Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues
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 s...
| Authors | Zhangqi Duan, Shuyan Huang, Alexander Scarlatos et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 May 2026 |
| Last updated at source | 30 August 2026 |
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