"AAB AI Education Evidence Registry Dataset v0.1: Cases, Pilots, Frameworks, Initiatives, Policies and Community Signals for AI Literacy Education"

Abstract

"This dataset package provides a versioned AI education evidence infrastructure release from the AI Assessment Board (AAB). It includes structured datasets and supporting documentation related to global AI education and AI literacy, including documented cases, pilot implementations, international initiatives, framework crosswalks, free education resources, standards drafts, consensus materials, research briefs, and policy briefs.The dataset is designed to support comparative research, standards development, policy analysis, and public-interest documentation of AI literacy education across K\u201312, higher education, community learning, workforce training, teacher professional development, and physical AI or robotics-enabled learning contexts. Records may include information such as country or region, implementing organization type, target learners, educational setting, AI education focus, pedagogy, AI tool role, evidence type, safeguards, observed outcomes, limitations, source references, and provisional Evidence Maturity Index (EMI) classification where applicable.This release also includes global AI education and AI literacy initiatives from governments, nonprofit organizations, universities, industry partners, community organizations, and education networks. These initiative records are intended to help researchers and policymakers understand how AI literacy efforts are emerging across different regions, institutional settings, and implementation models.The package does not contain raw student data, personally identifiable learner information, private assessment records, or confidential institutional data. Inclusion of a case, pilot, framework, initiative, resource, or document in this dataset does not imply endorsement, certification, ranking, accreditation, or product recommendation by AAB. The purpose of the dataset is to preserve structured public evidence, improve transparency, support cross-context comparison, and contribute to future standards development in AI education and AI literacy."