AI Diffusion Gaps: Unequal Integration of AI Across K-12 Schools

Christopher Campos and John Singleton

No 35347, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Although use of generative AI tools has quickly become widespread in education settings, emerging evidence suggests that effects on learning will depend on how that use is supported and guided. This paper reports findings from an original national survey of K-12 school principals designed to measure institutional integration of AI in schools through policies, teacher training, guidance for student use, leadership engagement, and the availability of AI-enabled tools. We find that AI use has spread rapidly across schools, largely as a productivity aid. Students mainly use AI for homework help and writing, while educators primarily use it for lesson planning and administrative tasks. The development of teacher training, guidance, and school policies has lagged adoption. We next document two diffusion gaps across schools: First, lower AI integration is associated with a higher share of disadvantaged students (a one standard deviation increase in disadvantage is associated with a 0.07-0.11 SD lower score on an index of AI integration); Second, private and charter schools score 0.23-0.44 SD lower on the AI integration index than traditional public schools. Although several surveyed school-level factors strongly predict AI integration, they do little to explain these gaps. Differences in district size account for roughly one-third of the disadvantage gap between public schools. These findings suggest that the factors associated with greater AI integration differ from those needed to narrow disparities in how schools support and guide AI use.

JEL-codes: I21 O30 O32 (search for similar items in EconPapers)
Date: 2026-06
Note: ED LS PR
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