StudentSim：基于大语言模型的学生模拟器训练

摘要

当AI导师能适应每个学生的优势、劣势和偏好的指导方式时，其作用最大。然而，关于哪种指导对哪个学生更有效，从真实学习者那里收集此类证据既稀疏、缓慢且成本高昂。学生模拟器可作为一种代理来提供此类信号，但现有方法存在局限：状态跟踪模型能拟合学生行为，却难以处理解释或纠正；而大语言模型（LLM）的角色扮演虽能流畅地遵循指导，却无法可靠地匹配所模拟学生的真实能力水平。为此，我们提出了StudentSim，一种训练框架，通过先进行汇总训练，再进行针对单个学生的特化，将稀疏的个体学生数据转化为个性化模拟器。由此产生的模拟器既能复现学生自身的回答，也能在导师指导下更新这些回答。我们还推出了StudentSimEval，一个标准化评估协议，覆盖国际象棋、第二语言英语写作和数学三个领域的60名学生，使用公开共享、记录经去标识化处理的学习者数据集。StudentSimEval衡量两个核心指标：行为保真度（F），即模拟器匹配学生回答的程度；以及指导响应度（R），即模拟器在导师指导下更新的难易程度。所有方法均在相同的记录上进行拟合并评估。在全部三个领域中，StudentSim在两项指标上均优于GPT-5.4。在国际象棋上，StudentSim达到F=0.51、R=0.91，而GPT-5.4仅为0.23和0.72，Maia2为0.45和0.27。作为概念验证，将StudentSim用作导师强化学习的奖励模型，训练出的国际象棋导师被人类专家评价为比无强化学习基线以及以GPT-5.4模拟器奖励训练的导师更准确、指导性更强且更加个性化。代码见https://github.com/microsoft/StudentSim。

English

AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow, and costly to collect from real learners. Student simulators can provide this signal as a proxy, yet existing approaches are limited: state-tracking models fit student behavior but struggle to process explanations or corrections, while LLM role-play follows guidance fluently but does not reliably match the competence of the student being imitated. We present StudentSim, a training framework that turns sparse per-student data into individualized simulators through pooled training followed by per-student specialization. The resulting simulators both mirror a student's own responses and update them under tutor guidance. We also introduce StudentSimEval, a standardized protocol covering 60 students across chess, second-language English writing, and mathematics, using public learner datasets with de-identified records shared for research. StudentSimEval measures behavioral fidelity (F), or how well a simulator matches a student's responses, and guidance responsiveness (R), or how readily it updates under tutor guidance, with all methods fit and evaluated on the same records. Across all three domains, StudentSim outperforms GPT-5.4 on both metrics. In chess, StudentSim reaches F=0.51 and R=0.91, compared with 0.23 and 0.72 for GPT-5.4 and 0.45 and 0.27 for Maia2. As a proof of concept, using StudentSim as a reward model for tutor reinforcement learning produces a chess tutor that expert humans rate as more accurate, better-guided, and more personalized than a no-RL baseline and a tutor trained against a GPT-5.4 simulator reward. Code is available at https://github.com/microsoft/StudentSim.

StudentSim：基于大语言模型的学生模拟器训练

StudentSim: Training LLM-based Student Simulators

摘要

Support