Collaborative Research: Using Artificial Intelligence to Support Student Metacognition in Undergraduate Biology and Bioinformatics Courses

This project aims to serve the national interest by advancing undergraduate STEM education through the development of artificial intelligence (AI) tools that strengthen students’ metacognitive learning practices. Metacognition—students’ ability to plan, monitor, and evaluate their learning—is essential for success in increasingly data-rich and complex biology and bioinformatics courses. The significance of this project lies in its focus on ensuring that emerging AI technologies enhance, rather than replace, productive learning behaviors. This Level 1 Engaged Student Learning project plans to design and implement AI-enabled virtual assistants embedded within course assignments to guide students’ reasoning, reflection, and problem-solving processes. The project will integrate structured AI supports into undergraduate coursework to promote self-regulated learning, improve student outcomes, and expand instructor capacity to support students. The goals of this project are to investigate how different AI support designs influence students’ metacognitive awareness, engagement, and learning behaviors and to identify mechanisms through which AI can promote effective self-regulation. The scope includes introductory biology and bioinformatics courses, enabling comparisons across class contexts and conceptual domains. The project plans to experimentally compare multiple virtual assistant designs, including unmoderated access, example-based support, conceptual guidance, and Socratic prompting. Analyses of quantitative and qualitative data will evaluate changes in metacognition, student assignment interaction patterns, and learning outcomes. In addition, the project will develop instructor dashboards that synthesize student data to inform real-time instructional decisions. Assessment and evaluation will include metacognitive measures, behavioral analytics, and iterative dashboard refinement. The project team will disseminate results through faculty communities, published manuscripts, and national presentations, contributing new knowledge and practical design principles for AI-enabled learning in STEM education. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.