#496: Coding Foundations, AI Futures: How Districts Built K-12 AI Capacity Through Collaborative Peer Networks

Artificial intelligence (AI) education builds on core computational thinking (CT) practices such as abstraction, algorithms, and data. Yet most research to date over-emphasizes classroom tools rather than how schools can more systematically implement AI efforts. This qualitative study examines how six U.S. school districts extended K-12 CT pathways to build AI capacity through peer-networks. Guided by Coburn’s theoretical framework, depth, spread, shift in ownership, and sustainability, we followed districts over one year as they navigated AI implementation. Data sources included monthly cohort recordings, district artifacts, district presentations, and a questionnaire. Findings indicated that districts leveraged existing CT practices for depth, launching both AI/data science courses and integrative activities. Spread occurred through cross-district learning, policy development, and professional growth. The cohort catalyzed a shift in ownership through district-led AI efforts. Last, existing CT efforts coupled with peer-networks proved crucial for sustainability of scalable and coherent, district-driven AI education.