory work with software development. When software development is the exact opposite.
Maybe those authors should visit a real factory and talk to some real engineers.
And I find it absolutely fascinating how they talk about "professional domains", when all they mean is "software development".
(Not even through the first page and already feeling the need to vent, sorry.)
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Chapter 3. Framework.
Finally.
Paraphrasing a little: "the learning by doing" philosphy connects completing real world tasks with learning new concepts and developing new skills. Experiental learning has also been explored to mimic solving real world problems. We focus on settings where workers must acquire new skills to complete tasks. We seek to understand both the impact of AI on productivity
and skill formation. We ask whether AI assistance presents a tradeoff between immediate productivity and longer-term skill development or if AI assistance presents a shortcut to enhance both.Right. There it is again: productivity. Even within this framing, there are at least 3 more possibilities. That AI does not actually increase productivity; that AI has no effect at all; or that AI improves learning only. I think it's very telling that the authors don't even conceive of these options. Particularly the last one.
But I'm becoming more and more convinced that the framing of productivity as an essential factor to measure and judge by is itself the whole purpose of this paper. And, specifically, productivity as defined by production output. But maybe I'm getting ahead of myself.
And now we have actual research questions! It feels like it shouldn't take this long to get these, but w/e
1. Does AI assistance improve task completion productivity when new skills are required?
2. How does using AI assistance affect the development of these new skills?We'll learn how the authors propose to answer these questions in the next chapter: Methods.
But first, there is a 6 year old in here demanding I play minecraft, and I'd rather do that.
To be continued... probbaly
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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!
@r343l @inthehands @jenniferplusplus
“struggling through a problem, debugging & digging through source & so on has been CRITICAL to my skill development” … because the “cognitive struggle” is like doing physical exercise or activity to get your body and brain better + faster doing it.
Making a request & waiting for the output result is like ordering a meal from a restaurant menu & somehow expecting that action to make you an expert Chef. At most, you become an expert at ordering off a menu. -
@jenniferplusplus "Learning with instruction is generally more effective than learning through struggle"
I'm not sure I agree! Desirable difficulties literature and metacognition lit both agree short term failures can lead to better long term retention (people's lack of belief in this is often pointed to as a reason we engage in inefficient problem solving). That is one reason project based learning can sometimes beat sage on a stage lectures
Eg classic lit here: https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork_RBjork_2011.pdf
@grimalkina I think I phrased that badly. I'm aware and agree that doing a thing, mistakes and all, is very often has better learning outcomes than lectures from experts.
What I meant was doing a thing with guidance and feedback from an expert has better outcomes than doing it in isolation.
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@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.
@jenniferplusplus
The purpose of a paper is the assumptions it makes. -
> 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.
@jenniferplusplus I think the 'control group' here didn't use AI at all. At least that's how I read it. And they completed the task in more or less the same time and two grades better results.
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And now we have actual research questions! It feels like it shouldn't take this long to get these, but w/e
1. Does AI assistance improve task completion productivity when new skills are required?
2. How does using AI assistance affect the development of these new skills?We'll learn how the authors propose to answer these questions in the next chapter: Methods.
But first, there is a 6 year old in here demanding I play minecraft, and I'd rather do that.
To be continued... probbaly
There's a whole series of recent studies from MIT, CMU, Boston Consulting Group, BBC, and Oxford Economics arguing that AI/LLM assistants do NOT improve productivity.
Walk-through here:
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@jenniferplusplus
The purpose of a paper is the assumptions it makes.@dalias Not all the time. But if it's research conducted and published by the in-house research team of Anthropic? Yeah, probably
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@dalias Not all the time. But if it's research conducted and published by the in-house research team of Anthropic? Yeah, probably
@jenniferplusplus Yeah. Or if there are conflicts of interest in the funding, or if the researchers are just aspiring to getting hired into the industry or getting VC for their own ideas.
