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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Sometimes—but the evidence points to a risk, not an inevitable outcome. Generative AI can take effort out of information and reasoning tasks. In one controlled student study, that ease coincided with weaker reasoning in a specific research exercise. The concern is not that every AI-assisted task harms thinking; it is that accepting a fluent answer can replace the practice of checking, explaining, and deciding for yourself.
What the studies actually found
A short scientific inquiry with university students
A 2024 randomized study assigned 91 university students to investigate whether nanoparticles in sunscreen are harmful, using either ChatGPT 3.5 or Google. Students using ChatGPT reported lower cognitive load, but their final recommendations showed lower-quality reasoning and argumentation than those produced by the Google group. The groups did not differ significantly in perspective diversity. The study, published in Computers in Human Behavior, measured performance on one short task—not lasting skill or intelligence. It used ChatGPT 3.5 in 2023 and a German university sample, so it cannot establish what happens with every tool, age group, subject, or pattern of use.
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Knowledge workers’ reported effort
A 2025 CHI study surveyed 319 knowledge workers who used generative AI at work at least weekly. Participants provided 936 examples of use. In those examples, respondents reported “less” or “much less” effort in Knowledge (72%), Comprehension (79%), Application (69%), Analysis (72%), Synthesis (76%), and Evaluation (55%). These figures describe reported effort categories, not measured declines in ability.
The survey found an association between greater confidence in GenAI and less reported critical-thinking effort; greater task-specific self-confidence was associated with more reported critical-thinking effort. That is an association in respondents’ accounts, not proof that confidence causes someone to think less. Participants also described verifying and integrating AI responses and overseeing the work. The authors note that people may conflate doing less general work with doing less critical thinking. The CHI 2025 paper relies on self-reports and does not show that AI caused lasting deterioration in critical-thinking skill.
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Less effort is not automatically less thinking
Effort, engagement, and ability are related but distinct. AI might make a task easier while a person still reasons carefully; it might take over a part of the reasoning; or it might reduce critical engagement. A user can also perceive a task as requiring less critical thinking without actually losing the ability to think critically. Neither a report of lower effort nor a faster result, by itself, tells us whether durable ability has changed.
The student experiment is useful because it measured the quality of a defined task’s final reasoning, not just how effortful the task felt. But even that finding has a narrow scope: it shows a difference in one research-and-recommendation exercise, not that AI inevitably makes users less intelligent or permanently damages their thinking.
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Where the thinking goes when AI helps
AI can shift effort rather than simply remove it. The CHI paper describes three changes in work:
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- From finding information to verifying it. A generated answer may assemble claims quickly, but the user still needs to check them against reliable sources and relevant expertise.
- From solving a problem to integrating a response. The person must decide whether the output fits the real question, audience, and constraints—not merely whether it sounds plausible.
- From executing a task to stewarding the system. The user guides and monitors the tool and remains responsible for the result.
Those shifts can be useful when AI handles routine or clerical work and leaves the important judgment to a person. They become risky when the answer is accepted without checking, when the user cannot explain why it is appropriate, or when the task’s purpose is to learn the reasoning itself.
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How to use AI without handing over the learning
For study and skill-building
- Make an initial attempt. Write down your answer, approach, or current understanding before asking AI to solve the task.
- Ask for critique, not just a replacement answer. Have the tool identify assumptions, gaps, counterarguments, or steps you should check.
- Explain the reasoning yourself. Put the answer into your own words and be able to justify its key steps without leaning on the generated explanation.
- Check important claims. Follow up against course materials or dependable sources, especially when accuracy matters.
This sequence is a practical recommendation, not an intervention proven by the studies above. Its aim is to keep the learner doing the explanation and evaluation that the task is meant to develop.
For work and decisions
- Use AI to reduce search or drafting burden, but verify consequential facts against reliable sources.
- Judge whether the response fits the specific task, audience, and constraints before incorporating it.
- Keep a person accountable for the decision and the final output; a plausible response is not a substitute for oversight.
When is it reasonable to let AI do more?
The answer depends on what success means. If the goal is to complete a routine task efficiently, shifting some work to AI may be acceptable, provided the result is checked as needed. If the goal is to learn, practice a foundational skill, or make a consequential judgment, outsourcing the first explanation or accepting the output as final can remove precisely the work that builds understanding.
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A useful test is: Could I explain, check, and defend this result without treating the AI’s fluency as evidence? If not, use the response as a starting point rather than a conclusion. The evidence does not establish that ordinary AI use causes lasting cognitive decline; it does show why preserving active verification and reasoning matters when those are the goals.
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