student-simulation

StudentSim trains personalized LLM-based student simulators from sparse data to mirror learner responses and adapt to tutor guidance, outperforming GPT-5.4 across chess, writing, and math domains.

The paper proposes Stochastic Student Knowledge Graphs (SSKG) to enable faithful simulation of student knowledge by LLMs, overcoming the limitation where LLMs tend to perform at their own capability level instead of simulating varying mastery profiles.