@inproceedings{zhao-etal-2026-edumars,
title = "{E}du{MARS}: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on {C}hinese K-12 Answers",
author = "Zhao, Xuan and
Chen, Jiashun and
xu, Wanting and
Yan, Huiyuan and
Fang, Chaowei and
Wei, Xing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.466/",
pages = "9561--9583",
ISBN = "979-8-89176-395-1",
abstract = "Automated grading of student work is a critical application of AI in education. However, existing benchmarks fall short in evaluating models on realistic, cognitively demanding tasks. Most rely on synthetic, well-structured text inputs, overlooking the multimodal, error-prone, and often handwritten nature of real student responses, especially in K-12 settings. We introduce EduMARS, a multimodal benchmark designed for rubric-aligned evaluation of real Chinese K-12 student answers. The dataset contains over 4,500 authentic responses from high-stakes exams across eight subjects, featuring noisy handwriting,mixed-layout diagrams,mathematical expressions, and narrative reasoning. Each response is meticulously annotated by expert teachers using step-wise scoring rubrics, error classifications, and key-point mappings, providing fine-grained supervision aligned with real-world pedagogical practices. We evaluated existing SOTA MLLMs across the dimensions of final score and the reasoning process of grading, reveals a significant gap between existing SOTA MLLMs and human-level performance. To bridge this performance gap, we propose the Retrieval-Augmented Adaptive-Rubric Grading (RARG), enabling models to emulate expert grading logic by dynamically synthesizing case-specific evaluation schemas. RARG effectively enhances the performance and interpretability of various MLLMs on EduMARS, surpassing in-context learning and chain-of-thought."
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<abstract>Automated grading of student work is a critical application of AI in education. However, existing benchmarks fall short in evaluating models on realistic, cognitively demanding tasks. Most rely on synthetic, well-structured text inputs, overlooking the multimodal, error-prone, and often handwritten nature of real student responses, especially in K-12 settings. We introduce EduMARS, a multimodal benchmark designed for rubric-aligned evaluation of real Chinese K-12 student answers. The dataset contains over 4,500 authentic responses from high-stakes exams across eight subjects, featuring noisy handwriting,mixed-layout diagrams,mathematical expressions, and narrative reasoning. Each response is meticulously annotated by expert teachers using step-wise scoring rubrics, error classifications, and key-point mappings, providing fine-grained supervision aligned with real-world pedagogical practices. We evaluated existing SOTA MLLMs across the dimensions of final score and the reasoning process of grading, reveals a significant gap between existing SOTA MLLMs and human-level performance. To bridge this performance gap, we propose the Retrieval-Augmented Adaptive-Rubric Grading (RARG), enabling models to emulate expert grading logic by dynamically synthesizing case-specific evaluation schemas. RARG effectively enhances the performance and interpretability of various MLLMs on EduMARS, surpassing in-context learning and chain-of-thought.</abstract>
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%0 Conference Proceedings
%T EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers
%A Zhao, Xuan
%A Chen, Jiashun
%A xu, Wanting
%A Yan, Huiyuan
%A Fang, Chaowei
%A Wei, Xing
%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 zhao-etal-2026-edumars
%X Automated grading of student work is a critical application of AI in education. However, existing benchmarks fall short in evaluating models on realistic, cognitively demanding tasks. Most rely on synthetic, well-structured text inputs, overlooking the multimodal, error-prone, and often handwritten nature of real student responses, especially in K-12 settings. We introduce EduMARS, a multimodal benchmark designed for rubric-aligned evaluation of real Chinese K-12 student answers. The dataset contains over 4,500 authentic responses from high-stakes exams across eight subjects, featuring noisy handwriting,mixed-layout diagrams,mathematical expressions, and narrative reasoning. Each response is meticulously annotated by expert teachers using step-wise scoring rubrics, error classifications, and key-point mappings, providing fine-grained supervision aligned with real-world pedagogical practices. We evaluated existing SOTA MLLMs across the dimensions of final score and the reasoning process of grading, reveals a significant gap between existing SOTA MLLMs and human-level performance. To bridge this performance gap, we propose the Retrieval-Augmented Adaptive-Rubric Grading (RARG), enabling models to emulate expert grading logic by dynamically synthesizing case-specific evaluation schemas. RARG effectively enhances the performance and interpretability of various MLLMs on EduMARS, surpassing in-context learning and chain-of-thought.
%U https://aclanthology.org/2026.findings-acl.466/
%P 9561-9583
Markdown (Informal)
[EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers](https://aclanthology.org/2026.findings-acl.466/) (Zhao et al., Findings 2026)
ACL
- Xuan Zhao, Jiashun Chen, Wanting xu, Huiyuan Yan, Chaowei Fang, and Xing Wei. 2026. EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9561–9583, San Diego, California, United States. Association for Computational Linguistics.