Explains record integrity in relation to AI competency frameworks, covering scope, evidence, decision authority, material exceptions and continuing assurance.
In 2024, consideration of record integrity in relation to AI competency frameworks must take account of the international frameworks for students and teachers released in 2024 and the responsibilities it places before education systems. Consistent application requires a clear distinction between the required result, recommended methods and examples that may assist implementation. Suitability should be judged within the relevant system rather than against a presumed universal administrative model.
For AI competency frameworks, for the applicable requirement, the public interest is not confined to institutional compliance. Technology may support teaching, administration and access, but consequential educational decisions must remain accountable, explainable and open to effective review.
Scope and application of record integrity in relation to AI competency frameworks
The evidential record for record integrity in relation to AI competency frameworks should permit a reviewer to trace the matter from decision to outcome. 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 be based on a stated method rather than general assurance. A reliable record should identify what occurred, when it occurred, who was responsible, the authority for the action and any later correction. 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.
Evidence required
For decisions concerning AI competency frameworks, responsibility should be identifiable at the point where consequential decisions are made. Evidence is sufficient when it is current, attributable, representative of the relevant scope and capable of being reconciled with other available records. Escalation should follow whenever the available record cannot support a safe conclusion for the affected learners.
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. Protection should operate across the complete service, irrespective of how delivery is divided.
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.
Decision criteria and exceptions
The review method for record integrity in relation to AI competency frameworks should be reproducible. 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. A competent reviewer should be able to follow the record from source selection to conclusion and exception handling.
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. Equivalent methods should be assessed by demonstrated result, with the basis for acceptance retained. A limitation preventing a complete conclusion should remain visible and unresolved until suitable evidence is obtained.
Particular care is required when interpreting evidence about the control. In work concerning AI competency frameworks, a technical capability is not evidence that a use is educationally justified. Accuracy measured in one setting may not transfer to another population, language, curriculum or decision context. An isolated example cannot establish consistent operation, and an isolated failure should be evaluated for materiality, recurrence and systemic effect. Within the scope under review, 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.