There is no good evidence that AI has caused a population-wide decline in people’s ability to think. The more immediate concern is narrower: when people routinely hand over the reasoning, checking, and judgment a task is meant to exercise, they may get less practice doing those things themselves. AI can also support thinking—by offering examples, feedback, or counterarguments—when the person stays responsible for evaluating the result.
Is AI making us less able to think for ourselves?
That is a reasonable risk to watch, but it is not an established outcome. Current studies do not show that AI has caused lasting, general cognitive decline. They examine narrower questions: what workers say AI changes about their effort, how people describe different kinds of offloading, and how students’ AI use relates to their own assessments of creativity.
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The distinction matters. A polished answer or faster completed task shows that a tool helped produce an output; it does not show that the person learned the underlying skill or could perform the task independently later. Conversely, using an external aid does not automatically mean someone has stopped thinking. The important question is what the user delegates—and what they still practice and decide.
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Knowledge workers reported perceived changes, not measured decline
A 2025 CHI study by researchers at Microsoft Research and Carnegie Mellon University surveyed 319 knowledge workers and collected 936 first-hand examples of generative AI use. Participants described how they perceived critical thinking and effort in their work. Those figures describe the study sample and examples, not the prevalence of AI-related cognitive decline among workers. The survey did not establish that participants’ ability had declined or assess long-term effects. Read the study overview and the CHI paper.
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Frequency is not the same as dependence
A 2026 three-wave, time-lagged study of 589 participants distinguished autonomous AI use from dependent cognitive offloading—the transfer of core cognitive work to AI. It reported different associations with participants’ subjective appraisals of downstream cognitive functioning. The study did not measure demonstrated ability or prove that AI use caused those appraisals; its authors also call for replication across populations and tasks. In other words, the number of times someone uses AI alone does not reveal whether they are delegating routine support or surrendering the central judgment. See the study of dependent and autonomous cognitive offloading.
Perceived creativity can rise even as dependence presents a concern
A 2026 cross-sectional survey of 936 undergraduates at six universities in China found that greater AI-use intensity was positively associated with students’ perceived academic creativity. It also found a negative indirect association through cognitive dependence. These results concern self-reported perceptions, not scores on an objective creativity test; a cross-sectional survey cannot establish which factor came first or whether one caused another. The findings therefore point to a possible tension, not proof that AI either improves or damages students’ creative ability. Read the study.
AI literacy is promising, not a proven safeguard
In that same student survey, AI literacy was associated with less cognitive dependence and more perceived academic creativity. The authors recommend critical evaluation, source verification, and keeping responsibility for reasoning. These are sensible practices consistent with the findings, but the associations do not prove that teaching them will prevent dependence or preserve cognition in every setting. The study’s findings and recommendations should be read with its cross-sectional design in mind.
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When does AI help you think, and when does it replace thinking?
A useful way to judge an AI interaction is to ask who is doing the cognitive work. AI is more likely to act as a scaffold when it gives you material to consider and you set the goal, test the claims, and choose what to do. It is closer to substitution when it performs the central reasoning and you accept the result without being able to explain or check it.
| Question | Scaffolding | Substitution |
|---|---|---|
| Who sets the goal? | You define the problem and the standard for a good answer. | The prompt and generated response effectively determine what counts as an answer. |
| What does AI contribute? | Examples, feedback, alternative explanations, or objections you can assess. | The core analysis or decision you were meant to make. |
| What happens to claims? | You check important facts against reliable sources. | You treat a fluent response as sufficient evidence. |
| What can you do afterward? | You can explain the reasoning and, where needed, attempt the task without AI. | You have a finished output but cannot independently reconstruct or evaluate its reasoning. |
This is a practical distinction, not a validated diagnostic test. In particular, a better immediate output is not evidence by itself that the user has learned more.
How to use AI without handing over your judgment
- Try the core step first when practice matters. Write your own outline, solve the first problem, or state your initial view before asking AI. That gives you a real attempt to compare with the tool’s contribution.
- Ask for material to evaluate, not just a finished answer. Request examples, competing explanations, assumptions, or counterarguments. You remain responsible for deciding which are sound.
- Check consequential claims. Follow important factual assertions to reliable sources rather than treating confident wording as proof.
- Explain the result in your own words. If you cannot describe why the answer makes sense, identify what you do not understand before relying on it.
- Keep some practice independent. When building or retaining a skill matters, do at least some of the relevant work without AI. The evidence supports preserving opportunities for practice as a reasonable approach, not as a guarantee against cognitive effects.
A specific 2026 Microsoft Research experiment tested an “assumption-analysis” cognitive forcing function in an AI-assisted writing task. The intervention reduced overreliance in that experiment without increasing cognitive load, and participants found a what-if prompt helpful. That is a useful design idea for a particular task—not proof that one prompt or technique prevents overreliance across all activities. Read Microsoft Research’s experiment summary.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why lost practice is a more grounded concern than “AI makes people stupid”
The risk is not that every automated task damages a person’s mind. It is that a tool can make it easy to skip the repeated attempts through which people practice judgment—and then leave them less prepared when they need to handle an unfamiliar or consequential case. The CHI paper reproduces a warning attributed to Bainbridge about automation removing routine opportunities to practice judgment while leaving people to deal with exceptions. That is an argument about a possible design risk, not a finding that the survey proved cognitive atrophy. The paper discusses the automation concern.
For individuals, the practical test is whether using AI leaves them able to question, explain, and—when the skill matters—perform the work themselves. For organizations and educators, it is whether systems and assignments preserve meaningful human responsibility rather than rewarding only speed or a polished deliverable. Neither test can be reduced to how often AI is used.
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