When AI helps too much
29 April 2026
Preamble: The research discussed below is a preprint that can be accessed here – it has not yet completed peer review. The findings are interesting but they should be read as early, task-specific results rather than settled evidence. More on that below.
The email that AI handled better than I could
Not long ago I found myself needing to write a difficult email. The recipient would not welcome its contents. It had to be transparent, precise and fully compliant with university rules and regulations. As a mediator in the situation, I also needed to ensure that nothing in the message could leave me exposed to complaints. I was, in essence, only the messenger – the message had to be correct, clear and carefully worded.
The scenario practically demanded AI assistance. So I used one of the available platforms. I explained the situation, the required outcome, the regulatory constraints, the tone. The process took perhaps ten minutes of careful explanation on my part. Then, in seconds, the platform generated the message.
It was exactly right. Clear, compliant, professionally phrased and constructed in a way that protected my position entirely. If I am honest, it was better than what I would have produced myself. In that context, AI was not just useful – it was genuinely valuable in a way that is hard to argue with.
The second email
The same day, I needed to write another message. It was less complex, closer to the kind of communication I handle routinely as part of my daily work. There was no particular regulatory minefield to navigate, no unusual sensitivity to manage. An ordinary, professional email.
And I simply could not be bothered to write it myself.
Why engage my brain when I could sketch out a few points, hand them to AI and receive a polished, professional message within seconds? The elegant solution from earlier in the day had reset my expectations. The bar for what felt worth doing manually had shifted, almost without my noticing.
This is where the problem begins. The temptation is large. And it comes at a cost.
Enter the research
A recently published preprint provides causal evidence that what I experienced is not merely personal idiosyncrasy. The paper, by researchers at Oxford, MIT and UCLA, reports findings from a series of randomised controlled trials involving over 1200 participants. Across tasks including mathematical reasoning and reading comprehension, they found that while AI assistance improved performance in the short term, participants who had used AI performed significantly worse on subsequent unassisted tasks. Importantly, they were more likely to give up. These are short, controlled tasks, so the results reflect immediate behavioural changes rather than long-term cognitive decline.
What makes this particularly striking is the speed at which the effects emerged. Measurable reductions in persistence and independent performance appeared after approximately 10 min of AI interaction. Not months of habitual use. Ten minutes!
The authors argue that AI conditions people to expect immediate answers, reducing their tolerance for productive difficulty – the kind of struggle that is not incidental to learning, but central to it. A mentor or tutor scaffolds learning precisely by not always providing the answer. Current AI systems are optimised to do the opposite. What this suggests is not just a performance effect, but a shift in effort strategy: once an answer is available, people invest less in solving the problem themselves.
A Note on the research itself
Before going further, the limitations of this study are worth stating clearly. It is a preprint, currently under review, and has not yet been independently replicated. The tasks examined – structured mathematical and reading problems – are well-suited to controlled experimentation, but are narrower than the full range of ways people use AI in real professional and academic contexts. There is no preregistration or open data currently available, which limits the ability to audit the findings independently.
But, with these caveats clearly in place: the findings resonate.
What practice actually does
Some years ago I had a productive academic year in which I wrote four research papers in succession. The first was painful from start to finish. The content was demanding, but so was the writing itself. It had been some time since I had written a full paper, and it showed. Multiple rounds of revision were required before it was finally accepted for publication.
The second paper was easier. The third, easier still. By the fourth, the manuscript came together in roughly two weeks. Co-authors required minimal revisions. Peer review went smoothly. The content of that paper was no less complex than the first – perhaps even more complex – but the continuous practice had transformed the process.
Writing, it turns out, is a skill that responds to training in the way most skills do. The more you do it, the more fluent it becomes. The connections between thinking and expression grow stronger. The ability to structure an argument, anticipate a reader's objections and calibrate tone all improve – but only through repeated, effortful practice.
AI, used unreflectively, removes exactly that practice.
The real question
None of this means AI has no place in academic or professional writing. The complex email I described earlier is a genuinely good use case. When a task requires holding in mind a large network of constraints – regulatory requirements, institutional tone, professional risk, precise language – AI's ability to manage that complexity simultaneously is remarkable. It does something that is genuinely difficult to do well unassisted.
The question is not whether to use AI. The question is how.
The risk is not the tool itself but the habit of reaching for it automatically, including for tasks that would benefit from the effort of doing them yourself. The professor who has had an assistant handle everything for so long that they can no longer draft a straightforward memo is not a hypothetical figure. That trajectory is now available to anyone with a smartphone, tablet of computer to access AI – and it can apparently begin within ten minutes.
What the preprint suggests, and what my own experience supports, is that the cost of AI convenience is not always visible at the moment of use. It accumulates quietly, in the form of skills not practised, difficulties not worked through, and a gradually lowered threshold for engaging with hard problems independently.
A more useful relationship with AI
The more productive approach – easier to describe than to maintain – is to use AI as a tool for learning rather than a replacement for thinking. When AI produces a well-structured email or a clearly argued paragraph, the response that serves you best is not simply to send it, but to read it carefully and understand why it works. What did it do that you did not? What did it see that you missed? How is it constructed? In fact, current tools even provide the info: often you get sections such as "why this works better", allowing exactly this type of analysis.
Used that way, AI becomes something closer to the mentor the preprint describes: something that scaffolds your development rather than substituting for it. The output is still useful. But the process also makes you better.
That requires discipline, particularly when the alternative – sketch a few points, receive a polished result, move on – is frictionless. The temptation is real. I know this because I gave in to it on an ordinary Tuesday afternoon, writing a perfectly routine email.
The research suggests I was not alone. And that the consequences may be more significant than a single email implies. The question is no longer whether AI helps, but what it changes in how we approach thinking itself.
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