Evidence Alignment and Transfer Boundaries in Ai Educational Assessment And Clinical Risk Prediction

Abstract

Progress in AI educational assessment and clinical risk prediction depends on more than accumulating favorable results. This critical synthesis connects AI-based student-performance prediction as a case study in educational assessment with a clinical nomogram and web calculator for individualized lymph-node-metastasis risk and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. The review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links construct validity, data drift, and fairness to downstream questions of explainability and teacher oversight. The combined literature indicates that methodological gains become actionable only when construct validity and data drift are evaluated together and when limits associated with teacher oversight are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. The article concludes with a research agenda built around transparent comparators, targeted stress tests, and evidence records that can be reused without overstating causal or practical reach.