Leveraging generative AI to foster metacognition and self-directed learning

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.