{"schema_version":"ICEQC_NEWS_ARTICLE_V1","id":"iceqc-news-1bc0a0f1a271fd85","slug":"2025-06-24-internal-review-of-implementation-of-ai-literacy-obligations","language":"en","publication_status":"PUBLISHED","publication_date":"2025-06-24","last_modified_date":"2025-06-24","title":"Internal review of implementation of AI literacy obligations","summary":"Describes how improvement in implementation of AI literacy obligations can be planned, measured and maintained through clear responsibility and follow-up evidence.","category":{"code":"QUALITY_IMPROVEMENT_METHODS","label":"Quality Improvement Methods"},"article_type":"Quality improvement method","publisher":"International Council for Education Quality Certification (ICEQC)","jurisdictional_scope":"International","historical_reference_basis":"Regional artificial intelligence rules applicable from February 2025","reference_authority":"Relevant public authorities and official international sources","sections":[{"heading":null,"paragraphs":["In 2025, consideration of implementation of AI literacy obligations must take account of the regional artificial intelligence rules applicable from February 2025 and the responsibilities it places before education systems. A disciplined improvement process separates immediate containment from corrective action directed at the underlying cause. The effect on learner access and reliable decision-making should inform the scale of control applied.","A proper review the issue under review should establish the intended outcome before selecting controls or indicators. For implementation of AI literacy obligations, a complete improvement record should define the baseline, affected scope, causal hypothesis, responsible owner, resources, milestones and measures of effectiveness. In the context of implementation of AI literacy obligations, suitability, authorised variation and the date for reconsideration should be established when the arrangement is approved."]},{"heading":"Improvement objective and baseline","paragraphs":["When examining implementation of AI literacy obligations, the applicability described by the regional artificial intelligence rules applicable from February 2025 changes the implementation context for implementation of AI literacy obligations. Entry into force or applicability establishes an operative reference point, but the resulting duties must still be traced to the persons, services and jurisdictions covered. Authorities should distinguish immediate duties from staged provisions, and providers should retain the legal and operational basis for any conclusion about application.","Review is independent when the reviewer is sufficiently separate from the design, operation and approval of the matter to reach and report an impartial conclusion. Organisational location alone does not establish independence. For decisions concerning implementation of AI literacy obligations, a formally complete record is not reliable if its scope or measure does not correspond to the decision being made.","Failure in relation to the issue under review may arise even where the stated policy is reasonable. Material concerns include unverified outputs entering teaching or assessment, loss of meaningful human review, opaque use of personal or inferred data, and unclear responsibility between providers and suppliers. In work concerning implementation of AI literacy obligations, an exception should be assessed by effect, duration, recurrence and reach, including possible exposure beyond the initial sample.","Relevant evidence for the corrective programme will normally include documented authority for each consequential use, pre-deployment and periodic performance testing, learner information and accessible challenge routes, records of human review and overrides, and data provenance and access controls. As regards implementation of AI literacy obligations, contradictory evidence should be investigated and resolved, not omitted from the record."]},{"heading":"Controls and accountable action","paragraphs":["The review method for implementation of AI literacy obligations should be reproducible. The method for the intervention is to define the review question and criteria, record competence and conflicts, preserve access to relevant evidence, and protect the reviewer’s ability to report adverse findings. Assign acceptance of residual risk to an authority outside the reviewed activity. Where implementation of AI literacy obligations is under review, documentation should be sufficient to reconstruct the judgement without relying on unrecorded explanation.","The assurance record for the improvement objective should retain the date of the evidence, the source responsible for it, the scope examined and the version of any instrument or definition applied. For the assessment of implementation of AI literacy obligations, this enables later review to separate substantive change from correction, reclassification or expanded coverage. For implementation of AI literacy obligations, revision should not remove an earlier conclusion from the record where reliance has occurred."],"bullets":["Who decides the response?","Can adverse findings be reported without alteration?","Who designed and operates the control?","Is competence established?","Does the reviewer have a relevant conflict?"]},{"heading":"Evidence of effect","paragraphs":["Where implementation of AI literacy obligations involves partners, suppliers or several public bodies, responsibility should be mapped across the complete service. In the context of implementation of AI literacy obligations, governance between participating bodies should make information duties and corrective authority explicit. Learner safeguards associated with implementation of AI literacy obligations should remain continuous where provision is delivered by several bodies.","The improvement record for the issue under review should contain the verified problem, affected scope, immediate containment, causal analysis, selected intervention, accountable owner, resources, milestones and effectiveness measure. Reporting should distinguish work performed from the outcome demonstrated after implementation. Closure reporting should not obscure unresolved action or risk retained by the responsible authority."],"bullets":["Prohibit uses for which evidence or authority is insufficient.","Retain accountable human decision-makers, identifying the accountable function and affected scope.","Control personal and confidential information.","Test performance across relevant groups, with responsibility, scope and timing recorded.","Classify uses by effect on learners before using it to determine a learner or provider outcome."]},{"heading":"Sustaining improvement","paragraphs":["The quality significance of implementation of AI literacy obligations follows from a basic distinction between availability and effective provision. Technology may support teaching, administration and access, but consequential educational decisions must remain accountable, explainable and open to effective review. For decisions concerning implementation of AI literacy obligations, assurance should follow the learner journey and test more than a single access point or aggregate result.","Interpretation of the improvement objective should not extend beyond the population, period and setting examined. A technical capability is not evidence that a use is educationally justified. In work concerning implementation of AI literacy obligations, accuracy measured in one setting may not transfer to another population, language, curriculum or decision context. As regards implementation of AI literacy obligations, methods should be proportionate to the significance and recurrence of the problem; low-risk local issues and systemic learner-protection failures require different levels of control. Where implementation of AI literacy obligations is under review, decision-makers and affected users should receive the conclusion together with its material evidential limits.","A complete conclusion on the improvement objective requires evidence extending beyond an individual measure or safeguard. A conclusion concerning implementation of AI literacy obligations should be revised when stronger evidence materially changes the assessment of implementation, outcome or risk."]}],"word_count":945,"content_hash":"sha256-016f05975583124a50736731269eaf3e8cb574b205ea1ea11189bac9896980da","seo_keywords":["internal review of implementation of AI literacy obligations","education quality improvement","education quality","ICEQC"],"schema_type":"TechArticle","related_resources":[{"label":"Evidence and remote assessment","path":"/certification/evidence-and-remote-assessment"}]}