AI and critical thinking

Used to skip the thinking, AI weakens critical thinking. Used to pressure-test it, AI can strengthen it. That distinction is the whole answer. The evidence does not show that AI lowers intelligence. It shows that when people lean on a model to reason for them, they do the reasoning less often, trust the output more, and lose the habit of questioning it. Critical thinking does not vanish; it migrates, from producing an answer to verifying the machine's, and it thins as confidence in the tool rises. Whether your critical thinking declines is therefore not decided by ChatGPT. It is decided by whether you think before you ask, and whether you interrogate the reasoning rather than just accept the result. Most people do neither, because the tool makes it so easy not to.

What the evidence shows

The clearest workplace evidence comes from a 2025 study by Microsoft Research and Carnegie Mellon, which asked 319 knowledge workers about 936 real uses of AI in their jobs. It found that the more people trusted the tool, the less critical thinking they applied, and that the character of the thinking changes: from gathering information to verifying the machine's output, from solving the problem to integrating the answer, from doing the task to supervising it. The workers who kept thinking critically were those with the confidence and skill to inspect and correct the AI. The rest tended to accept what they were given.

A separate 2025 study by Michael Gerlich, across 666 people, found a significant negative correlation between frequent AI use and critical-thinking scores, with cognitive offloading as the mechanism in between, and the effect strongest among the youngest participants, who have leaned on these tools earliest and hardest. A 2025 experiment at the MIT Media Lab used EEG to compare people writing essays with a language model, with a search engine, or unaided, and found the AI group showed the weakest brain connectivity and the lowest sense of ownership over their own work, an effect the authors called cognitive debt.

None of this is new in kind. The mechanism underneath it, cognitive offloading, was mapped by Risko and Gilbert in 2016: we hand mental work to external tools to reduce effort, and, crucially, we decide to offload based on how hard a task feels, a judgement that is often wrong. And the precedent is older still. Sparrow and colleagues, in Science in 2011, showed the Google effect: when we expect information to stay available, we remember where to find it rather than the thing itself. What was true of facts is now becoming true of reasoning. The tool that holds the answer gradually holds the thinking too.

The honest reading of all of this is not that AI makes people stupid. It is that critical thinking is a practice, and AI is very good at letting us skip the practice while still producing the output. Skip it often enough and the capability quietly follows the effort out of the room.

Where the evidence remains uncertain

The direction of the risk is well supported; the size is not, and it matters to say so plainly. The Microsoft and Carnegie Mellon findings are self-reported: they capture how workers describe their own thinking, not a measured before-and-after. Gerlich's result is a correlation, which cannot on its own separate whether AI use erodes critical thinking or whether people who think differently simply use AI differently. The MIT study is striking but rests on 54 participants, is a preprint, and has been questioned by its own later commentators on sample size and reproducibility. Treat it as suggestive, not settled.

There is also a real counter-current. Used deliberately, AI can raise the quality of thinking: challenging a position, surfacing a counterargument, exposing a gap in an analysis. The same tool can be a crutch or a sparring partner. Almost no study yet measures the long-run difference between the two modes of use in real workplaces, which is exactly the question that matters most. The responsible conclusion is that the risk is real enough, and quiet enough, to be worth designing against now, rather than waiting for a decade of proof.

The SuperSkills view

Critical thinking is not being destroyed by AI. It is being relocated, and most people have not noticed the move. The work of thinking is shifting from generating an answer to checking one, and checking is the harder discipline. It requires you to hold an independent view against which to test the machine, and that view is precisely what you lose if you ask the machine first and think second. The danger is not that AI thinks for you. It is that AI removes the moment where you would have thought for yourself, and does it so smoothly that nothing feels lost.

This is why I put a single practical principle at the centre of it: on any judgement-heavy task, think first, then consult. Form an initial position before you open the model, so you keep an independent reference point to evaluate its answer against. It is a small habit with a large effect, because it preserves the one thing the evidence says is at risk: your capacity to notice when a confident output is wrong. The failure mode is not using AI. It is outsourcing the first move, the framing of the problem, which is where judgement actually lives.

The distinction between a crutch and an amplifier is what SuperSkills calls the Augmented Mindset: working with AI so it extends your capability rather than replacing it. A person with a strong augmented mindset uses AI to attack their own reasoning, not to avoid it, and can always say where the tool helped, where it misled, and where their own judgement overrode it. That is critical thinking with AI in the room, rather than critical thinking handed to it. And it connects directly to the wider account of judgement and to the missed reps: every time the tool does the reasoning, the person misses the repetition that would have built the capability to do it themselves.

What to do about it

For yourself: think before you ask. On anything that requires judgement, write your own position first, even a rough one, then use AI to test and extend it rather than to produce it. Interrogate the reasoning, not just the result, because a fluent answer can be confidently wrong and the fluency is what disarms you. Work unaided sometimes, deliberately, to keep the capability exercised, the way a musician still practises scales. And treat cognitive convenience as a cost as well as a benefit: the easier it is to skip the thinking, the more it is worth asking whether you should.

For managers and teams: make reasoning visible. Ask people to show their thinking, not only their output, because output quality has stopped being a reliable signal of whether the person can think. Keep some work AI-free on purpose, especially for people still building their judgement. Reward the person who can explain and defend a recommendation over the one who simply produced a polished one. And treat verification as real, skilled work rather than a rubber stamp, because in an AI-assisted team the checking is the thinking.

The market already values this. The World Economic Forum's 2025 Future of Jobs report names analytical thinking as the single most sought-after core skill among employers. As AI makes fluent output cheap, the ability to question it becomes the scarce and valuable thing. Critical thinking is not being made obsolete by AI. It is being made more valuable, and more fragile, at the same time.

Key research and primary sources

Go to the study rather than the article reporting it.

• Lee, H.-P. et al. (2025). The Impact of Generative AI on Critical Thinking. Microsoft Research and Carnegie Mellon, CHI 2025.
• Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6.
• Kosmyna, N. et al. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt. MIT Media Lab preprint.
• Risko, E. F. and Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9).
• Sparrow, B., Liu, J. and Wegner, D. M. (2011). Google Effects on Memory. Science, 333(6043).
• World Economic Forum (2025). The Future of Jobs Report 2025.

Related SuperSkills research

This page sits within a wider body of work: AI and human judgement, the Augmented Mindset, the missed reps, decision quality in the AI era and drift versus design.

About this research

Rahim Hirji is the author of SuperSkills: The Seven Human Skills for the Age of AI (Kogan Page, 2026) and the founder of The SuperSkills Intelligence Company. This work draws on research across more than 200 organisations in 30 countries over seven years. Findings on this page are attributed to the studies that produced them, and kept separate from the interpretation, which is the author's. It is a living reference, reviewed and updated as significant new evidence appears.