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    <abstract>Large Language Models (LLMs) have become integral to personalized education systems, particularly in the realm of student behavior simulation. By predicting fine-grained learning behaviors, these simulations enable intelligent systems to provide tailored instructional support. However, most existing methods rely on a single high-capacity LLM to represent an entire population of diverse learners. In this work, we demonstrate that this “one-size-fits-all” approach induces a systematic ability-dependent bias, where high-capacity models tend to overestimate low-ability students while lower-capacity models underestimate high-ability ones. To mitigate this distortion, we propose an **ability-aware student simulation framework** that dynamically matches students with appropriate LLM backbones through cognitive alignment. We leverage Neural Cognitive Diagnosis (NeuralCD) to extract multidimensional cognitive profiles for both human students and LLM agents within a shared skill space, subsequently pairing each student with the most cognitively representative model. Extensive experiments demonstrate that our approach substantially reduces simulation bias and consistently outperforms single-model baselines across the entire proficiency spectrum. Our findings suggest that faithful behavior simulation necessitates the **alignment of model capacity with student ability**, establishing cognitive diagnosis as a principled mechanism for model assignment in educational AI.</abstract>
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%0 Conference Proceedings
%T One LLM Does Not Simulate All Students: Ability-Aware Student Simulation via Cognitive Diagnosis Guided LLM Assignment
%A Que, Huixing
%A Liu, Qi
%A Gao, Weibo
%A Huang, Zhenya
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F que-etal-2026-one
%X Large Language Models (LLMs) have become integral to personalized education systems, particularly in the realm of student behavior simulation. By predicting fine-grained learning behaviors, these simulations enable intelligent systems to provide tailored instructional support. However, most existing methods rely on a single high-capacity LLM to represent an entire population of diverse learners. In this work, we demonstrate that this “one-size-fits-all” approach induces a systematic ability-dependent bias, where high-capacity models tend to overestimate low-ability students while lower-capacity models underestimate high-ability ones. To mitigate this distortion, we propose an **ability-aware student simulation framework** that dynamically matches students with appropriate LLM backbones through cognitive alignment. We leverage Neural Cognitive Diagnosis (NeuralCD) to extract multidimensional cognitive profiles for both human students and LLM agents within a shared skill space, subsequently pairing each student with the most cognitively representative model. Extensive experiments demonstrate that our approach substantially reduces simulation bias and consistently outperforms single-model baselines across the entire proficiency spectrum. Our findings suggest that faithful behavior simulation necessitates the **alignment of model capacity with student ability**, establishing cognitive diagnosis as a principled mechanism for model assignment in educational AI.
%U https://aclanthology.org/2026.findings-acl.302/
%P 6067-6084

Markdown (Informal)

[One LLM Does Not Simulate All Students: Ability-Aware Student Simulation via Cognitive Diagnosis Guided LLM Assignment](https://aclanthology.org/2026.findings-acl.302/) (Que et al., Findings 2026)

• One LLM Does Not Simulate All Students: Ability-Aware Student Simulation via Cognitive Diagnosis Guided LLM Assignment (Que et al., Findings 2026)

ACL

• Huixing Que, Qi Liu, Weibo Gao, and Zhenya Huang. 2026. One LLM Does Not Simulate All Students: Ability-Aware Student Simulation via Cognitive Diagnosis Guided LLM Assignment. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6067–6084, San Diego, California, United States. Association for Computational Linguistics.