{"schema_version":"ICEQC_NEWS_ARTICLE_V1","id":"iceqc-news-c9dc919c16455956","slug":"2024-09-16-record-integrity-in-relation-to-ai-competency-frameworks","language":"en","publication_status":"READY_FOR_IMPORT","publication_date":"2024-09-16","last_modified_date":"2024-09-16","title":"Record integrity in relation to AI competency frameworks","summary":"Clarifies the practical meaning of record integrity in relation to AI competency frameworks and the evidence required to support a reliable assurance conclusion.","category":{"code":"STANDARDS_INTERPRETATION","label":"Standards Interpretation"},"article_type":"Standards interpretation","publisher":"International Council for Education Quality Certification (ICEQC)","jurisdictional_scope":"International","historical_reference_basis":"International frameworks for students and teachers released in 2024","reference_authority":"United Nations Educational, Scientific and Cultural Organization","sections":[{"heading":null,"paragraphs":["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. Oversight of the control should reflect the principle that 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 the relevant requirement, the public interest is not confined to institutional compliance. A decision concerning the assurance question should recognise that technology may support teaching, administration and access, but consequential educational decisions must remain accountable, explainable and open to effective review."]},{"heading":"Scope and application of record integrity in relation to AI competency frameworks","paragraphs":["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 instrument identified by the international frameworks for students and teachers released in 2024 provides a formal policy reference for the control. In relation to record integrity in relation to AI competency frameworks, this distinction protects learners from overstated claims and enables providers to plan against a defined obligation.","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.","In practical terms, the stated expectation should be reviewed against a stated method rather than general assurance. Oversight of the relevant requirement should reflect the principle that a reliable record should identify what occurred, when it occurred, who was responsible, the authority for the action and any later correction. Records should remain protected against unauthorised alteration while legitimate amendments remain visible.","Risk assessment of the relevant requirement 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."]},{"heading":"Evidence required","paragraphs":["Responsibility for record integrity in relation to AI competency frameworks should be visible at the point where consequential decisions are made. Oversight of the assurance question should reflect the principle that 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 the assurance question involves partners, suppliers or several public bodies, responsibility should be mapped across the complete service. In relation to record integrity in relation to AI competency frameworks, agreements 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.","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 should be assessed against the information then available, with later amendments separately dated and explained."],"bullets":["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."]},{"heading":"Decision criteria and exceptions","paragraphs":["The review method for record integrity in relation to AI competency frameworks should be reproducible. For the issue under review, 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.","The final record on the relevant 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. The analysis of the relevant requirement proceeds on the basis that 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. A decision concerning the issue under review should recognise that an isolated example cannot establish consistent operation, and an isolated failure should be evaluated for materiality, recurrence and systemic effect. Limitations should be prominent wherever the finding may influence a consequential decision.","The appropriate response to the stated expectation is therefore one of controlled implementation and review. The objective should be explicit, the evidence proportionate and learner impact visible. An evidential gap should lead to a qualified conclusion and continued action, not administrative closure."]}],"word_count":872,"content_hash":"sha256-02e476b9d0bf1c0550e9cce5a539edc684ddf2d39c4f52a3bd94e2d100183d26","seo_keywords":["record integrity in relation to AI competency frameworks","education quality standards","education quality","ICEQC"],"schema_type":"TechArticle"}