Collaborative Research: The Current State and Future Directions of AI Education PD for K-12 Teachers

As artificial intelligence (AI) becomes increasingly common, states and school districts are urgently investing in professional development to prepare K-12 educators to teach with and about AI. However, it is currently unclear what effective AI professional development entails, including whether it should focus on basic AI literacy or deeper concepts like machine learning and model architecture. Furthermore, the rapid educational emphasis on AI risks disrupting the decade-long investment in ”Computer Science (CS) for All” initiatives by potentially displacing resources, institutional priorities, and teacher availability. This project will address these fundamental issues (1) by investigating how AI education is being treated in summer professional development programs and (2) by explicitly examining its relationship with established computer science education programs and initiatives. By generating the first multi-state, evidence-based guide for integrating AI into K-12 educator professional development, the project will provide critical policy and practice guidance for future AI professional development efforts. The broader impacts of this work include ensuring that current K-12 students are adequately prepared to thrive in an AI-driven society, protecting the millions of dollars invested in K-12 science, technology, engineering and mathematics (STEM) infrastructure, and empowering state departments of education and local districts to implement and to support appropriate evidence-based professional development. Ultimately this initiative will establish a foundational understanding of the AI teacher professional development landscape, promote the building of teaching capacity across a range of school contexts, and foster the sustainable integration of artificial intelligence and computer science education nationwide. The scientific goal of this project will be to empirically map the current landscape of artificial intelligence professional development and systematically measure its alignment with educator needs, barriers, and existing computer science capacity building efforts. To achieve this goal, the research team will employ a concurrent transformative mixed-methods design across national providers and states. The methodology will consist of five interconnected phases, beginning with a rigorous landscape audit and document analysis of 50 professional development instructional offerings. These materials will be analyzed and coded utilizing the Technological Pedagogical Content Knowledge (TPACK) framework to evaluate how well the professional development curricula integrate technical knowledge, pedagogy, and subject content. Subsequent research phases will include detailed surveys and semi-structured interviews with professional development providers to identify the specific contextual and psychosocial barriers influencing their instructional design choices. Additionally, the study will deploy an extensive cross-sectional survey to over 1,500 educators and administrators to assess perceived instructional needs, followed by purposive sampling for semi-structured interviews to explore identified gaps. Finally, the project will investigate broader systemic trends by distributing targeted questionnaires to state-level computer science supervisors and embedding specialized survey modules within the Computer Science Teachers Association’s annual national landscape study. By synthesizing these diverse quantitative and qualitative data streams, the project will advance theoretical frameworks for artificial intelligence pedagogical content knowledge and deliver a comprehensive National Artificial Intelligence Professional Development Guidance Framework designed to support researchers, practitioners, and educational policy makers with evidence-based guidance. This project was funded by the STEM K-12 program, which supports fundamental, applied, and translational research on STEM learning in both formal K-12 education and informal learning environments. 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.