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 |
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| Language: | English |
| Authors: | Brandon Lowry, Samantha McGrath, Chad Eitel, Heather Hall, Tod R. Clapp (ORCID |
| 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 |
| 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. |
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| ISSN: | 1935-7877 1935-7885 |
