Gao et al. (2026) — Microsoft Research / MIT / UC Santa Barbara. arXiv preprint.
KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing bridges two key AI-in-education paradigms: personalized-learning through question generation and learning-analytics through knowledge tracing. Rather than generating generic practice questions, KT4EQG uses a Knowledge Tracing model to first identify the knowledge concept that would maximize a student's potential improvement in overall mastery, then trains an llm-based generator to produce a question faithfully grounded in that concept. This two-stage architecture — KT for concept selection, LLM for faithful question generation — outperforms less personalized baselines on XES3G5M and MOOCRadar datasets. The approach represents a significant advance in adaptive-learning system design, connecting to knowledge-tracing-irt research on modeling student knowledge states and automated-question-generation work on producing high-quality educational content. Unlike earlier systems such as slidesqaqa-pedagogical-question-generation that generate questions from static content, KT4EQG personalizes based on dynamic student models, aligning with intelligent-tutoring goals of providing the right question at the right time for each learner.
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