News
AABoard publishes six AI education registry datasets on IEEE DataPort
May 2026
AABoard has published six AI education registry datasets on IEEE DataPort, creating citable research infrastructure for studying AI literacy standards, evidence, assessment, initiatives, frameworks, pilots, and implementation cases.
The datasets extend AABoard's public evidence work by giving researchers, policy analysts, standards writers, and institutional partners stable dataset references for registry-based analysis. Each dataset is designed for descriptive and comparative use: it helps users map evidence, identify patterns, compare claims, and preserve source trace across public records.
Six citable datasets
- AI Education Evidence Registry Dataset: Cross-collection registry data connecting cases, pilots, frameworks, and initiatives. DOI: 10.21227/fb0t-1b14
- Global AI Literacy Assessment and Credentialing Registry Dataset: Assessment and credentialing records for comparing AI literacy claims and evidence boundaries. DOI: 10.21227/3qba-1x45
- Global AI Education Initiatives Registry Dataset: Public-interest map of AI education initiatives, partnerships, sectors, and implementation signals. DOI: 10.21227/ts1e-m423
- AI Literacy Framework Crosswalk Dataset: Alignment layer for comparing AI literacy frameworks, competency models, and standards references. DOI: 10.21227/yqrv-rs33
- AI Education Pilot Registry Dataset: Structured pilot records covering implementation phases, pilot types, safeguards, and evidence artifacts. DOI: 10.21227/stq6-yw93
- AI Education Case Registry Dataset: Curated case records for studying real-world AI education practices and source-traced evidence. DOI: 10.21227/z095-8k08
Why this matters
AI literacy is increasingly discussed in education, workforce development, public policy, and institutional governance, but the evidence base remains fragmented. AABoard's registry datasets are intended to make that evidence more discoverable, comparable, and reusable.
The dataset series supports a standards-development workflow in which public claims about AI literacy can be checked against documented cases, pilots, framework language, assessment models, policy signals, and implementation context.
Responsible use
The datasets should be interpreted as registry evidence infrastructure, not as certification, ranking, or endorsement of any tool, provider, program, organization, or instructional approach. Impact claims still require record-level review of the underlying source evidence.
AABoard will continue refining registry records, evidence fields, source trace, and crosswalk structures as the field develops.