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TRUVACE RECORD VERSION
record: TRV-2026-1026
version: 2
kind: revised
reason: Direction audit: the gain line stated no AI consequence and was removed.
timestamp: 2026-10-26T06:38:47.287686Z
status: published
lens: p_space
sector: education
headline: Developing validity arguments for artificial intelligence-based assessment: Balancing affordances and threats
dek: Background Artificial intelligence (AI) is increasingly used to generate, score and interpret educational assessment, yet these applications are being adopted in a largely unregulated environment. This creates a paradox: Whereas AI systems used in clinical care are subject to formal scrutiny for safety, performance and monitoring, AI systems used to inform consequential decisions about learner progression and future clinical practice are not. Existing validity frameworks remain useful but may not fully account f…
gain_title: (none)
problem_title: AI-based assessment introduces distinct validity threats across scoring, generalisation, extrapolation and implications, including contamination, instability, inequities, automation bias and deskilling when used for consequential learner progression decisions.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: AI-based assessment introduces distinct validity threats across scoring, generalisation, extrapolation and implications, including contamination, instability, inequities, automation bias and deskilling when used for consequential learner progression decisions.
problem_evidence: AI introduces distinct validity threats across the inferential chain. | For scoring, key risks include construct contamination, prompt instability and limited explainability. | For implications, risks include subgroup inequities, automation bias, deskilling, weak accountability and erosion of academic integrity.
quick_read: A 2026 conceptual review in Medical Education examined how artificial intelligence used to generate, score and interpret assessments affects validity. Using Kane's four inferences, the authors mapped threats such as prompt instability and domain shift and noted that AI assessment is advancing without formal scrutiny comparable to clinical AI.

The analysis matters because validity failures could lead to unfair progression decisions, subgroup inequities and erosion of academic integrity in medical training. The authors propose treating validity as continuous governance with auditability and multi-site evaluation, but the paper does not provide new empirical data on whether such governance improves outcomes.
limitation: Findings derive from a conceptual review synthesizing literature rather than new empirical multi-site validation of an AI assessment system.
tag: Evidence-backed problem
key_points: Review applied Kane's four inferences: scoring, generalisation, extrapolation and implications to AI-based assessment. | Scoring threats identified include construct contamination, prompt instability and limited explainability. | Generalisation threats include domain shift, rater culture differences and temporal drift across settings and time. | Implications threats include subgroup inequities, automation bias, deskilling, weak accountability and erosion of academic integrity.
rundown: The authors reviewed validity for AI-based assessment using Kane's framework, noting AI is being adopted in a largely unregulated environment compared to clinical AI, creating risks for decisions about progression.

They argue defensible use requires explicit validity arguments, auditability, multi-site evaluation and continuous consequence monitoring as ongoing governance of a sociotechnical system rather than one-time validation.
sources:
- peer_reviewed | Medical Education | https://doi.org/10.1111/medu.70309 | 2026-09-08
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