EIREducational Innovation Research3029-18443029-1852Bio-Byword Scientific Publishing Pty. Ltd.10.26689/EIR.v4i7.16067ArticleGrey–Copula Dynamic Prediction of Hidden Learning Risk in Vocational Mathematics under Generative AIhttps://artdesignp.com/journal/EIR/4/7/10.26689/EIR.v4i7.16067WangYan2026472026-07-26Generative AI can raise students’ assignment marks while concealing a decline in their unaided problem solving. In vocational college mathematics, a semester supplies only twelve to sixteen weekly observations per student, so large-sample classifiers cannot be estimated reliably; risk appears as several indicators deteriorating together, so single-threshold rules overlook students approaching two limits at once; and dependence between indicators shifts once generative AI enters the classroom, so static models respond too slowly. This paper proposes a grey–Copula dynamic prediction method built around these three constraints. Four weekly indicators operationalise hidden learning risk as two conjunctive events; an improved grey model produces one-step forecasts and threshold-crossing probabilities from a few weeks of data; a rolling-window Copula couples paired indicators and is re-estimated weekly; and joint probabilities are mapped onto a three-tier alert. In a simulation of 15 scenarios and 1,600 replications, the joint probabilities were better calibrated than threshold and single-indicator rules (Brier score 0.115 against 0.177 and 0.262), and the rolling window recovered the post-break dependence within about 2.5 weeks. The Copula layer improved recall more than calibration, and an autoregressive alternative forecast better. No classroom validation is claimed.Generative artificial intelligence,Vocational college mathematics,Learning risk,Grey prediction model,Copula function

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