From Articulation to Enforcement: District Governance of Generative AI Under Policy Ambiguity in Chicago Public Schools

Districts must govern generative artificial intelligence (GenAI) in K–12 education without binding federal or state mandates. This study examines how Chicago Public Schools (CPS) publicly articulated a district-level GenAI governance model during 2023 to 2025, through document analysis and policy tracing. Analysis is guided by equity-anchored implementation, a construct linking implementation theory and data justice: equity commitments endure in proportion to the degree and focus of their institutional embedding. CPS articulated flexible guidance, human oversight, vendor accountability, and prohibitions on high-risk uses, alongside an articulation-enforcement gap: commitments are detailed, but monitoring, enforcement, and differentiated supports are less documented. A bounded comparison with New York City, Philadelphia, and Miami-Dade shows that institutional anchors narrow gaps only in domains they directly govern. It reframes policy ambiguity as a condition for governance articulation, distinguishes equity as harm prevention from capacity distribution, and concludes that AI governance should be judged by institutional capacity, not policy sophistication.