From Mechanism to Decision in Ai Educational Assessment And Football Pool Forecasting: Cross-Scale Reasoning and Reproducibility

Abstract

This review examines a shared methodological problem in AI educational assessment and football pool forecasting: how evidence from AI-based student-performance prediction as a case study in educational assessment can be placed in analytical dialogue with a football-lottery playing method that converts match judgments into ticket combinations without erasing differences in scale, assumptions, or intended use. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: construct validity, data drift, fairness, explainability, teacher oversight. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. Comparison reveals recurring trade-offs among construct validity, data drift, and fairness. These trade-offs do not support a universal ranking; instead, they identify the operating envelope within which each method remains credible and the perturbations most likely to expose fragile conclusions. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.