The analysis of AI-supported assessment identifies what the evidence establishes, where comparison is limited and which qualifications must be reported.
In examining AI-supported assessment: evidence, coverage and limitations, its relevance to the available evidence should be assessed against the affected jurisdiction, learner population and form of provision.
Application to AI-supported assessment
AI-supported assessment should provide valid and sufficiently consistent evidence that the stated learning outcomes have been achieved by the learner receiving the result.
When examining AI-supported assessment, Review of the comparison should follow a stated and reproducible method. 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.
A narrow control over the available evidence may create false assurance. In the present context, tasks that do not assess the stated outcome, uncontrolled changes to assessment and inconsistent judgement between markers or locations may produce acceptable aggregate reporting while individual learners remain exposed to material disadvantage. For AI-supported assessment, adverse cases should form part of the sample wherever they may reveal a material control weakness.
The evidential record for the comparison should permit a reviewer to trace the matter from decision to outcome. This may require approval and change-control records, authorship and identity controls proportionate to risk, marking criteria and calibrated judgement, and appeal and correction records, supported by assessment maps to learning outcomes and analysis of results and differential outcomes. For AI-supported assessment, sampling remains insufficient where it excludes a material group or cannot resolve contradictory evidence or recurrence.
Controls for AI-supported assessment
The review 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. Across the defined scope, the retained analysis should be reproducible from the selected evidence, decision rule and recorded reasons for accepted exceptions.
Decisions concerning the analysis should remain traceable to the information available for the stated reference period. For AI-supported assessment, changes in condition, evidence, method and interpretation should be recorded separately when a conclusion is revised.
Review of AI-supported assessment
For comparative analysis, reliability without validity produces consistent but potentially irrelevant results.
In examining AI-supported assessment: evidence, coverage and limitations, across the defined scope, multiple delivery partners do not justify fragmented accountability or remedy.
A reliable conclusion requires corroboration across the material scope.