What it is for
The Standard says what AI literacy is. The Competency Framework turns that definition into concrete, observable descriptors that people can design programs and assessments against. It elaborates the Standard; it does not change it. The five components, sixteen sub-competencies and three levels set out in the Standard remain the normative foundation, and the framework must stay consistent with them.
Five roles
The framework gives a full set of descriptors for each of five stakeholder roles. The roles are archetypes, not job titles, and one person can hold more than one.
| Role | Focus of the descriptors |
|---|---|
| Learner | AI literacy in education, from school through higher education and lifelong learning |
| Educator | Teaching, curriculum development and student guidance |
| Researcher | Research, scientific inquiry and knowledge advancement |
| Clinician / health care provider | Health care delivery, patient care and clinical decision-making |
| Administrator | Operations, administrative processes and workplace productivity |
How it is structured
For every role, the framework has one table per component (TEC, EVA, PRA, ETH, DAT). Each row is a sub-competency, such as TEC.1 Conceptual Awareness or DAT.4 Data Visualization and Communication, and each column is a level: Foundation (L1), Intermediate (L2) and Advanced (L3). Each cell lists a small number of observable behaviors.
The sub-competency codes stay the same across roles. What changes is the domain language, the scope of impact expected and the professional setting. A clinician and a researcher are both assessed on data analysis, for example, but the evidence of competence looks different in a clinic and in a laboratory.
The framework closes with an assessment section. It describes four complementary approaches (self-assessment questionnaires, practical tasks, portfolios, and peer and expert review) and states that placement at a level should rest on convergent evidence from several sources, not on a single instrument score.
How to use it
- Program designers use the descriptors to write learning objectives, map existing curricula and find gaps. Not every sub-competency needs equal weight in a program; decisions on emphasis and sequencing follow the Implementation Pathway Model.
- Assessment developers use the level descriptors to build performance indicators, rubrics and instruments.
- Institutional leaders use it to understand the scope of AI literacy expected across roles.
Adopters are encouraged to record which version of the framework their work was based on. The full descriptor tables are long and are not reproduced on this site.
Version history
Version 1.0 (2024) introduced the role-specific descriptors. Version 2 (February 2026) aligned them with ALiF Standard v1.0r1, standardized the sub-competency codes (TEC.1 to DAT.4) and added the assessment section. It is listed in the ecosystem inventory as Core, active, with expert review as its validation basis.