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Leveraging Generative AI to Foster Metacognition and Self-Directed Learning

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Title: Leveraging Generative AI to Foster Metacognition and Self-Directed Learning
Language: English
Authors: Brandon Lowry, Samantha McGrath, Chad Eitel, Heather Hall, Tod R. Clapp (ORCID 0009-0008-9102-7896)
Source: Journal of Microbiology & Biology Education. 2026 27(1).
Availability: American Society for Microbiology. 1752 N Street NW, Washington, DC 20036. Tel: 202-737-3600; e-mail: journals@asmusa.org; Web site: https://journals.asm.org/journal/jmbe
Peer Reviewed: Y
Page Count: 11
Publication Date: 2026
Sponsoring Agency: National Science Foundation (NSF), Graduate Research Fellowship Program (GRFP)
Contract Number: 2234690
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Metacognition, Independent Study, Artificial Intelligence, Graduate Students, Anatomy, Neurosciences, Biomedicine, Research Universities, Masters Programs, Technology Uses in Education, Models, Learning Readiness, Student Attitudes
ISSN: 1935-7877
1935-7885
Abstract: With the ever-expanding amount of data, students increasingly find themselves needing to engage in self-directed learning to be successful. Students studying science, technology, engineering, and mathematics often struggle with self-directed learning and are often discouraged, leading to higher attrition within these disciplines. There is a lack of opportunities for students to develop and practice self-directed learning skills within traditional curricula. This research explored the ways in which a generative artificial intelligence model could be used to cultivate metacognition and promote readiness for self-directed learning among graduate students. By leveraging the relationship between metacognition and self-directed learning, with the customizability of the artificial intelligence model, we sought to facilitate conversations between students and the model to enhance metacognitive awareness and self-directed learning readiness. Using the Metacognition Awareness Inventory and Self-Directed Learning Instrument, we found that students improved significantly on both pre- and post-assessment comparisons. Students needed to interact with the model twice a week, for 10 minutes per session. Our findings demonstrate a novel application of generative artificial intelligence in supporting students' personal development and expand our understanding of how artificial intelligence can be leveraged to generate a supportive process, rather than solely as a mechanism for generating answers or some other product.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1504816
Database: ERIC
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Microbiology+%26+Biology+Education%22"><i>Journal of Microbiology & Biology Education</i></searchLink>. 2026 27(1).
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  Data: American Society for Microbiology. 1752 N Street NW, Washington, DC 20036. Tel: 202-737-3600; e-mail: journals@asmusa.org; Web site: https://journals.asm.org/journal/jmbe
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  Data: With the ever-expanding amount of data, students increasingly find themselves needing to engage in self-directed learning to be successful. Students studying science, technology, engineering, and mathematics often struggle with self-directed learning and are often discouraged, leading to higher attrition within these disciplines. There is a lack of opportunities for students to develop and practice self-directed learning skills within traditional curricula. This research explored the ways in which a generative artificial intelligence model could be used to cultivate metacognition and promote readiness for self-directed learning among graduate students. By leveraging the relationship between metacognition and self-directed learning, with the customizability of the artificial intelligence model, we sought to facilitate conversations between students and the model to enhance metacognitive awareness and self-directed learning readiness. Using the Metacognition Awareness Inventory and Self-Directed Learning Instrument, we found that students improved significantly on both pre- and post-assessment comparisons. Students needed to interact with the model twice a week, for 10 minutes per session. Our findings demonstrate a novel application of generative artificial intelligence in supporting students' personal development and expand our understanding of how artificial intelligence can be leveraged to generate a supportive process, rather than solely as a mechanism for generating answers or some other product.
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      – Text: English
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        PageCount: 11
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      – SubjectFull: Metacognition
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      – SubjectFull: Independent Study
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Student Attitudes
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