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.

翻译：学习路径推荐（LPR）对于个性化教育至关重要，然而现有方法通常未能考虑历史交互中的不确定性（例如偶然猜测或疏忽失误），且缺乏对不同学习目标的适应性。我们提出U-GLAD（面向不确定性的认知自适应扩散生成式学习路径推荐）。为缓解表征偏差，该框架将认知状态建模为概率分布，通过高斯LSTM捕获学习者潜在的真正状态。为实现高度个性化推荐，目标导向概念编码器利用多头注意力机制和目标特定变换，动态地将概念语义与个体学习目标对齐，生成独特定制的嵌入向量。不同于传统判别式排序方法，我们的模型采用生成式扩散模型预测下一最优概念的潜在表征。在三个公开数据集上的大量评估表明，U-GLAD显著优于代表性基线方法。进一步分析证实其在感知交互不确定性、提供稳定且目标驱动的推荐路径方面具有卓越能力。