From Mechanism to Decision in Ai Educational Assessment And Battery Manufacturing Quality: Cross-Scale Reasoning and Reproducibility

Abstract

Research spanning AI educational assessment and battery manufacturing quality increasingly joins methods that were developed for different objects and decisions. Here, AI-based student-performance prediction as a case study in educational assessment is compared with correlation analysis linking large-scale manufacturing variability with battery electrochemical stability to determine which claims can travel across those boundaries and which remain context dependent. 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. The resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.