How to Use AI to Strengthen Metacognition

AI can finish the task before the learner has understood the thinking.

That is one of the defining design challenges of AI-supported learning. A student asks AI for feedback and receives a clear list of improvements. The suggestions are useful. The structure becomes stronger. The explanation becomes clearer. The final version is better than the first.

Then the teacher asks, “What changed in your thinking?”

The student describes the edits.

That moment reveals the deeper opportunity. The work improved, but the learner may not yet understand the improvement. They may know what changed on the page, in the design, in the code, in the explanation or in the presentation. They may not yet understand what changed in their thinking.

In classrooms, metacognition is less about naming thinking and more about helping students notice the decisions their thinking is making. It includes the student’s ability to plan, monitor and evaluate their own thinking, and to use that awareness to adjust strategies and improve learning (Flavell, 1979; Education Endowment Foundation, 2025). It helps students ask what they are trying to understand, how well their current approach is working, what needs to change and why a revision improves the quality of their work.

AI makes this more important, not less. When AI is available, students can move quickly from uncertainty to a polished response. They can receive suggestions before they have fully examined their own thinking. They can improve the surface of the work before they understand the gap that needed improving. Research on AI-generated feedback distinguishes between directive feedback, which provides corrections or explicit guidance, and metacognitive feedback, which prompts students to reflect, track progress and develop self-regulated learning skills (Alsaiari et al., 2025).

With thoughtful design, AI can help students slow down at exactly the right moment: the moment where thinking becomes visible, revisable and more conscious.

AI strengthens metacognition when it helps students notice the quality of their thinking.

This is the role of AI in metacognitive learning. It helps students examine, test and revise their own thinking as part of completing the task in front of them.

This article is part of the Visible Agency series. For the broader framework, see Visible Agency: How to Design AI-Supported Learning Without Outsourcing Student Thinking.

When AI improves the work faster than students understand the improvement

AI can help students improve work quickly. It can suggest a stronger explanation, a clearer structure, a better example, a more precise question, a cleaner design, a more efficient solution or a more persuasive presentation. That improvement can be useful, especially when it helps students see possibilities they had not yet considered.

The deeper learning question is whether students understand the improvement. A student who accepts an AI suggestion may produce better work before they have understood why the suggestion was better. They may replace a weak explanation with a stronger one before they notice what made the first explanation weak. They may revise a design before they understand the trade-off. They may correct a line of code before they understand the logic. They may add evidence to an argument before they understand how the evidence changes the strength of the claim.

AI-supported learning becomes more powerful when students pause at that point of improvement and examine the thinking behind it. Feedback has a stronger learning purpose when students actively interpret it, compare it with criteria and use it to regulate their next steps (Hattie & Timperley, 2007; Nicol & Macfarlane-Dick, 2006). This is especially important with AI feedback because the support can arrive quickly, fluently and confidently, which increases the need for students to interpret and act on it thoughtfully.

What was unclear before? What became clearer? What assumption changed? What decision improved the work? What would the student now do differently without AI support? These questions turn improvement into learning.

A better answer becomes more valuable when students understand the thinking that made it better.

This is the metacognitive opportunity. AI can help students produce better work, and the deeper value comes when students understand how better work is developed.

How AI can make thinking easier to notice

AI can become a useful mirror when students examine the reflection and decide what it reveals about their own thinking. It can reflect back patterns, gaps, assumptions, alternatives and possible weaknesses. It can help students see what they may not have noticed in their first attempt.

That makes AI useful for metacognition when the student remains active in the process. A student might ask AI to identify where an explanation is unclear, then decide whether the feedback is accurate. They might ask AI to generate alternative interpretations, then compare them with the evidence. They might ask AI to question an assumption, then examine whether the assumption is actually present in their work. They might ask AI to suggest revisions, then explain which revision improves the thinking and why.

The metacognitive value sits in the student’s awareness. Students are asking, “How can this work be improved?” They are also asking, “What does this reveal about how I was thinking?” This aligns with self-regulated learning research, which describes learners as active participants who set goals, monitor progress, evaluate strategies and adjust future action (Zimmerman, 2002; Education Endowment Foundation, 2025).

That second question turns AI support into learning evidence. Students begin to notice the quality of their own thinking. They see where their understanding was strong, where it was incomplete, where their reasoning needed support and where their next question should go.

In AI-supported learning, metacognition is one way students remain active agents in the process. When students can explain what they asked, what they noticed, what they changed and why, AI use becomes part of a visible learning process. This connects directly to the broader agency challenge identified in research on generative AI and education: AI may support learning, but students still need to retain meaningful authorship, autonomy and responsibility within the process (Roe & Perkins, 2024).

This is also the heart of Visible Agency. If AI is part of the learning process, students need to show the questions they asked, the choices they made, the judgements they formed, the revisions they justified and the ownership they demonstrated. For a deeper look at this evidence framework, see How to Make Student Thinking Visible When AI Is Part of the Process.

Use AI to test thinking before settling on an answer

One of the strongest uses of AI in learning is to help students test their thinking before they settle on an answer. Students can ask AI to challenge a claim, identify assumptions, offer counterexamples, reveal missing evidence, suggest alternative explanations or point out where a solution might fail. The purpose is to create a thinking encounter the student has to respond to.

AI becomes a prompt for examination. The student judges the response, tests it against criteria, compares it with evidence and decides what to do next. This is where metacognition and judgement begin to work together.

For example, a student might write an explanation of a historical cause and then ask AI, “What assumptions does this explanation rely on?” Another student might test a science claim by asking, “What evidence would challenge this conclusion?” A student designing a prototype might ask, “Where might this solution fail for the intended user?” A student preparing for a discussion might ask, “What would someone with a different perspective question in my position?”

Each question invites the learner to look again. AI can help students examine ideas from another angle before they become too attached to their first answer. It can help them see that an answer can sound complete while still needing stronger reasoning, better evidence or a more careful distinction.

This also connects to AI literacy. AI literacy frameworks commonly include understanding, using, evaluating and critically reflecting on AI systems and their outputs (Long & Magerko, 2020; Zhang & Magerko, 2025). In classroom learning, that means students need to evaluate AI responses and use those responses to examine the quality of their own thinking.

This links closely to How to Design AI Tasks That Require Student Judgement. Judgement asks students to evaluate the quality of AI output. Metacognition asks students to use that encounter to examine the quality of their own thinking.

Use AI to deepen the question

AI often gives students an answer quickly. In metacognitive learning, the first answer should help students see the next question more clearly.

A student who asks AI for an explanation may receive a clear response. A metacognitive task might then ask the student to identify which part of the explanation raises a deeper question. A student who asks AI for a list of design ideas may then ask which idea has the strongest assumptions, the greatest trade-offs or the most interesting constraints. A student who asks AI to summarise a topic may then ask what a beginner would miss, what an expert would question or what remains uncertain.

AI can help students extend inquiry. A strong AI-supported task might ask students to generate three deeper versions of their original question. It might ask them to compare a surface question with a more precise one. It might ask them to identify the question that would lead to better evidence, more careful thinking or a stronger final product.

The learning purpose is to make the question more productive.

The first answer should help students see the next question more clearly.

This is one of the most useful ways AI can strengthen metacognition. Students begin to notice the difference between finishing an answer and improving the quality of the inquiry. They become more aware of what they are trying to understand, what remains unclear and where their thinking needs to go next. The Education Endowment Foundation’s guidance on metacognition and self-regulated learning emphasises the importance of helping students plan, monitor and evaluate their learning within subject tasks rather than treating reflection as a disconnected activity (Education Endowment Foundation, 2025).

Use AI feedback to make revision more deliberate

Revision is one of the clearest places to strengthen metacognition. AI can suggest improvements quickly. It can identify weak structure, unclear explanation, missing evidence, awkward phrasing, possible misconceptions, design limitations or alternative approaches. These suggestions can be helpful, and the learning deepens when students explain what changed in their thinking because of the revision.

A revision should show that the work improved. It should also show that the student understands why the improvement matters. A student might revise a paragraph because AI feedback helped them see that their claim was too broad. They might adjust a design because AI helped them notice a user need they had overlooked. They might change a data interpretation because AI raised a question about the evidence. They might improve a presentation because AI identified a gap between the intended audience and the structure of the message.

In each case, the revision becomes metacognitive when the student can explain the thinking behind the change.

Revision becomes metacognitive when students can explain why the change improved the thinking.

This is the difference between accepting feedback and learning from feedback. The student evaluates the suggestion, connects it to the learning purpose and decides how to act on it. They understand the improvement well enough to explain it. Research on formative feedback and self-regulated learning supports this emphasis because feedback becomes more powerful when learners use it to clarify standards, monitor progress and take greater responsibility for improvement (Nicol & Macfarlane-Dick, 2006; Hattie & Timperley, 2007).

This is also where metacognition connects to ownership. Students can stand behind the final work when they understand the thinking that shaped it. For a deeper exploration of ownership in AI-supported learning, see How to Design AI-Rich Tasks That Still Require Student Ownership.

Student reflection prompts for AI-supported metacognition

Student reflection works best when it is brief, specific and connected to the learning. A long reflection after the task may produce general comments. A sharper prompt at the right moment can help students notice their thinking while it is still developing.

These prompts can be used before, during and after AI use. They are examples of questions teachers can choose from depending on the learning purpose.

Before using AI

• What am I trying to understand?
• What do I already think about this?
• What am I uncertain about?
• What kind of support would improve my thinking?
• What do I want AI to help me examine?

During AI use

• What is AI helping me notice?
• What assumption is being challenged?
• What suggestion do I need to test?
• What part of my thinking feels clearer now?
• What question is becoming more important?

After using AI

• What changed in my thinking?
• What do I understand more clearly now?
• What did I accept, adapt or reject?
• What revision shows my thinking has developed?
• What can I now explain or defend more confidently?

These questions help students use AI with awareness. They move attention from the final product to the thinking process that shaped it. This is consistent with metacognitive instruction more broadly: students benefit when they are explicitly supported to plan, monitor and evaluate their thinking in relation to meaningful learning tasks (Education Endowment Foundation, 2025; Zimmerman, 2002).

What teachers can look for as evidence of metacognition

Metacognition needs to become visible enough for teachers to recognise it. That visibility can come through evidence that students noticed, monitored or evaluated their thinking. Teachers might look for a clearer question, a tested assumption, a changed explanation, a justified revision, a comparison between first thinking and later thinking, or a student explanation of what changed and why.

The evidence may be written, spoken, annotated, recorded or discussed. It should fit the task and the learning purpose. A student might show metacognition by explaining why they rejected an AI suggestion. Another might show it by identifying the assumption that changed during the process. Another might show it by comparing an early explanation with a final version and explaining what became clearer. Another might show it by defending a revision during a short conference or Socratic discussion.

The evidence can be brief when it reveals awareness.

The evidence of metacognition is the student’s awareness of how their thinking changed.

This helps teachers see more than the improvement in the work. It helps them see the student’s developing awareness of the work. This also sits within the wider assessment-for-learning tradition, where evidence of learning is used to guide feedback, next steps and student self-regulation (Black & Wiliam, 1998; Nicol & Macfarlane-Dick, 2006).

Designing AI tasks around moments of changed thinking

Using AI to strengthen metacognition requires small but deliberate design choices. Teachers can build metacognitive moments into tasks students already complete. The key is to decide where students should pause, examine their thinking and explain what changed.

A writing task might ask students to compare their first claim with their revised claim. A design task might ask students to identify one assumption AI helped them question. A research task might ask students to explain which AI suggestion required verification. A coding task might ask students to explain why a suggested fix works. A presentation task might ask students to describe how audience feedback changed the structure of the message.

These moments help students use AI as part of a thinking process. They also give teachers clearer evidence of learning. The strongest AI-supported tasks give teachers and students better visibility at the point where thinking changes.

Frequently asked questions

Final thought

Metacognition becomes more important, not less, when AI is available. Students need to know more than what AI helped them produce. They need to understand what AI helped them notice. They need to see how their thinking changed, where their understanding deepened and what they can now explain or defend more clearly.

That is the deeper promise of AI-supported learning. AI can help students complete tasks. Used with intention, it can also help them become more aware of the thinking that makes learning possible.

Where to go next

This article is part of the Visible Agency series.

• For the broader framework, read Visible Agency: How to Design AI-Supported Learning Without Outsourcing Student Thinking.

• For the process evidence side of the work, read How to Make Student Thinking Visible When AI Is Part of the Process.

• For the judgement side of the work, read How to Design AI Tasks That Require Student Judgement.

• For the ownership side of the work, read How to Design AI-Rich Tasks That Still Require Student Ownership.