A look at how AI might support student planning, monitoring, and self-reflection in learning
By Joseph J. Slade, OSU School of Psychological Science
Educators have long guided students not only by sharing knowledge but also by helping them develop the ability to plan, monitor, and reflect on their own learning. These metacognitive skills are what allow learners to become more independent, self-aware, and intentional about how they approach challenges. Today, many students are turning to AI tools to support their academic work, whether to generate ideas, receive feedback on drafts, or, more controversially, to complete entire assignments on their behalf. This trend raises important questions for educators: does reliance on AI undermine authentic learning, or can these tools be harnessed to actively support students in developing metacognitive and self-regulated learning skills?
Metacognition refers to a students’ awareness and regulation of their own thinking and learning processes. Self-regulated learning (SRL) builds on this foundation, describing an iterative learning cycle with three key phases in which students set goals and plan strategies, monitor progress, and reflect on their outcomes (Zimmerman & Schunk, 2011). By iteratively engaging in this learning process, students learn not only what to study but also how to study, evaluating their strengths and weaknesses, selecting appropriate strategies, and adjusting as needed (Winne, 1995; Zimmerman, 1990).
Research consistently shows that self-regulated learners are more successful because they proactively manage their own development using higher-level thinking skills such as goal-setting, strategy use, and critical reflection (Bjork et al., 2013). But cultivating these habits is not easy, especially in educational contexts where students may feel overwhelmed by large classes, limited instructor feedback, or the sheer volume of academic tasks.
This is where AI may offer an important complement. Recent studies highlight AI’s potential for personalized learning, intelligent feedback, and scalable pedagogical support (Chen et al., 2023). AI-powered systems, for example, can prompt students to plan their study sessions, provide real-time monitoring of performance, and generate reflection prompts after tasks are completed (Molenaar, 2022). In larger classrooms where faculty cannot give every student individual guidance, AI may allow learners to practice SRL strategies more frequently and deliberately (Jin et al., 2023).
Planning: Setting goals and structuring effort
AI tools can co-construct study plans tailored to specific course schedules as well as a learner’s pace, helping students set realistic learning goals, break tasks into steps, and create timelines. Such planning can help students allocate their time, attention, and effort strategically. And with basic versions of tools like ChatGPT, Gemini, and Claude offered at no cost, AI broadens access to personalized planning support which many students might not otherwise receive.
Sample student prompts for planning with AI:
“I have three weeks to prepare for a midterm in Cognitive Psychology. Can you help me break down the chapters into a weekly study schedule with daily tasks?”
“I need to write a 10-page paper due in two weeks. Can you help me create a step-by-step timeline for brainstorming, researching, drafting, and revising?”
Monitoring: Tracking progress and adjusting strategies
AI can act as a kind of “learning dashboard,” providing feedback on progress toward goals through the use of practice quizzes, writing feedback, or problem-solving hints. This kind of adaptive practice system can highlight strengths and weaknesses in real-time, giving students information they can use to adjust their study strategies.
Monitoring also involves noticing patterns in one’s learning environment and behaviors. For instance, a student might realize they are easily distracted while working in their room and decide to move to the library to concentrate more effectively. This kind of self-observation and adjustment is at the heart of metacognition and can be reinforced through AI feedback and prompts.
However, learners may over-rely on AI-generated judgments without fully engaging in their own self-monitoring, potentially outsourcing critical reflection. Instructors could help by encouraging students to compare AI feedback with their own perceptions and evaluations of performance.
Sample student prompts for monitoring with AI:
“Based on the last two drafts of my essay, what areas show the most improvement, and what weaknesses still remain for me to work on?”
“Can you help me identify which of my study habits are supporting my learning and which ones might be holding me back?”
Reflection: Making thinking visible
Reflection encourages students to pause, make their thinking explicit, and consider alternatives. Research shows that reflection supports deeper learning and transfer, helping students move knowledge from one context to another (Chi, Roy, & Hausmann, 2008). Students can guide the process by posing reflective questions to AI, which can respond with feedback, probing questions, or alternative perspectives. This approach keeps the learner in charge of their own meaning-making, while positioning AI as a supportive guide rather than a substitute for reflection.
Sample student prompts for reflection with AI:
“I just completed a practice quiz. Can you guide me in thinking about the mistakes I made and what they reveal about gaps in my understanding?”
“Here’s my summary of today’s lecture. Can you ask me questions that prompt me to connect these ideas to concepts we studied earlier in the course?”
Conclusion
AI has the potential to both support and hinder students’ ability to plan, monitor, and reflect on their learning. On the one hand, if used uncritically, AI tools may tempt students to outsource their thinking and short-circuit the deep reflection that leads to genuine understanding. But when guided by thoughtful prompts and intentional instructional design, these same tools can act as powerful partners, helping students break tasks into steps, check their progress, and articulate what they’ve learned. For educators, the challenge may not be whether to use AI at all, but how to help students adopt it in ways that foster independence, critical thinking, and self-regulation. By modeling effective prompts and encouraging metacognitive engagement, teachers can harness AI’s benefits while mitigating its risks.
References
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444. https://doi.org/10.1146/annurev-psych-113011-143823
Chen, X., Zou, D., Cheng, G., Xie, H., & Jong, M. (2023). Blockchain in smart education: Contributors, collaborations, applications, and research topics. Education and Information Technologies, 28(4), 4597–4627. https://doi.org/10.1007/s10639-022-11399-5
Chi, M. T. H., Roy, M., & Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301–341. https://doi.org/10.1080/03640210701863396
Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37. https://doi.org/10.1186/s41239-023-00406-5
Molenaar, I. (2022). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, Article 100070. https://doi.org/10.1016/j.caeai.2022.100070
Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist, 30(4), 173–187. https://doi.org/10.1207/s15326985ep3004_2
Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. Handbook of Self-Regulation of Learning and Performance, 15–26. https://doi.org/10.4324/9780203839010
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
About the author: Joseph J. Slade is a psychology Ph.D. student at Oregon State University studying the intersection of AI, teaching, and learning. He serves as the director of Project FAILSafe (Fostering AI Learning Safely), a collaboration between the School of Psychological Science and the School of Computer Science and Electrical Engineering that is developing AI educational tools and testing their efficacy through controlled experiments and classroom applications. His work focuses on identifying how educators can make best use of large language models to improve student engagement and learning outcomes.
Top image generated with Microsoft Copilot
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