A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students, which can facilitate tutor model evaluation and training. Existing work mostly focuses on within-dialogue simulation, which lacks context on student knowledge and behavior, partly due to not grounding in past student question-answering or dialogue interactions. In this work, we introduce the task of history-conditioned student simulation, where the goal is to accurately predict student dialogue turns by leveraging information in the student's learning history. We propose a two-component framework in which a profile generator summarizes a student's history and a simulator predicts student turns conditioned on the resulting profile. We train both components with reinforcement learning (RL), yielding profiles optimized for faithful student simulation.
Beyond within-dialogue simulation: Duan et al. introduce history-conditioned student simulation, a task where LLM-based student simulators are grounded in past question-answering and dialogue interactions. This moves beyond prior work that simulated students only within a single dialogue context.Two-component RL framework: A profile generator summarizes a student's learning history into a compact profile, and a simulator predicts student dialogue turns conditioned on that profile. Both components are jointly trained via Reinforcement Learning (if that page exists) to optimize for faithful simulation.Real-world math tutoring dataset: The paper collects and evaluates on the first-of-its-kind dataset of real student dialogues and question responses from a math learning platform, grounding the work in authentic Student Experience data rather than synthetic proxies.Significant implications for tutor evaluation: History-aware student simulation enables more realistic evaluation of Intelligent Tutoring systems, allowing developers to test tutor responses against diverse student profiles without requiring live students. This connects to Kt4eqg Personalized Question Generation which also leverages student modeling for personalized learning.RL-optimized profiles ensure that the compressed student representation captures information most relevant to dialogue prediction, rather than using generic summary metrics. This approach could generalize to other Learning Analytics tasks where student state compression is needed.Connected Concepts
Reinforcement LearningStudent ExperienceIntelligent TutoringLearning AnalyticsConnected Articles
Kt4eqg Personalized Question GenerationCitation
Zhangqi Duan, Shuyan Huang, Alexander Scarlatos, Jaewook Lee, Simon Woodhead, & Andrew Lan (2026). Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues. arXiv:2605.30051. arXiv preprint.