{"schema_version":"ICEQC_NEWS_ARTICLE_V1","id":"iceqc-news-809a952498032fd9","slug":"2019-06-26-reporting-artificial-intelligence-in-education-policy-coverage-and-revision-risk","language":"en","publication_status":"READY_FOR_IMPORT","publication_date":"2019-06-26","last_modified_date":"2019-06-26","title":"Reporting artificial intelligence in education policy: coverage and revision risk","summary":"Reviews reporting artificial intelligence in education policy: coverage and revision risk and identifies the conditions required for sound interpretation, responsible.","category":{"code":"DATA_RESEARCH","label":"Data Research"},"article_type":"Data and research analysis","publisher":"International Council for Education Quality Certification (ICEQC)","jurisdictional_scope":"International","historical_reference_basis":"Beijing Consensus adopted in May 2019","reference_authority":"United Nations Educational, Scientific and Cultural Organization","sections":[{"heading":null,"paragraphs":["In 2019, consideration of artificial intelligence in education policy must take account of the Beijing Consensus adopted in May 2019 and the responsibilities it places before education systems. In reviewing the measure, the value of the present data lies in the questions it can answer reliably and in the limits it makes visible. The control response should be sufficient to protect learners while avoiding burdens not justified by the evidence.","The quality significance of the available evidence follows from a basic distinction between availability and effective provision. In reviewing the issue, technology may support teaching, administration and access, but consequential educational decisions must remain accountable, explainable and open to effective review. Assurance should follow the learner journey and test more than a single access point or aggregate result."]},{"heading":"Analytical scope","paragraphs":["Assurance of artificial intelligence in education policy should draw on more than one form of evidence. Useful records include data provenance and access controls, pre-deployment and periodic performance testing, records of human review and overrides, learner information and accessible challenge routes, and supplier change and incident records. In relation to reporting artificial intelligence in education policy, system-wide assurance cannot be inferred from a favourable case chosen after the event.","The formal status of the Beijing Consensus adopted in May 2019 should be preserved in any public account. In this case, the instrument should be used to identify the intended direction, the actors addressed and the implementation measures that remain necessary.","The Beijing Consensus on Artificial Intelligence and Education was adopted in May 2019. It addresses policy planning, management, teaching, learning, skills, lifelong learning, inclusion, gender equality, data and research. It promotes human-centred and equitable use rather than technology adoption as an end in itself. Authorities and providers should therefore connect each proposed use to an educational purpose, governance responsibility and evidence of benefit and risk.","A focused examination of the available evidence requires a clear analytical discipline. In reviewing the comparison, data quality comprises accuracy, completeness, timeliness, consistency and traceability. Strength in one dimension does not compensate automatically for weakness in another, particularly where the information informs a consequential learner decision.","Risk assessment of the analysis should give particular attention to unclear responsibility between providers and suppliers, opaque use of personal or inferred data, and automation bias in consequential decisions. A provider should also consider unverified outputs entering teaching or assessment and unequal performance across learner groups."]},{"heading":"Definitions and data coverage","paragraphs":["The governing expectation for artificial intelligence in education policy should be capable of consistent application. A decision concerning the comparison should recognise that reported averages should be accompanied by sufficient distributional information to identify material differences between learner groups, locations and forms of provision. Operational definitions should be precise enough to support consistent consequential decisions and explain justified variation.","Public reporting on the available evidence should distinguish established fact, analytical judgement and planned action. Revision history should remain available where users have relied on the earlier conclusion.","Records relating to the issue should preserve both the conclusion and its limits. The correction record should state what the new evidence changes and which earlier conclusions or decisions require review."],"bullets":["Classify uses by effect on learners.","Review incidents and supplier changes, with responsibility, scope and timing recorded.","Prohibit uses for which evidence or authority is insufficient.","Retain accountable human decision-makers.","Notify users of material limitations."]},{"heading":"Use of the findings","paragraphs":["Implementation of artificial intelligence in education policy can be tested without imposing unnecessary reporting. Review of the analysis should trace selected records to source, reconcile totals across systems, quantify missing and late submissions, review manual adjustments and retain a revision history. Escalate discrepancies that could alter a published conclusion or individual outcome. In relation to reporting artificial intelligence in education policy, the assurance record may draw on existing sources, provided their limitations and fitness for the current purpose are examined.","Decision-makers using evidence on the comparison should be told what the data cannot establish as clearly as what it can. Reporting should state whether a result describes, compares or evaluates, together with the level at which it is valid. Application in another setting depends on a separate examination of context and comparability.","Findings on the analysis should preserve material uncertainty and limits on application. In reviewing the measure, 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. Oversight of the available evidence should reflect the principle that international comparison can identify variation, but institutional and policy context remains necessary before a practice is transferred from one setting to another. A finding should not be separated from limitations capable of changing how it is understood or applied.","Review prompted by the present development should establish how the comparison moves from stated commitment to accountable implementation and outcome. Public confidence cannot be separated from an institution's ability to identify responsibility and substantiate its conclusions."]}],"word_count":809,"content_hash":"sha256-865063d538d1be44e09556af4a76b8b6b89913392df4e9887e385f4ae4d0457a","seo_keywords":["reporting artificial intelligence in education policy","international education data research","education quality","ICEQC"],"schema_type":"AnalysisNewsArticle"}