Policy Silence as Governance: AI Decision-Making, Responsibility, and Risk in K–12 Education
TL;DR
Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined.
Abstract
This study examines how policy silence functions as a governance mechanism in the context of generative artificial intelligence (AI) in K–12 education. While existing research has focused on how districts regulate or integrate AI, far less attention has been given to what happens when formal guidance is delayed, incomplete, or unresolved. Drawing on a single-district qualitative case study of a Texas public school district, the analysis uses interviews and document analysis as primary data sources, while student survey data provide descriptive context regarding patterns of AI access and guidance. Findings show that policy silence actively redistributes interpretive authority to educators and school leaders, shifting responsibility for ethical and instructional decision-making onto individual classrooms without corresponding institutional support. This redistribution produces uneven enactment and may contribute to disparities in student access, guidance, and learning opportunities. Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined. Equitable AI integration requires policies that pair clarity with support for professional judgment, positioning AI governance as central to contemporary school improvement.