Unlocking the Potential of AI-Enabled Self-Regulated Learning in Calculus Education

This project aims to serve the national interest by implementing evidence-based practices to help undergraduate Calculus students become more conscientious, self-reliant, and autonomous learners. The uniqueness of this project lies in leveraging artificial intelligence (AI) to enhance students’ learning experiences by advancing their metacognitive knowledge and mathematics-specific self-regulated learning skills. Toward these goals, this Level 2 Engaged Student Learning project plans to develop, implement, and evaluate the effectiveness of innovative instructional modules on self-regulated learning skills—time management, problem-solving, and exam preparation. Developing stronger self-regulated learning skills prepares all students for success in Calculus, upper-level mathematics courses, other STEM studies, and future careers in STEM-related fields. The project's objectives closely align with national goals to promote excellent undergraduate STEM education by using innovative and creative methods to generate knowledge and produce new insights into STEM teaching and learning. The innovation advanced by the project can serve as a replicable model for other institutions of higher education nationwide. Using a design-based research methodology, comprising four iterative cycles, the project plans to design, implement, and rigorously examine the research objectives: (1) evaluate the impact of the self-regulated learning intervention on Calculus students’ learning outcomes, motivation, and self-regulated learning skills; (2) compare effectiveness and cognitive processes across three interaction modes: AI-bot, video and text; (3) explore students’ perceptions of AI-bot on their learning. Through carefully designed experiments with controlled confounding factors, the project plans to rigorously examine the effectiveness and benefits of course-integrated self-regulated learning training, supported by innovative AI-bot technology, for Calculus students’ learning, self-regulated learning skills, and motivation. In addition, a detailed qualitative analysis, enhanced by natural language processing, aims to uncover how students engage cognitively and emotionally with the self-regulated learning activities and increase understanding of the processes underlying human-AI interactions. The project plans to disseminate the scientific knowledge generated by the study, self-regulated learning modules, and training materials to various educational stakeholder groups through publications and workshops. The scientific knowledge generated by the project aims to enable breakthroughs in supporting self-regulated learning in college Calculus and utilizing AI chatbots in undergraduate mathematics, potentially transforming and enhancing learning environments for all undergraduate STEM students. 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.