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        <namePart type="given">Zhangqi</namePart>
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    <abstract>Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (LLMs) to simulate and predict possible student errors in their responses to these problems can be challenging: they often suffer from mode collapse and fail to fully capture the diversity in syntax, style, and solution approach in student responses. In this work, we present KASER (Knowledge-Aligned Student Error Simulator), a novel approach that aligns errors with student knowledge. We propose a training method based on reinforcement learning using a hybrid reward that reflects three aspects of student code prediction: i) code similarity to the ground-truth, ii) error matching, and iii) code prediction diversity. On two real-world datasets, we perform two levels of evaluation and show that: At the per-student-problem pair level, our method outperforms baselines on code and error prediction; at the per-problem level, our method outperforms baselines on error coverage and simulated code diversity.</abstract>
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%0 Conference Proceedings
%T KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks
%A Duan, Zhangqi
%A Fernandez, Nigel
%A Lan, Andrew
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F duan-etal-2026-kaser
%X Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (LLMs) to simulate and predict possible student errors in their responses to these problems can be challenging: they often suffer from mode collapse and fail to fully capture the diversity in syntax, style, and solution approach in student responses. In this work, we present KASER (Knowledge-Aligned Student Error Simulator), a novel approach that aligns errors with student knowledge. We propose a training method based on reinforcement learning using a hybrid reward that reflects three aspects of student code prediction: i) code similarity to the ground-truth, ii) error matching, and iii) code prediction diversity. On two real-world datasets, we perform two levels of evaluation and show that: At the per-student-problem pair level, our method outperforms baselines on code and error prediction; at the per-problem level, our method outperforms baselines on error coverage and simulated code diversity.
%U https://aclanthology.org/2026.acl-long.1858/
%P 39988-40006

Markdown (Informal)

[KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks](https://aclanthology.org/2026.acl-long.1858/) (Duan et al., ACL 2026)

• KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks (Duan et al., ACL 2026)

ACL

• Zhangqi Duan, Nigel Fernandez, and Andrew Lan. 2026. KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 39988–40006, San Diego, California, United States. Association for Computational Linguistics.