We measure capability, then close the gap

Why measurement comes first

Training is easy to sell and hard to justify. An organization that cannot say what its people could do before cannot say what changed afterward.

So we lead with a validated instrument rather than a catalog. The assessment tells you where people actually stand against a published international standard. The training closes the specific gap the assessment found. Reassessment shows whether it worked.

What we stand on

Every track of the AI Competency Assessment is content-validated before publishing by an independent panel of at least six subject matter experts, who score each item on relevance and clarity using the Content Validity Index, accepted at a scale-level S-CVI/Ave of 0.90 or above, following Lynn (1986) and Polit and Beck (2006). Results are reported against the UNESCO AI Competency Frameworks, the UNESCO Recommendation on the Ethics of Artificial Intelligence, the AI Fluency 4D Framework, and the U.S. Department of Labor AI Literacy Framework, plus the professional body guidance specific to each track.

Content validity is one psychometric property, not the whole set. Reliability and construct validity require accumulated response data. We do not describe the instrument as psychometrically validated, because it is not yet, and the buyers who matter can tell the difference.

How programs are built

Delivered in cohorts, in person, spread over weeks rather than compressed into a day. Participants work on their own material. Completion is on submitted work rather than attendance, and outcomes are verified by inspecting what people produced.

Those are not stylistic preferences. Duration, job-embeddedness, collaboration and feedback are what education regulators test for when they accredit professional development, and they are the things a self-paced online module cannot claim.