Enacting Coding and Artificial Intelligence Education Policy in Elementary Schools

The integration of coding and artificial intelligence (AI) education in elementary schools has emerged as a global policy priority, yet significant gaps persist between policy formulation and classroom implementation, particularly in developing countries. This qualitative collective case study examines how elementary schools in Grobogan District, Indonesia enact the national coding and AI education policy introduced through the Merdeka Curriculum. Drawing upon policy enactment theory, TPACK framework, and computational thinking concept, data were collected through in-depth interviews with teachers (n=8), principals (n=4), and students (n=12), classroom observations of six learning sessions, and document analysis. Four distinct implementation models emerged: integrated project-based plugged coding, hybrid unplugged-plugged activities, subject-integrated unplugged approaches, and introductory demonstrations. Policy enactment was incremental and contingent upon school leadership and "champion" teachers. Teachers' TPACK readiness varied considerably, with technological knowledge representing the primary constraint. Despite infrastructural limitations, schools developed creative local adaptation strategies. Students demonstrated emergent computational thinking elements through trial-and-error activities, collaborative debugging, and systematic problem decomposition. Successful policy implementation requires sustained teacher capacity building, instructional leadership, and district-level support systems beyond regulatory frameworks. These findings offer evidence-based recommendations for narrowing the policy-practice gap in coding and AI education within resource-constrained contexts.