Navigating Teaching in the Age of AI: Sprints for Authentic STEM Experiences

Artificial intelligence is rapidly changing what students need to learn in science, technology, engineering, and mathematics (STEM) and how teachers teach it. Teachers in rural districts, where 20% of students in the United States attend school, face this transformation with limited access to sustained, collaborative professional learning. This project, a partnership between Education Development Center (EDC) and the University of Wisconsin-Milwaukee (UWM), will build and test a professional learning model that supports forty rural STEM teachers as they navigate AI-transformed education, and through their classrooms will reach thousands of rural students. Participating teachers will also co-design and lead online workshops for a broader audience of teachers, extending the project’s reach beyond the original cohort. By preparing teachers to use AI in ways that deepen rather than replace student thinking, the project will serve the national interest in developing a STEM workforce ready for hybrid human-machine roles and will contribute to opportunity in rural communities where STEM education pipelines drive economic growth. The project has three goals: to design and test a scalable, replicable model for rural STEM teacher professional learning in the AI era; to advance empirical understanding of human-centered AI use in STEM education; and to build actionable knowledge about teacher learning in the context of rapid technological change. Teachers will participate in three intensive three-week online "sprints," an approach adapted from the technology sector, working in collaborative groups to investigate authentic instructional challenges, experiment with AI-enabled STEM tasks, refine materials for classroom use, and co-create frameworks for responsible AI use in STEM teaching and learning. In the final sprint, teachers will co-design and deliver workshops to bring what they have learned to other rural STEM teachers. Mixed-methods research will investigate how the model supports teachers’ sense of their own autonomy, competence, and connection to colleagues; how teachers engage with AI as they co-create authentic STEM learning opportunities; how students demonstrate discipline-specific habits of mind and responsible AI use; and which model components teachers perceive as most influential. Data collection will include adapted Technology-based Experience of Need Satisfaction surveys, rubric-based analysis of teacher-created artifacts, interviews, and focus groups. Quantitative data will be analyzed using hierarchical linear modeling, and qualitative data through thematic analysis. The project will contribute new knowledge about how sprint-based professional learning structures support teachers during rapid technological change and will produce asynchronous professional development modules, implementation guides, and teacher-led workshops. 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.