Policy analysis reveals shifting federal authority toward science and innovation in congressional AI education proposals, highlighting workforce-driven governance over classroom instruction.
This study explores U.S. congressional proposals on artificial intelligence in education (AIED) from 2016 through early 2026, treating them as an underexamined site of federal policy design. The study analyzes AIED bills across the P-16 continuum to examine how Congress frames AI education through capacity building, coordination, and workforce-oriented logics rather than instructional governance. Drawing on a policy design framework, it identifies four legislative clusters and demonstrates how agenda-setting delineates the institutional boundaries of federal AIED governance. The findings suggest that federal authority is shifting from education policy venues toward science and innovation domains amid technological uncertainty and educational federalism.
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Yeon et al. (2026) studied this question.
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