Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit. However, few existing EQG approaches are able to achieve such fine-grained personalization. In this paper, we explore how EQG can benefit from knowledge tracing (KT), which models students' knowledge states based on historical performance and predicts future performance. We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model. Specifically, KT4EQG seeks to maximize a student's potential improvement in overall knowledge mastery by leveraging the KT model to select the most suitable knowledge concept for the student to practice. An LLM-based question generator is then trained to produce a question faithfully grounded in the selected concept. Experimental results on XES3G5M and MOOCRadar show that KT4EQG consistently generates more effective questions than methods with limited or no personalization.

翻译：教育习题生成（EQG）旨在合成定制化的习题以增强学生学习效果。理想的EQG系统应能通过建模学生的知识状态，生成能带来最大学习收益的个性化习题。然而，现有EQG方法鲜少能实现如此细粒度的个性化。本文探索了EQG如何受益于知识追踪（KT）技术——该技术基于历史表现建模学生知识状态并预测未来表现。我们提出KT4EQG，一种在KT模型指导下为个体学生生成有效习题的个性化EQG框架。具体而言，KT4EQG通过利用KT模型选择最适合学生练习的知识概念，最大化学生在整体知识掌握度上的潜在提升。随后，基于LLM的习题生成器被训练以生成严格围绕所选概念的高保真习题。在XES3G5M和MOOCRadar数据集上的实验结果表明，KT4EQG能持续生成比有限个性化或无个性化方法更有效的习题。