From Mastery Profile to Simulated Response: Stochastic Student Knowledge Graphs (SSKG) for Faithful LLM Student Simulation

arXiv:2608.21668v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels to generate synthetic training data and stress-test tutoring systems. Existing approaches primarily leverage an LLM to answer questions or perform tests while instructing it to simulate various skill levels. However, LLMs tend to execute at their full built-in capability regardless of target skill levels. We demonstrate this limitation on SAT questions across five mastery levels, where three LLMs consistently achieve 96.8-100\% accuracy. To fai

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