Chaeyeon Lim

Topic: The Meta-Level Gap in Generative AI Competency Frameworks. Specifying Skills and Co-Creat ing Regulatory Capacity for AI in Education

Objective: Develop an analytical framework that maps the complex intersections of AI governance, ethical considerations, and equitable digital transformation in educational systems, with particular attention to both established and emerging policy responses.

Outcome: Policy recommendations, comparative analyses of international approaches, or a comprehensive assessment tool that policymakers can utilise to evaluate AI integration against equity, inclusion and accessibility benchmarks.

Current AI competency frameworks conceptualise competency as individual skill acquisition (i.e., what a learner can do with AI). However, AI-mediated learning involves distributed regulatory processes across individual and social levels. Current frameworks inadequately address this distributed nature, and policy evaluation tools lack procedural indicators that capture how competency is exercised, not merely whether predefined outcomes are achieved. This project conducts a comparative policy analysis of 2-3 major GenAI competency frameworks, identifies gaps in how they address distributed regulatory processes, and proposes conceptual prototype indicators for assessing regulatory capacity rather than skill endpoints.