PS$^2$: Parameterized Control for Fine-Grained Student Proficiency Simulation

• URL: http://arxiv.org/abs/2602.00850v1
• Date: Sat, 31 Jan 2026 18:27:56 GMT
• Title: PS$^2$: Parameterized Control for Fine-Grained Student Proficiency Simulation
• Authors: Ruochen Liu, Zhiyuan Wen, Hao Yan, Jun Yin, Senzhang Wang, Jiannong Cao,
• Abstract summary: Student Simulation (PS$2$) is an unsupervised and parameterized model-level framework that simulates students with different proficiencies.<n>PS$2$ achieves finer-grained and consistent proficiency simulation compared to existing baselines.
• Score: 37.112666030892115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding how students with different proficiency levels respond to educational materials is a critical issue within the field of AI for Education. However, acquiring sufficient real student response data for a robust evaluation is often hindered by cost, ethics, and security constraints. Consequently, LLM-based student proficiency simulation, especially prompt-based methods, has emerged as a practical alternative under data-scarce conditions. Despite their promise, current methods still exhibit limited controllability with coarse-grained proficiency representations, high sensitivity to prompt design, and the lack of calibration with academic performance. Therefore, we propose Parameterized Student Proficiency Simulation (PS$^2$), an unsupervised and parameterized model-level framework that simulates students with different proficiencies by interpolating between a strong upper-bound LLM and a weaker, cognitive error-informed lower-bound student LLM via a hybrid ratio. Specifically, the lower-bound model is constructed by fine-tuning the weaker LM to exhibit cognitive errors when responding to educational materials. To ensure alignment with target proficiency levels, PS$^2$ further calibrates the interpolation ratio with academic performance. Experiments on two public datasets demonstrate that PS$^2$ achieves finer-grained and consistent proficiency simulation compared to existing baselines, leading to superior performance in student behavior similarity and item difficulty prediction.

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