Case Registry
Documents real-world AI education implementations, including context, constraints, process, outcomes, and lessons learned.
About AAB
The AI Assessment Board is an independent nonprofit evidence and standards initiative. AAB documents real AI education practice, preserves implementation evidence, and helps governments, institutions, researchers, educators, and communities compare what is happening across settings.
AI education is expanding rapidly in K-12 classrooms, after-school programs, libraries, higher education, workforce training, and government literacy initiatives. Access is no longer the only challenge. The harder public question is how to judge quality, safety, evidence, and trust when market narratives move faster than shared evaluation systems.
AAB responds to that governance gap by creating structured public memory: documented cases, documented pilots, comparative frameworks, and evidence signals that can be reviewed over time.
AAB's public platform is organized around documentation, not marketing. Records can include successes, failures, constraints, risks, and open questions.
Documents real-world AI education implementations, including context, constraints, process, outcomes, and lessons learned.
Preserves early-stage experiments with objectives, evaluation design, risk mitigation, and observed implementation signals.
Identifies recurring patterns across documented records, including enabling conditions, safety practices, and assessment signals.
AAB uses registry records, pilot notes, policy scans, frameworks, community signals, and research briefs to identify recurring patterns. Those patterns may inform consensus reports, framework crosswalks, draft standards, and public consultation.
The process is intentionally gradual. Evidence maturity matters: early documentation is treated differently from repeated, reviewed, and independently supported evidence.