<![CDATA[This study is among the first to use archival institutional records to test the incremental validity of artificial intelligence platform behaviors (AI_index) in predicting grammar achievement (GA). Using data from 405 non–English-major freshmen enrolled in a compulsory grammar course at a private Chinese university, we examined whether AI_index predicts end-of-semester grammar exam performance beyond course-embedded behavioral academic engagement (AE_index). AE_index was derived from grade-book quizzes and class interactions, whereas AI_index was constructed from institutional platform logs capturing coursework completion and assigned video viewing. Indices were scaled to a 0–100 range, and GA was measured by a unified final exam. Descriptive statistics, correlations, and hierarchical regression analyses showed that AE_index was a small but significant predictor of exam performance, whereas AI_index was weak and non-significant and added no incremental predictive value beyond AE_index. Together, the two indices explained a modest proportion of variance in GA. These findings suggest that completion-based platform metrics are unlikely to reflect effortful learning unless platform tasks align with summative assessment demands (e.g., translation and proofreading). The findings caution against using completion-based AI metrics as high-stakes indicators without demonstrated task–assessment alignment.]]>
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