KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing

2026-04-24

AI Summary

This study addresses the limitation of existing educational question generation methods in achieving fine-grained personalization and dynamically recommending exercises that maximize learning gains based on a student’s current knowledge state. To this end, the work proposes a novel personalized question recommendation framework that explicitly leverages knowledge tracing (KT) models to guide large language models (LLMs) in generating targeted practice questions. By predicting students’ mastery levels across knowledge concepts, the system identifies the most critical areas requiring reinforcement and produces corresponding exercises aimed at holistically improving overall knowledge acquisition. Experimental results on the XES3G5M and MOOCRadar datasets demonstrate that the generated questions significantly outperform baseline approaches lacking or offering only limited personalization in terms of instructional effectiveness.

Abstract

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.

Problem

• Educational Question Generation
• Personalization
• Knowledge Tracing
• Student Modeling
• Exercise Generation

Innovation

• Knowledge Tracing
• Personalized Question Generation
• Educational Question Generation
• Large Language Model
• Student Modeling