Reframing Ai Educational Assessment And Built-Environment Evaluation: Measurement Chains and Validation Design

Abstract

Research spanning AI educational assessment and built-environment evaluation 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 post-occupancy evaluation and mechanism diagnosis for rural construction to determine which claims can travel across those boundaries and which remain context dependent. 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. 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.