StudentSim: Training LLM-based Student Simulators
StudentSim turns sparse user histories into individualized simulators that model both current behavior and response to guidance, a pattern for testing adaptive agents before live deployment.
StudentSim uses pooled training followed by per-student specialization. StudentSimEval covers **60 students** across chess, English writing, and mathematics, measuring behavioral fidelity and guidance responsiveness on the same de-identified learner records.
For adaptive agents, separate imitation accuracy from response-to-intervention. A simulator used for evaluation or as a reward model should reproduce a user's baseline behavior and model how that behavior changes after guidance; fluent role-play alone does not establish either property.
StudentSim uses pooled training followed by per-student specialization. StudentSimEval covers **60 students** across chess, English writing, and mathematics, measuring behavioral fidelity and guidance responsiveness on the same de-identified learner records. For adaptive agents, separate imitation accuracy from response-to-intervention. A simulator used for evaluation or as a reward model should reproduce a user's baseline behavior and model how that behavior changes after guidance; fluent role-play alone does not establish either property. StudentSim beat GPT-5.4 on both metrics across all three domains. In chess it reached **F=0.51 and R=0.91**, versus **F=0.23 and R=0.72 for GPT-5.4**; however, the proof-of-concept tutor and expert ratings do not establish generalization beyond the evaluated learner datasets and tasks.
This adds a two-axis evaluation requirement for personalized simulators: matching an individual’s baseline behavior and reacting credibly to guidance are separate capabilities. It extends prior trajectory and pressure-sensitive evals from testing agent behavior to testing modeled user change, while the per-student results narrow the evidence to the supplied learner records and three domains rather than general-purpose personalization.