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What does over-reliance on AI mean?
Over-reliance is not simply using an AI tool often. In a 2024 systematic review of AI dialogue systems in education and research, Chunpeng Zhai, Santoso Wibowo, and Lily D. Li describe it as accepting AI-generated recommendations without adequately assessing their reliability or deciding how much trust they deserve. The issue is uncritical acceptance: treating a fluent answer as a sound one without doing the evaluation the task requires.
Why cognitive offloading matters
Cognitive offloading means delegating mental work to an external aid instead of doing it yourself. A calculator can handle arithmetic; a chatbot can draft an explanation, propose an argument, or summarize material. Offloading can be useful, but if the tool performs the steps where you would otherwise practice weighing evidence, identifying assumptions, or forming a conclusion, you may get the result without building the underlying skill.
That distinction helps explain why speed and understanding are not the same. A polished response can make a task feel easy while leaving you unable to explain why the answer is credible or what evidence might change it.
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What studies say about AI and critical thinking
AI use and critical-thinking performance
A 2025 mixed-method study by Michael Gerlich in Social Sciences surveyed 666 people across age groups and educational backgrounds and also used interviews. It found a significant negative correlation between frequent AI-tool use and critical-thinking ability, with cognitive offloading mediating the relationship. Younger participants showed greater dependence and lower critical-thinking scores; higher educational attainment was associated with stronger critical-thinking skills regardless of AI use.
This is an association from one study, not proof that AI caused a lasting decline. The findings support concern about how people use the tools, but they do not show that every frequent user loses skill or that the relationship applies equally across tasks and users.
Student inquiry and effort
A 2024 study in Computers in Human Behavior, titled “Cognitive ease at a cost,” reported that using large language models reduced mental effort while compromising the depth of students’ scientific inquiry compared with traditional search. The distinction matters: an answer delivered with less effort may be convenient without providing the same depth of investigation.
In workplace settings, the Microsoft Research paper The Impact of Generative AI on Critical Thinking surveyed knowledge workers who used generative AI. Participants reported reduced cognitive effort on some tasks. The authors warn that efficiency may inhibit critical engagement and contribute to potential over-reliance, but say longitudinal research is needed to determine longer-term effects.
What educators and teachers expect
These survey figures capture views and expectations, not measured long-term changes in students’ ability:
| Source and group | Finding | What the figure measures |
|---|---|---|
| AAC&U and Elon University’s Imagining the Digital Future Center, November survey of 1,057 faculty | 95% expected generative AI to increase student over-reliance; 75% expected “a lot” of impact. | Faculty expectations about over-reliance. |
| AAC&U and Elon University’s Imagining the Digital Future Center, November survey of 1,057 faculty | 90% expected generative AI to diminish students’ critical-thinking skills; 66% expected “a lot” of impact. | Faculty expectations about critical-thinking skills. |
| Pew Research Center, 2024, U.S. public K–12 teachers | 25% said AI tools do more harm than good, 32% said the benefits and harms are equal, and 6% said AI does more good than harm. | Teachers’ assessments of AI’s overall effects. |
| Oxford University Press AI Survey, lecturers | 46% were concerned students using AI would fail to develop core skills such as critical thinking. | Lecturers’ concerns about skill development. |
What the evidence does not establish
The available evidence does not demonstrate a universal or permanent loss of critical-thinking ability caused by AI. Much of it is correlational, self-reported, review-based, or focused on what educators expect. Establishing lasting effects would require longitudinal measurement and objective performance tests across repeated real-world use, while accounting for differences in task difficulty, tool type, user expertise, and instructional context.
Does AI make students less thoughtful?
It can encourage shallow work when students use a chatbot to supply the explanation or conclusion and skip the reasoning needed to understand it. That is a problem with the learning process, not evidence that students who use AI are inherently lazy. The relevant question is whether the assignment still asks them to practice analysis, evaluate sources, and explain their own judgment.
The OECD’s Digital Education Outlook 2026 presents both sides: offloading cognitive tasks to general-purpose chatbots risks metacognitive laziness and disengagement that may hinder skill acquisition, while pedagogically guided use can strengthen argumentation, critical thinking, creativity, and collaboration. In other words, the design of the task and the role given to AI matter.
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Unrestricted delegation versus guided AI use
| Question | Unrestricted delegation | Guided use |
|---|---|---|
| Who does the reasoning? | The tool supplies the explanation or conclusion; the user may accept it without reconstructing the argument. | The user forms a view and uses AI for bounded support, such as surfacing counterarguments or assumptions. |
| How are claims checked? | Claims may pass into the final work without independent verification. | Important claims are checked against primary sources before they are relied on. |
| What is prioritized? | Speed and a finished response can take precedence over depth of inquiry. | The user compares evidence and explanations, using AI as one input rather than a substitute for inquiry. |
| Can the user explain the result? | The user may struggle to defend the conclusion without the tool’s response. | The user must be able to explain the conclusion and how the evidence supports it. |
| Does the task preserve practice? | Core judgment and argumentation may be handed off. | The user retains responsibility for evaluating evidence and making the final judgment. |
How to use AI without handing over your thinking
- Make a first attempt. Before asking for an answer, write a provisional conclusion, outline, or list of questions. This gives you a baseline to compare with the tool’s response.
- Use AI to challenge your view. Ask it to identify assumptions, give competing explanations, raise counterarguments, or point out missing evidence. Treat these as leads to assess, not as verified facts.
- Verify important claims independently. Follow key claims to primary sources and check whether those sources support what the AI said. Fluency and confidence are not evidence.
- Keep the tool’s contribution distinguishable. In notes or drafts, separate generated suggestions from your own reasoning. This makes it easier to see what you have evaluated and what you have merely received.
- Explain the result without the chatbot. If you cannot state the conclusion and defend it in your own words, return to the evidence before relying on it.
- Keep consequential decisions with a person. Use AI as assistance, not the final authority, when a decision has meaningful consequences.
For teachers and people designing workflows
Build tasks that reward the thinking process, not only a polished answer. Ask learners or staff to explain their reasoning, evaluate sources, compare alternatives, and describe how they revised a conclusion. Those requirements make the human contribution visible and preserve practice in judgment and argumentation.
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