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What the knowledge-worker study found
The most-cited study here is a peer-reviewed paper by Hao-Ping (Hank) Lee and colleagues, presented at CHI 2025. It surveyed 319 knowledge workers and collected 936 first-hand examples of generative AI use at work.
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- Trust in AI: higher confidence in GenAI was associated with less reported critical-thinking effort.
- Trust in yourself: higher confidence in one’s own ability on the task was associated with more reported critical thinking.
- A shift, not only a loss: the authors write that “GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship.” Workers described checking AI output, merging it with other material, and taking responsibility for the final result.
Three limits matter. The data is self-reported. It covers working adults, not students. And it shows associations, not cause. It measures how much effort people say they apply, so it does not show lasting cognitive decline.
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Better output is not the same as learning
The OECD’s Digital Education Outlook 2026 draws the sharpest line. It says general-purpose generative AI can boost performance on a task without producing learning gains. It names the risks of cognitive offloading as disengagement and “metacognitive laziness.” It also notes that some advantages in output shrink or reverse when learners sit exams without AI access.
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The practical meaning: a polished essay or working solution produced with AI proves the task got done. It does not prove you can do it yourself. The OECD contrasts this with educational AI built or used for a pedagogical purpose, which in the evidence it reviews tends to show sustained learning improvements. It describes this research as emerging, not as a settled universal effect.
When AI helps and when it hurts
The OECD, Microsoft Research and UNESCO guidance points to six questions that separate useful use from harmful use. They are a synthesis of those sources, not a tested ranking of products.
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| Question | Lower risk | Higher risk |
|---|---|---|
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| Did you reason before seeing an answer? | You attempted the problem first | AI is the first move |
| What does the tool give you? | Prompts to verify, explain and reflect | A finished response to copy |
| Do you already know the basics? | Yes, so you can judge the output | No, so you cannot tell good from bad |
| Is there unaided recall afterwards? | You apply or recall without AI | Nothing tests what you kept |
| Is a teacher involved? | Teacher oversight and privacy protections | Unsupervised, with no data safeguards |
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These steps apply the cited guidance. No study has proven that they prevent long-term skill loss.
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- Ask for critique, not completion. Request counterarguments, gaps in your reasoning, or feedback on your draft instead of a finished version.
- Verify claims. Check important facts against reliable sources. This is the “information verification” the CHI paper describes.
- Explain it in your own words. If you cannot restate the reasoning without looking, you have not learned it yet.
- Practice without AI sometimes. Do some problems unaided, so you know what you can do alone.
- Watch your confidence. The survey link between trusting AI and thinking less is a cue to be more skeptical when the output feels convincing.
What teachers and schools are advised to do
The OECD recommends that learning aim at foundational knowledge and independent thinking “without GenAI, with educational GenAI, and then with general-purpose GenAI.” It adds that AI should be used “selectively and purposefully for pedagogical reasons to enrich learning and not replace cognitive effort or weaken the human relationships at the heart of education.” It also urges investment in tools grounded in learning science and effectiveness research.
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Walker and Vorvoreanu’s October 2025 Microsoft Research review of classroom evidence makes four recommendations:
- Ensure learners are ready before AI is introduced.
- Teach AI literacy so learners can assess outputs.
- Use AI to supplement traditional learning and keep teacher guidance.
- Use engagement interventions: limit copy-paste, improve metacognitive calibration (how accurately learners judge their own understanding), and nudge learners toward critical thinking.
The review also notes that outcomes vary across learner groups and contexts. UNESCO’s guidance, published 7 September 2023 and updated on its official page on 16 January 2026, calls for a human-centered, age-appropriate approach with attention to data privacy, ethical validation and pedagogical design. Those are policy recommendations, not evidence of a particular learning outcome.
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What teachers say
The OECD cites TALIS 2024 figures for lower secondary teachers only. They are not rates for all teachers or current adoption.
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- 37% had used AI for their job in 2024.
- 57% agreed AI helps write or improve lesson plans.
- 72% believed AI can harm academic integrity by letting students pass off work as their own.
What remains unknown
Long-term causal effects on critical-thinking ability are unresolved. The evidence does not yet separate effects by age, subject or pattern of use, and the main worker survey is not a longitudinal study of students. Claims that AI “destroys” critical thinking, or that research “proves” students are losing it, go further than the evidence. The defensible version is that when AI supplies the answer, people may do less of the work that builds judgment, and the risk depends on how it is used.
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