Invisible learners: An analysis of state K-12 artificial intelligence governance and English learner equity

The rapid integration of Artificial Intelligence (AI) into K-12 education creates a critical juncture for equity, coinciding with the growth of English Learners (ELs). This study analyzes how emerging state-level AI governance addresses this convergence. Through a qualitative analysis of 28 guidance documents issued by 27 state education agencies and the U.S. Department of Education, this paper investigates how ELs’ needs are represented. Findings show that state guidance offers limited EL specific direction and instead concentrates on academic integrity, privacy, and general ethics. When documents mention ELs, they tend to position them as translation use cases or as groups exposed to algorithmic risk, while offering little connection to research-based EL instruction and assessment. The study concludes by urging policymakers to adopt linguistically responsive AI governance to reduce the risk that early AI policies leave EL relevant instructional, assessment, and access questions underdeveloped in state guidance documents, which can increase equity risks in policy framing for ELs.