Researchers introduce StudentSim, a training framework that creates individualized student simulators by combining pooled training with per-student specialization. The accompanying StudentSimEval protocol standardizes evaluation across chess, language writing, and mathematics domains, measuring behavioral fidelity and guidance responsiveness. The framework outperforms GPT-5.4 and enables reinforcement learning for AI tutors that human experts rate as superior.
