AI-Driven Professional Learning to Strengthen Pre-Service Teachers' Content Knowledge, Pedagogical Content Knowledge, and Mathematical Creativity

This project aims to serve the national interest by improving mathematics teacher preparation. The United States faces persistent shortages of well-prepared mathematics teachers, particularly in rural and high-need communities, while many pre-service teachers enter preparation programs with limited confidence in mathematics and insufficient preparation to foster creative, conceptually rich instruction. This Level 3 Engaged Student Learning project plans to develop and study an AI-guided professional learning model designed to strengthen pre-service mathematics teachers’ content knowledge, pedagogical content knowledge, and mathematical creativity. Through interactive AI-guided modules, pre-service teachers will engage in flexible problem solving, problem posing, instructional analysis, and structured reflection on teaching practices that promote creative mathematical thinking. The professional learning model will be embedded within teacher preparation programs across eight geographically diverse institutions, including rural-serving and urban-serving universities, and will be designed to be accessible, adaptive, and scalable across diverse institutional contexts. By leveraging artificial intelligence to provide personalized feedback and structured metacognitive guidance at scale, this project seeks to improve mathematics teacher preparation nationwide and justify federal investment in innovative, technology-enabled solutions to strengthen the future teaching workforce. The project will implement and study a series of AI-guided professional learning modules integrating creativity-directed instructional practices within fraction computation and rate and proportional reasoning. The AI system will function as a cognitive partner, delivering adaptive prompts, scaffolded feedback, and metacognitive questioning tailored to each participant’s responses. Using a mixed-methods research design, including hierarchical linear modeling and qualitative analyses of classroom observations, interviews, and AI interaction logs, the study will examine changes in pre-service teachers’ content knowledge, pedagogical content knowledge, and mathematical creativity, as well as how these domains interact to support creativity-directed instructional practices during practicum teaching. The project contributes to the emerging field of AI-supported teacher education by (1) advancing theoretical integration of content knowledge, pedagogical content knowledge, and mathematical creativity; (2) providing multi-site empirical evidence of AI-supported professional learning in pre-service contexts; and (3) developing a scalable, transferable model for mathematics teacher preparation that can extend to additional mathematical domains and diverse teacher education settings. This project is supported through the Robert Noyce Teacher Scholarship Program (Noyce) and the NSF IUSE: EDU Program. The Noyce program supports the training and development of undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. 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.