The effect of metacognitive prompts using generative AI on cognitive load, task performance, and self-efficacy in online self-regulated learning
Abstract
Self-regulated learning is essential in online environments, where learners must manage large amounts of information, resulting in increased cognitive load and reduced task performance. While metacognitive prompts can support self-reflection and self-management, their classroom integration remains challenging. Recent advancements in generative AI, such as customised ChatGPT, provide new opportunities for more practical integration. To explore this potential, we conducted a study with 40 South Korean university students randomly assigned to either an experimental or a comparison group. All participants watched Python programming video lectures and completed problem-solving tasks using ChatGPT, but only the experimental group received metacognitive prompts. Cognitive load and self-efficacy were assessed through self-reported surveys, and task performance was evaluated based on problem-solving processes, outcomes, and retention tests. The results revealed significant group differences in problem-solving processes, with the experimental group showing a tendency towards lower cognitive load, higher self-efficacy, and improved task performance.
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These approaches keep students cognitively engaged and make it more difficult for them to drift into passive acceptance or superficial completion.
“These approaches keep students cognitively engaged and make it more difficult for them to drift into passive acceptance or superficial completion (Lee et al., 2024; Joo et al., 2026; Lukešová & Jennings, 2025; Sun et al., 2026; R. Zhou et al., 2025).”
From Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review (2026) · cites this paper as Joo et al., 2026
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Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review
Wang, G., Wang, W., Yang, D., & Ren, J. (2026) · Behavioral Sciences · doi:10.3390/bs16071150 · Cited by 20 · see citing works
“These approaches keep students cognitively engaged and make it more difficult for them to drift into passive acceptance or superficial completion (Lee et al., 2024; Joo et al., 2026; Lukešová & Jennings, 2025; Sun et al., 2026; R. Zhou et al., 2025).”
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Cognitive offloading in student–AI collaboration: A longitudinal analysis of prompting strategies
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Yan, Z., Mendoza, N. B., Zhan, Y., & Wu, Y.-T. (2026) · Educational Psychology · doi:10.1080/01443410.2026.2739049
- Authors
- Joo, S., Han, I., & Park, I.
- Affiliations
- Florida State University (US)
- Korea University (KR)
- Corresponding author
- Han, Insook
- Venue
- Educational Psychology
- Publisher
- Informa UK Limited
- Published
- 2026-01-27
- Pages
- 1-24
- ISSN
- 0144-3410 (ISSN-L)
- 1469-5820
- OpenAlex
- W7125946877
- Cited by
- 5
- References
- 42
- Citations by year
- 2026: 5
- FWCI
- 72.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Psychology (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- metacognitive prompts, generative AI, ChatGPT, cognitive load, self-efficacy, task performance, student self-regulation, online learning, programming education, problem solving, university students
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