A few months ago I caught myself doing something odd. I had used AI to draft a page for one of my sites, published it, and a week later I could not have explained the argument in it without opening the file. The work was done. The thinking, apparently, had not stuck.

That feeling has a name now, and researchers have started measuring it. The question of AI and critical thinking is no longer just a worry people raise at dinner. Three studies, two of them peer reviewed, have looked at what happens inside the heads of people who lean on AI, and the results are worth knowing before you build your next work habit around a chatbot.

The short version: the risk is real, it is not automatic, and the difference comes down to how you use the tool rather than whether you use it.

What the research says about AI and critical thinking

The most useful study of working adults came from Microsoft Research and Carnegie Mellon University, presented at CHI 2025. The team surveyed 319 knowledge workers and collected 936 first-hand examples of generative AI used in real work tasks.

The headline finding is a confidence effect, and it cuts both ways. People with higher confidence in the AI did less critical thinking. People with higher confidence in their own ability at the task did more. Same tool, opposite outcomes, depending on who was holding it.

The researchers also found that the thinking does not disappear so much as move. It shifts away from producing an answer and toward verifying information, stitching AI output together, and managing the task overall. That is still real cognitive work. It is just different work, and it only happens if you choose to do it. You can read the full paper on the Microsoft Research site.

The MIT essay study, and what it does not prove

The study that got the loudest headlines came from the MIT Media Lab. Researchers put EEG caps on participants and asked them to write essays under three conditions: using an LLM, using a search engine, or using nothing at all.

Brain connectivity was strongest in the no-tools group, moderate in the search group, and weakest in the LLM group. The LLM writers also reported the lowest sense of ownership over their essays, and struggled to accurately quote work they had produced minutes earlier. The authors called the pattern “cognitive debt”. The full paper is on arXiv.

Now the honest part, because this study has been badly oversold online. It involved 54 participants across the first three sessions and only 18 in the fourth, and it looked at one narrow task. It is still a preprint. The authors revised it in December 2025, but it has not been through peer review. That is a signal worth taking seriously, not a verdict on your brain.

The order you work in seems to matter

Buried in the same MIT study is the most practical finding of the lot, and almost nobody reported it.

In the final session the researchers swapped the groups around. People who had been writing unaided and were then given an LLM showed higher memory recall and stronger brain activation, closer to the search-engine group. People who had been leaning on the LLM and then had it taken away showed reduced connectivity and looked under-engaged.

Read that again, because it is the whole practical lesson. Thinking first and bringing AI in second looked healthy. Starting with AI and trying to think afterwards did not.

The 2026 study that names the difference

The newest piece of this puzzle is also the most useful, and it arrived in July 2026 in the peer-reviewed journal Frontiers in Psychology. Researchers surveyed 589 university students and early-career knowledge workers across three rounds spaced out over time, and they split AI use into two modes instead of treating it as one thing.

Dependent offloading is handing over the actual thinking: accepting the output with little examination, letting the AI structure your ideas, treating what it produces as the finished article. Autonomous offloading is using the same tool as a scaffold: taking the output as a starting point, comparing it against your own reasoning, and keeping ownership of the final result.

The two modes pulled in opposite directions. Dependent use was linked to handing over cognitive agency and to lower motivation, and through those to weaker self-rated judgement, creativity and depth of thinking. Autonomous use was linked to higher motivation and better self-rated outcomes. Same tools, same tasks, opposite results based only on how people engaged.

Then comes the finding that should change how you work. Both modes produced comparable immediate benefits. The session feels equally productive either way, which means unhelpful AI use is very hard to spot from experience alone. Nothing in the moment tells you which mode you are in. You can read the full study on the Frontiers in Psychology site.

Keep it in perspective. It asks people to rate their own thinking rather than testing it, so it shows associations, not proof of cause. But it is peer reviewed, far larger than the MIT study, and it names the distinction the other two keep circling.

Five habits that protect your thinking

• Write your rough version first. Even five messy bullet points before you open the chat window changes the whole session. You arrive with a position instead of asking to be given one.
• Ask the AI to question you rather than answer you. Try “argue against this” or “what am I missing here” instead of “write this for me”. You get pushback rather than a finished product you never examined.
• Verify anything you would be embarrassed to get wrong. Names, numbers, dates, citations, legal or medical claims. AI systems produce confident wrong answers regularly, which is why our guide to AI hallucinations is one of the most useful things to read before you trust an output.
• Explain the result out loud in your own words. If you cannot, you have not understood it, and you will not be able to defend it in a meeting or a viva.
• Notice when a task feels low-stakes. The Microsoft researchers found that people review AI output far less carefully when they judge a task to be unimportant. Low-stakes tasks are exactly where sloppy errors slip through into public work.

Important tip: if you cannot explain the answer to another person in your own words, you have not learned it. You have only borrowed it.

When letting AI do the work is completely fine

None of this means you should feel guilty every time you open a chatbot. Offloading is only a problem when you are offloading something you actually needed to learn.

Reformatting a messy list, fixing spelling, converting a table, writing boilerplate you have written a hundred times, summarising a document you are only skimming for one fact. Hand all of that over without a second thought. From my own experience running websites and online tools, that category is where AI saves genuine hours, and none of those hours were making me smarter.

The line is simple. If the task is the learning, do it yourself first. If the task is friction around the learning, automate it. That distinction is also why our post on using AI tools without cheating keeps coming back to the same test.

What this means for students and researchers

If you are studying, the stakes are higher, because the entire point of the work is to build something in your head that stays there. A summary you did not read leaves nothing behind. Our plan for using AI to study for exams is built around this: generate questions, not answers.

Microsoft Research has since argued for designing AI as a “tool for thought” rather than an assistant that hands you finished work, warning that people risk becoming validators of machine output rather than authors of their own. You can read their argument here. Until the tools are built that way, the burden of using them well sits with you.

Common Questions

Does using AI actually make you less intelligent?

No study has shown that. What the research shows is reduced mental effort during AI-assisted tasks and weaker recall of AI-assisted work. Those are measurable short-term effects on specific tasks, not evidence of permanent change.

Is it better to just avoid AI for serious work?

Avoiding it entirely costs you speed and puts you behind on a skill employers now ask for. The evidence points toward using it deliberately rather than avoiding it, which is also the theme of our guide to the AI skills that matter most for future jobs.

What is cognitive offloading?

It means using something outside your head to do mental work for you. A shopping list, a calculator and a satnav all count. AI is a far broader version of the same thing, which is why where you draw the line matters more.

Final takeaway

AI and critical thinking are not enemies. The research suggests they come apart only when you hand over the part of the task that was supposed to change you. Think first, use the tool second, verify what matters, and be able to explain the result without the screen in front of you. Do that and the tool stays a tool.