Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion
For personalized education, this work improves learning path recommendation by explicitly modeling uncertainty and adapting to individual goals, though it is an incremental improvement over existing methods.
U-GLAD addresses learning path recommendation by modeling cognitive states as probability distributions to handle interaction uncertainty and using a generative diffusion model for personalized recommendations, significantly outperforming baselines on three datasets.
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 concept semantics with individual learning goals, generating uniquely tailored embeddings. Unlike traditional discriminative ranking approaches, our model employs a generative diffusion model to predict the latent representation of the next optimal concept. Extensive evaluations on three public datasets demonstrate that U-GLAD significantly outperforms representative baselines. Further analyses confirm its superior capability in perceiving interaction uncertainty and providing stable, goal-driven recommendation paths.