Mechanism-aware personalized learning intervention for short- and long-term academic outcome modeling.

Introduction: Improving academic achievement while also modeling students' longer-horizon academic development has become an important goal in educational psychology and AI-supported learning research. However, adaptive learning, learner modeling, feedback generation, and academic prediction are often studied separately, and the learner conditions used to guide intervention are not always defined with sufficient theoretical and methodological transparency. Methods: This study proposes Results: MAPLe-I achieved stronger comparative benchmark performance across multiple settings. On ASSISTments, it obtained an AUC of 0.824 and an Intervention Alignment Rate of 0.785. On EdNet-KT1, it obtained an AUC of 0.812. On OULAD, it achieved a Macro-F1 of 0.772 and reduced RMSE to 0.315. Ablation and sensitivity analyses further supported the contribution of the temporal encoder, mechanism head, intervention policy head, and dual-horizon optimization strategy. Discussion: The findings suggest that integrating learner-state diagnosis, theory-informed latent proxies, and intervention-policy modeling can improve offline predictive and rule-alignment performance in benchmark settings. However, the mechanism variables are computational proxies, the intervention labels are rule-derived rather than observed, and the results do not establish causal effects or authentic classroom impact. MAPLe-I should therefore be interpreted as a transparent benchmark framework for mechanism-aware personalized learning rather than as a validated real-world intervention system.