Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion

arXiv:2604.14613v1 Announce Type: cross Abstract: Learning Path Recommendation (LPR) is critical for personalized education, yet current methods often fail to account for historical interaction uncertainty (e.g., lucky guesses or accidental slips) and lack adaptability to diverse learning goals. We propose U-GLAD (Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion). To address representation bias, the framework models cognitive states as probability distr