Qualitative analysis investigates AI education guidance across 35 states, highlighting policy implications for innovation and risk.
As generative AI reshapes K-12 education, U.S. states are rapidly developing guidance to navigate its implementation. This study investigates the structural composition of AI education guidance across thirty-five states through qualitative document analysis. Utilizing the CAPE framework and the concepts of policy resilience versus fragility, the research examines how state-level signals influence systemic preparedness for AI education. Findings reveal a landscape of structural fragmentation, where guidance often prioritizes risk mitigation over pedagogical innovation. These early policy choices have potential to shape teacher capacity and sustainability, determining whether AI fulfills its promise of empowerment or widens existing education opportunity gaps.
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McCune et al. (2026) studied this question.
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