Comparative policy regimes of AI pedagogy: state-platform, market-ecosystem and rights-regulation
Minjuan Wang, Chris Dede, Chi-Kin John Lee, Xuefan LiPurpose
This article examines how education systems govern AI pedagogy after the diffusion of generative AI. Moving beyond AI literacy, it conceptualizes “governing AI pedagogy” as the organization of learning about AI (AI as content) and with AI (AI as tool–tutor), alongside treating AI as a governance object. The study develops three ideal-typical regimes–state–platform, market–ecosystem and rights–regulation–to compare how policy, platforms and classroom guidance shape legitimate uses of AI in schooling across national contexts.
Design/methodology/approach
The study conducts a small-N comparative documentary analysis of post-2023 AI-related policy, curriculum, legal and platform documents from the Chinese Mainland, Hong Kong, the USA, the European Union, Finland and France. A 3×3 operational matrix codes AI as content, tool–tutor and governance object across system, platform and classroom levels. Within-regime reconstruction and cross-regime comparison identify patterns of alignment and misalignment between policy visions and platform infrastructures.
Findings
Findings show that regimes distribute AI roles differently. State–platform systems tightly align policy and infrastructure, privileging centralized control. Market–ecosystem systems enable rapid tool diffusion but uneven governance coherence. Rights–regulation systems foreground risk, rights and safeguards, structuring cautious adoption. Across regimes, AI pedagogy is shaped less by technology availability than by how authority, incentives and accountability are configured across policy–platform–classroom chains.
Practical implications
Effective governance should be goal-first, augmentation-oriented and lifecycle-based.
Originality/value
Introduces a comparative regime framework for governing AI pedagogy beyond the literacy discourse.