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KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing

arXiv 2605.23933 ↗First posted 24 Apr 2026cs.CY cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 24 Apr 2026.

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References · 24

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12 more
  1. 2024Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratchcited by 24
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