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

TL;DR

U-GLAD is a novel uncertainty-aware generative learning framework for personalized learning path recommendation that models cognitive states probabilistically and employs diffusion models for tailored concept prediction.

Contribution

The paper introduces U-GLAD, integrating probabilistic cognitive modeling and diffusion-based generative recommendation for improved personalization and uncertainty handling.

Findings

U-GLAD outperforms baseline methods on three public datasets.

It effectively models interaction uncertainty in learning paths.

Provides stable, goal-driven recommendations with superior perception of uncertainty.

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 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.