Building AI Competencies in Elementary Teacher Preparation: A Place-Based Approach to Science Education

This project aims to serve the national interest by equipping elementary teachers with the artificial intelligence (AI) competencies needed to cultivate an AI-literate workforce and support young students’ foundational understanding of AI concepts. As AI rapidly transforms educational and professional contexts, ensuring that elementary teachers can integrate AI literacy and AI pedagogy into instruction in developmentally appropriate ways carries growing national significance. This need is important at the elementary level, where early learning experiences shape students’ later interest and participation in STEM and AI-related fields, yet most teacher preparation programs offer few evidence-based models for developing these competencies. This Level 1 Engaged Student Learning project plans to help future elementary teachers learn about AI by incorporating AI-related activities into a science teaching methods course, using local issues as the learning context. The project will create opportunities for pre-service teachers to explore how AI can support science learning and teaching while developing practical strategies for classroom implementation. The project's distinguishing feature lies in connecting AI literacy, science teacher education, and place-based learning—three bodies of scholarship that have previously evolved in parallel. Expected outcomes include enhanced AI competencies among approximately 200 pre-service teachers, improved approaches for integrating AI into elementary science teacher education, and sustainable instructional resources that support long-term adoption across teacher preparation programs. The project's goals and scope span four connected objectives. The first integrates AI literacy into an elementary science methods course through place-based learning focused on temperature inversions. The second examines the effects of this intervention on pre-service teachers' AI competencies and instructional practices. The third builds a generative model for AI-mediated, place-based science teacher education. The fourth produces instructional resources for broader dissemination. Building on research demonstrating that place-based learning enhances learners’ engagement with science and contextual understanding, this project draws on AI literacy frameworks that provide structured approaches to technology integration. Together, these foundations position the project to investigate how AI competencies develop within disciplinary contexts and identify instructional conditions that support meaningful AI integration in teacher preparation. The project will employ iterative design-based research across three academic years, with systematic curriculum development cycles that incorporate continuous refinement. The research plan uses a concurrent triangulation mixed-methods approach to examine both quantitative outcomes and qualitative processes. Quantitative data collection includes pre- and post-tests of AI knowledge and pre- and post-surveys of AI competencies. Qualitative data include participant reflections, lesson plans created by pre-service teachers, and classroom observations. This project is supported through the NSF IUSE:EDU Program and the Robert Noyce Teacher Scholarship Program (Noyce). 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.