You ask an assistant to explain a problem you have never studied. It answers in twelve neat paragraphs. The definitions sound precise, the examples make sense, and the conclusion is reassuring. Ten minutes earlier, you knew very little about the topic. Now you feel ready to explain it to someone else.
Then that person asks one unexpected question. Which part of the explanation was supported by an experiment? Which part was an assumption? Would the conclusion change under different circumstances? You discover that you have been following the flow of an explanation without building a reliable map of the reasoning inside it.
This is a hypothetical example, but it illustrates a practical problem in how we judge our own understanding. An answer can feel clear because the writing is clear. We may mistake the comfort of reading something coherent for the harder accomplishment of being able to use the idea independently. The difference matters whenever we rely on artificial intelligence to learn, make decisions, or discuss an unfamiliar subject.
The Psychology of Knowing What You Know
Psychologists call the ability to monitor and evaluate our own thinking metacognition. It includes the judgment that we have understood something, the recognition that we need more evidence, and the decision to check an answer before acting on it. Metacognition is imperfect. A person can feel confident without having the knowledge required to answer the next question.
This problem existed long before generative AI. An appealing textbook explanation can give readers an unwarranted sense of mastery. A colleague's persuasive presentation can smooth over a missing assumption. What changes with modern AI assistants is the speed and ease with which a complete-looking account becomes available. We can move from uncertainty to apparent certainty before we have tried to work through the problem ourselves.
In their review of cognitive offloading, psychologists Evan Risko and Sam Gilbert describe how people use external resources, from reminders to physical actions, to reduce mental demands. They also discuss how people's evaluations of their own abilities influence decisions about what to offload. Those evaluations can be mistaken. Their research does not establish that using an external aid necessarily damages thinking. It gives us a useful framework for examining whether the aid changes our judgment of what we can do alone. The review is available at https://doi.org/10.1016/j.tics.2016.07.002.
Think about a person who relies on a navigation app. The map can be excellent, and the journey can go smoothly. But successful arrival does not prove that the traveler could describe the route afterward. That may be perfectly acceptable. Remembering every turn often has little value. The danger appears when the traveler needs to recognize a serious mistake and has no independent understanding of the surrounding roads.
Not every mental task deserves to remain in our heads. We should be selective about what we delegate. The underlying ability to ask whether an answer makes sense deserves special protection because it determines whether many other acts of delegation succeed.
What A Study of AI Tutoring Reveals?
A 2025 experiment published in the Proceedings of the National Academy of Sciences helps distinguish immediate performance from later learning. Researchers studied nearly a thousand high school mathematics students. Students who used an unrestricted AI tutor performed better during practice, but they scored worse than students without that tool on a later exam when AI assistance was removed. A version of the tutor designed to give hints and guide reasoning largely avoided the negative effect on later unaided performance.
The researchers examined a specific mathematics-learning setting. They did not show that AI will weaken everyone's thinking across all subjects. Their results nevertheless provide a serious warning about how we measure success: completing practice problems with assistance can be easier even when independent mastery has not improved. The paper is at https://doi.org/10.1073/pnas.2422633122.
The design of the help mattered. A tutor that invites learners to make the next move may preserve opportunities to practice reasoning. A tool that supplies the solution immediately can remove that opportunity. This suggests that we should ask more than whether an assistant produces correct information. We should also ask whether the interaction helps the person learn the mental moves necessary to assess that information later.
Why Speed Can Hide The Gap?
Rapid assistance has real benefits. In a randomized experiment published in Science, Shakked Noy and Whitney Zhang tested an AI writing assistant with 453 college-educated professionals. For the writing tasks studied, the average time needed fell by 40 percent and output quality rose by 18 percent. These are substantial task-level gains. The experiment did not test whether participants became better at independently recognizing flawed reasoning after repeated use. See https://doi.org/10.1126/science.adh2586.
Those findings can coexist. A tool may help us produce better work while leaving some of the underlying skills underdeveloped. The proper response is to decide which abilities we need to retain. A professional preparing a routine memo may have little reason to practice the mechanics of formatting it. The same professional has a strong reason to preserve the ability to notice that a crucial piece of evidence is absent.
There is another complication: an answer can contain genuine citations and still stretch the meaning of its sources. A study of one group is presented as a rule for everyone. A survey response becomes a claim about behavior. A correlation becomes a recommendation. We do not need to distrust every answer to recognize that checking an important claim requires more than seeing a hyperlink.
A Three Question Test For Real Understanding
When a topic matters, I recommend a short check that can be performed before treating an AI explanation as learned knowledge.
First Question: state the main claim in your own words without looking at the answer. If the claim disappears when the screen is closed, you may have followed the wording rather than understood the argument.
Second Question: identify what would change your mind. Look for an assumption, an alternative explanation, or a piece of evidence that would force you to revise the conclusion. Understanding often shows up in the ability to describe where an explanation could fail.
Third Question: apply the idea to a different example. If the system explained how feedback can improve performance, ask what happens when the feedback is delayed, inaccurate, or directed at the wrong behavior. If you can work through the changed case, you have evidence that your learning has begun to transfer beyond the original wording.
These questions are practical tests, not formal psychological assessments. They do not guarantee expertise. They make the difference between feeling informed and demonstrating some independent understanding harder to ignore.
Keep The Right Kind of Difficulty
The point is not to make every task harder than necessary. A person who needs a reliable summary of a meeting should use a reliable method to obtain one. Someone learning a demanding skill, however, may benefit from trying the first step before asking for the solution. The difficulty of making a prediction, checking a source, or explaining a choice can provide practice that a finished answer removes.
This requires self-knowledge. Sometimes we need speed because a decision is time-sensitive. Sometimes we need to learn because the same decision will recur. Sometimes the consequences of a wrong answer are high enough that the evidence must be inspected directly. We should make the purpose of the interaction explicit before deciding how much reasoning to delegate.
Teachers can use guided prompts that ask students to explain their steps. Managers can request the assumptions behind important recommendations. Individuals can ask the system for counterexamples instead of a polished final answer. These practices keep the convenience of assistance while making room for judgment.
The most important habit may be modest: remain capable of saying, "That sounds clear, but I have not checked it." Clarity is valuable. Confidence can help us act. Neither substitutes for knowing what an explanation actually supports.
The next time an assistant answers a difficult question beautifully, close the window for a moment and try to answer one question it did not anticipate. What you can say next will tell you more about your understanding than how effortless the first answer felt.
Gleb Tsipursky, PhD, behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook
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