@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://preview.aclanthology.org/ingest-acl/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."
}