Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress (''knowledge tracing''; KT), and the prerequisite structure of the learning domain (''knowledge mapping''). While recent deep learning models achieve high KT accuracy, they do so at the expense of the interpretability of psychologically-inspired models. In this work, we present a solution to this trade-off. PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics, thus achieving interpretability by design. Moreover, by using scalable Bayesian inference, PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and learning histories. Evaluated on three datasets from online learning platforms, PSI-KT achieves superior multi-step predictive accuracy and scalable inference in continual-learning settings, all while providing interpretable representations of learner-specific traits and the prerequisite structure of knowledge that causally supports learning. In sum, predictive, scalable and interpretable knowledge tracing with solid knowledge mapping lays a key foundation for effective personalized learning to make education accessible to a broad, global audience.

翻译：智能辅导系统通过优化学习材料的选择和呈现时机，以增强理解效果与长期记忆保持。这需要同时估计学习者的进步轨迹（即"知识追踪"）和学习领域的先决知识结构（即"知识图谱"）。尽管近年深度学习模型在知识追踪准确性上取得突破，但这种提升是以牺牲心理学启发模型的可解释性为代价的。本研究针对这一权衡难题提出解决方案：PSI-KT是一种层级生成式方法，通过显式建模个体认知特质与知识先决结构对学习动态的双重影响，从设计层面实现可解释性。同时，采用可扩展贝叶斯推断技术，PSI-KT能够在学习者数量和学习历史持续增长的真实场景中实现高效个性化。在三个在线学习平台数据集上的评估表明，PSI-KT在持续学习场景中兼具卓越的多步预测准确性与可扩展推理能力，同时提供可解释的学习者特质表征与因果支持学习的知识先决结构。总之，融合知识图谱的预测性、可扩展且可解释的知识追踪，为构建有效的个性化学习体系奠定了关键基础，使优质教育惠及全球广泛受众成为可能。