AI can help you produce better work on some tasks, but that is not the same as making you better at doing those tasks unaided. Current studies show task-specific benefits as well as signs of cognitive offloading; they do not establish that AI broadly improves intelligence or inevitably weakens independent thinking. To keep your skills in view, try the problem yourself first, use AI for guidance rather than automatic answers, and check whether you can explain or repeat the work without it.
Does AI make people smarter, or more dependent?
There is no evidence-based yes-or-no verdict. Generative AI can improve performance on particular tasks, and relying on it can shift some thinking from the person to the tool. Those outcomes can coexist. The key distinction is between what you accomplish with AI open and what you can understand, remember, or do independently later.
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A 2024 perspective in Nature Human Behaviour describes generative AI as having potential to change human learning while raising concerns such as model imperfections and ethical dilemmas. Its authors call for rigorous research across learning contexts to evaluate effects on cognition, metacognition, and creativity. That caution matters: a polished answer or faster task completion is not, by itself, evidence of durable learning.
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Academic achievement: associations in one specific survey
A 2025 Scientific Reports study collected two waves of survey responses in March–May 2024 from 465 preservice teachers at five universities in Wuhan, China. Some measures of generative AI use were positively associated with academic achievement, while other tested measures did not show significant positive relationships. The study’s statistical model also linked shared metacognition and cognitive offloading with achievement.
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These are associations from a survey of a narrowly defined group, not proof that AI caused better learning or that the findings apply to students generally, other countries, or workplaces. A statistical mediation model can describe relationships among measured variables; it does not establish a causal mechanism.
Confidence and offloading: a descriptive finding, not a diagnosis
In a 2025 nonclinical mixed-methods study published by the American Psychological Association, 58% ± 7% of participants agreed that “AI did most of the thinking.” The study also reported an association between greater prompt dependence and lower override frequency, on one hand, and lower self-reported confidence in independent reasoning, on the other (r = −.61, p < .01). The author characterizes the findings as descriptive; they do not show that AI caused reduced confidence or executive function. The result describes that study’s participants, not the prevalence of dependence among all AI users. Read the APA-published study.
Creativity: better generated ideas on tested tasks
Five experiments using GPT-3.5 found more creative generated ideas with ChatGPT than with no technology or conventional web search for the everyday and innovation-related idea-generation tasks tested. The 2024 Nature Human Behaviour paper supports a task-specific conclusion about the ideas produced in those experiments—not a claim that people became more creative over time or improved at creating independently.
Feedback and learning: a developing evidence base
A 2025 npj Science of Learning study examined different AI-chatbot feedback, learning outcomes, and brain activity. Those topics are relevant to whether feedback can support learning, but the available study information does not establish which feedback condition worked best. It would be premature to treat a particular AI feedback style as a proven learning method on this basis.
How to use AI without handing over the thinking
These are practical safeguards, not routines proven to prevent cognitive decline. Use them to make your own understanding easier to check, and adjust them to the task.
- Make an unaided first attempt. Before asking for a solution, write down what you think, outline an approach, or try the first step. This gives you a point of comparison for the AI’s response.
- Ask for scaffolding before a finished answer. Request a hint, a counterargument, an example, or a critique of your draft. For example:
Give me one hint at a time. Do not solve the problem unless I ask.If you need a complete answer for a legitimate task, ask the AI to show its reasoning or assumptions so you can inspect them; do not mistake a fluent explanation for proof that it is correct. - Verify claims that matter. Check factual statements against authoritative sources, especially when a mistake could affect a decision. AI systems can produce imperfect answers, a concern highlighted in the 2024 learning perspective. Treat an answer as checked only after you have checked it.
- Close the tool and test yourself. Explain the idea from memory, reproduce the method, or solve a similar problem without AI. If you cannot, you may have a useful output without yet having independent command of the material.
- Keep responsibility for the conclusion. Decide whether the response fits your evidence, goals, and context. For consequential choices, do not let an AI answer substitute for the appropriate expert or authoritative source.
Choose the workflow to fit the task
AI does not need to play the same role in every activity. The useful question is whether the task calls for practice, idea generation, or routine production—and what you need to be able to do afterward.
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| Task | Use AI for | Keep for yourself | Check afterward |
|---|---|---|---|
| Learning or practice | Hints, worked examples, questions, and feedback on your attempt | The first attempt and the final explanation in your own words | Can you recall the concept and solve a similar problem unaided? |
| Brainstorming | Alternative angles, variations, or prompts that widen the idea pool | Choosing, combining, and developing ideas for your purpose | Can you explain why the selected idea fits, and improve it yourself? |
| Routine production | A draft, summary, or transformation of material you provide | Setting requirements, checking facts, and approving the finished work | Does the result meet the brief, and can you defend important claims? |
This is a decision aid, not a ranking of proven methods. Research does not yet establish one universally best workflow across tasks, populations, and time horizons.
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- Whether everyday AI use improves or harms independent thinking over months or years across different populations.
- A universal amount of AI use, or a specific routine, that protects thinking skills.
- That better AI-assisted output necessarily means better learning, memory, reasoning, or creativity without the tool.
- That cognitive offloading is always harmful. Delegating routine work may be useful; the important question is whether you are also practicing skills you need to retain.
The evidence points to a more practical standard than “use AI” or “avoid AI”: decide what you want to learn or retain, choose how much help the task needs, and test your own understanding after the tool is gone.
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