Record integrity in relation to AI competency frameworks

For AI competency frameworks, for the applicable requirement, the public interest is not confined to institutional compliance.

Scope and application of record integrity in relation to AI competency frameworks

This may require data provenance and access controls, learner information and accessible challenge routes, an inventory of systems and their intended uses, and pre-deployment and periodic performance testing, supported by supplier change and incident records and documented authority for each consequential use.

The international frameworks for students and teachers released in 2024 provides a policy reference for the control. In the context of AI competency frameworks, this distinction protects learners from overstated claims and enables providers to plan against a defined obligation.

When examining 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.

Review of the applicable expectation should follow a stated and reproducible method. For AI competency frameworks, records should remain protected against unauthorised alteration while legitimate amendments remain visible.

Risk assessment should give particular attention to unverified outputs entering teaching or assessment, automation bias in consequential decisions, and unclear responsibility between providers and suppliers. A provider should also consider unequal performance across learner groups and opaque use of personal or inferred data.

Controls relevant to record integrity in relation to AI competency frameworks

Where responsibilities for delivery relating to AI competency frameworks are shared with partners, suppliers or several public bodies, responsibility should be mapped across the complete service. Agreements governing AI competency frameworks should allocate information exchange, incident escalation, learner communication, record custody and corrective authority.

For AI competency frameworks, traceability is necessary for accountable decision-making and fair correction. For the control, 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. Historical decisions concerning AI competency frameworks should be assessed against the information then available, with later amendments separately dated and explained.

• Control personal and confidential information.
• Test performance across relevant groups.
• Retain accountable human decision-makers.
• Prohibit uses for which evidence or authority is insufficient.
• Classify uses by effect on learners.

Review criteria for record integrity in relation to AI competency frameworks

For the matter, the reviewer should specify mandatory fields, source ownership, access rights, retention and correction procedures. Test a sample from creation through use, amendment, reporting and disposal, including records created during disruption or by a delivery partner.

For decisions concerning AI competency frameworks, the final record on the applicable requirement should identify the applicable expectation, the relevant scope, the evidence examined, the sampling basis, material exceptions and the reason for the conclusion.

Particular care is required when interpreting evidence about the control. For AI competency frameworks, a technical capability is not evidence that a use is educationally justified. Across the defined scope, limitations should be prominent wherever the finding may influence a consequential decision.

The objective for AI competency frameworks should be explicit, the evidence proportionate and learner impact visible. An evidential gap in relation to AI competency frameworks should lead to a qualified conclusion and continued action, not administrative closure.