AI competency frameworks: implications for institutional accountability

In examining AI competency frameworks: implications for institutional accountability, for the policy position, the instrument should be used to identify the intended direction, the actors addressed and the implementation measures that remain necessary.

In examining AI competency frameworks: implications for institutional accountability, for AI competency frameworks, international artificial-intelligence competency frameworks for students and teachers were released in September 2024. They organise capability around human-centred understanding, ethics, techniques and application, with teacher responsibilities also covering pedagogy and professional development. Competency frameworks guide curriculum and workforce planning; they do not establish that competence has been achieved without suitable learning and assessment evidence.

Policy context for AI competency frameworks

In examining AI competency frameworks: implications for institutional accountability, review of the arrangements should follow a stated and reproducible method.

The principal risks in relation to implementation are unverified outputs entering teaching or assessment, unclear responsibility between providers and suppliers, opaque use of personal or inferred data, and loss of meaningful human review. For decisions concerning AI competency frameworks, a weakness in one part of the control environment may obscure a related failure elsewhere.

Controls for AI competency frameworks

Relevant evidence for AI competency frameworks will normally include supplier change and incident records, records of human review and overrides, pre-deployment and periodic performance testing, an inventory of systems and their intended uses, and learner information and accessible challenge routes. Currency, provenance and representativeness should be established before evidence is used for assurance. Across the defined scope, conflicting records require reconciliation before a complete assurance conclusion is reached.

For AI competency frameworks, accountability and effective correction both depend on a record that can be followed from evidence to decision. For the issue, the responsible body should be able to identify the evidence considered, the judgement made, the person or body authorised to make it and the action that followed.

• Notify users of material limitations before using it to determine a learner or provider outcome.
• Prohibit uses for which evidence or authority is insufficient.
• Control personal and confidential information before using it to determine a learner or provider outcome.
• Review incidents and supplier changes before using it to determine a learner or provider outcome.
• Classify uses by effect on learners.

Review of AI competency frameworks

In examining AI competency frameworks: implications for institutional accountability, for the arrangements, oversight should test whether formal commitments are reflected in decisions, resource allocation, provider conduct and accessible routes for review.

Review of the measure should map the complete process, identify the intended result and responsible authority at each stage, and test normal cases together with exceptions. For AI competency frameworks, an isolated incident and a recurring or systemic condition require different findings and responses.

For decisions concerning AI competency frameworks, the implementation record for the policy position should identify the instrument being applied, its status, the competent authority, the affected jurisdiction and the action expected of each responsible body.

Public reporting on implementation should distinguish established fact, analytical judgement and planned action. For AI competency frameworks, material revisions should be traceable to their reason and effective date.

Implications for AI competency frameworks

Neither one indicator nor one control can establish the complete position on the policy position. For AI competency frameworks, a reasoned conclusion should reconcile the governing requirement, evidence of operation, learner outcomes and residual risk, and remain open to better evidence.