Zhangqi Duan et al. (2026) — arXiv preprint.
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.
Key Contributions
- 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.
Related Pages
- learning-analytics — Educational data infrastructure and analytics pipelines
- intelligent-tutoring — AI tutoring systems and student modeling
- academic-integrity — AI's impact on academic honesty and policy
- over-reliance — Risks of student dependence on AI assistance
- ai-literacy — Building student and educator competency with AI tools
- student-experience — How students interact with and perceive AI in education
- edtech-platform — Platform and infrastructure design for educational technology
Citation
APA: 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.