Review of AI-supported assessment distinguishes completed activity from verified improvement and keeps unresolved action open to further examination.
In examining AI-supported assessment: an evidence-led improvement method, improvement of AI-supported assessment should begin with a defined problem, a credible account of its causes and a measure capable of showing whether the response has worked.
Application to AI-supported assessment
Any conclusion about AI-supported assessment still requires evidence from the setting concerned. The decision basis should identify what is evidenced, what reflects policy and what depends on authorised discretion.
When examining AI-supported assessment, the applicable expectation should be capable of consistent application.
The principal risks in relation to corrective action are reasonable adjustment altering the assessed outcome, tasks that do not assess the stated outcome, uncontrolled changes to assessment, and results used beyond the evidence they support. For AI-supported assessment, the risks are interdependent; failure of one control may conceal or disable another.
Analysis should make its decision rule explicit. Gaps may emerge when authority, records or action pass between responsible bodies. Across the defined scope, a stated decision rule enables comparable examination and limits retrospective explanations of adverse evidence.
Evidence concerning AI-supported assessment should be selected against a clearly defined question. For corrective action, the most relevant material is likely to include approval and change-control records, moderation and exception records, marking criteria and calibrated judgement, and assessment maps to learning outcomes.
Controls for AI-supported assessment
For corrective action, reliability without validity produces consistent but potentially irrelevant results.
For decisions concerning AI-supported assessment, the evidential trail should allow an affected decision to be identified, examined and corrected. For the matter, 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.
- Align tasks and criteria with learning outcomes.
- Calibrate assessors.
- Control changes, with responsibility, scope and timing recorded.
- Retain evidence sufficient for review.
- Moderate material variation.
Review of AI-supported assessment
The method for the matter is to map the complete process, identify the intended result and responsible authority at each stage, and test normal cases together with exceptions. For AI-supported assessment, contrary evidence should not be removed merely because aggregate performance appears acceptable.
- Who controls each stage?
- What outcome is intended?
- Where do exceptions occur?
- What action is required by the finding?
- Which evidence establishes operation?
Implications for AI-supported assessment
In the context of AI-supported assessment, the central objective should not be obscured by the form of the administrative response. Assessment should provide valid and sufficiently consistent evidence that the stated learning outcomes have been achieved by the learner receiving the result.
Authorities and providers should use the current development to test whether the intended improvement connects public commitment with effective operation and evidence of result.