Research.
As the benchmark develops, aggregate evidence can help build a clearer picture of school AI readiness. Findings need suitable cohorts and clear limitations.
The readiness picture.
The founding cohort is a starting point for building an evidence base. Any future aggregate findings need enough suitable data and a clear explanation of which schools are represented.
A pilot cohort is not automatically representative of all schools. More data does not automatically produce better recommendations or prove why a gap exists.
The anchor library.
Benchpoint aligns to established global and regional frameworks. The anchor library maps our standard back to the prevailing statutory guidance and best practice frameworks, creating a unified cross-reference.
| Code | Framework Reference |
|---|---|
| G1 | OECD / EC AILit Framework |
| G2 | UNESCO AI Competency Framework for Teachers |
| G3 | UNESCO AI Competency Framework for Students |
| G4 | UNESCO Guidance for Generative AI in Education |
| G5 | ISO/IEC 42001:2023 |
| G6 | WEF Education Readiness for the Age of AI |
| G7 | OECD Digital Education Outlook 2026 |
| G8 | UNCRC General Comment No. 25 |
The annual readiness report.
Published each year from the first full cycle. A comprehensive analysis of the state of AI readiness, common pitfalls, and emerging best practices identified across all assessed organizations.