Evidence Alignment and Transfer Boundaries in Ai Educational Assessment And Built-Environment Evaluation: Application Artificial Intelligence Educational and Correlation Between Everyday Public

Abstract

The literature on AI educational assessment and built-environment evaluation contains a recurring tension between methodological novelty and evidential comparability. By reading AI-based student-performance prediction as a case study in educational assessment alongside built-environment indicators, spatial correlation, and a dual-perspective evaluation framework for everyday public space, this article clarifies the conditions under which their conclusions can support a common research argument. 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 synthesis shows that construct validity cannot be interpreted independently of data drift, while fairness determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. 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.