The article examines a disaggregated analysis of AI-supported assessment, separating supported observations from causal claims and identifying where further evidence is required.
Evidence concerning AI-supported assessment should inform action without implying a level of precision, coverage or causal certainty that the underlying data cannot support. Learner effect, institutional duty and proper resource use should inform the judgement.
In examining a disaggregated analysis of AI-supported assessment, its relevance to the analysis should be assessed against the affected jurisdiction, learner population and form of provision.
For AI-supported assessment, implementation of the comparison should be organised around a decision that can be tested.
Application to disaggregated analysis of AI-supported assessment
Any indicator used in relation to the measure should distinguish description from causal explanation. For AI-supported assessment, interpretation should retain uncertainty, distributional differences and limits on generalisation.
For the measure, an average may improve while a material group experiences no improvement or a worse outcome. For decisions concerning AI-supported assessment, disaggregation should follow a defined public-interest question and should protect confidentiality where small numbers could identify individuals.
The principal risks associated with the issue should be assessed as connected conditions. A provider should also consider tasks that do not assess the stated outcome and reasonable adjustment altering the assessed outcome. In the context of AI-supported assessment, the control response should reflect whether an affected learner can identify the error and obtain an effective remedy in time.
For the analysis, the most relevant material is likely to include assessment maps to learning outcomes, appeal and correction records, approval and change-control records, and authorship and identity controls proportionate to risk. For AI-supported assessment, each source has limitations; confidence depends on corroboration between independent records and transparent treatment of uncertainty.
Controls for disaggregated analysis of AI-supported assessment
Implementation of AI-supported assessment can be tested without imposing unnecessary reporting. For the analysis, the reviewer should examine results by relevant learner, programme, location and delivery characteristics; compare both levels and rates of change; and test whether observed gaps persist after differences in coverage and prior conditions are considered.
The analytical record for AI-supported assessment should state the research question, data source, unit of analysis, reference period, coverage, exclusions, treatment of missing values and principal limitations.
Records relating to the analysis should preserve both the conclusion and its limits. For AI-supported assessment, the correction record should state what the new evidence changes and which earlier conclusions or decisions require review.
Review of disaggregated analysis of AI-supported assessment
Proportionality in relation to AI-supported assessment does not mean reduced protection for learners exposed to greater risk.
In examining a disaggregated analysis of AI-supported assessment, accountability for AI-supported assessment should follow decision-making authority.
Across the defined scope, neither one indicator nor one control can establish the complete position on the issue.
The reference period and version of the source identified in Rapid adoption of generative AI tools should remain traceable. A later correction, expanded dataset or revised classification may justify a new conclusion, but it should not reconstruct the earlier record without stating what changed and how the change affects comparability.