Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion
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 distributions, capturing the learner's underlying true state via a Gaussian LSTM. To ensure highly personalized recommendation, a goal-oriented concept encoder utilizes multi-head attention and objective-specific transformations to dynamically align co...
| Authors | Xiangrui Xiong, Hang Liang, Baiyang Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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