Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. However, accurately interpreting unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning poses a significant challenge due to the lack of authentic and domain-specific benchmarks. Additionally, current evaluation paradigms predominantly rely on the outcomes of downstream tasks (e.g., auto-grading), which often probe only a subset of the recognized content, thereby failing to capture the MLLMs' understanding of complex handwritten logic as a whole. To bridge this gap, we release EDU-CIRCUIT-HW, a dataset consisting of 1,300+ authentic student handwritten solutions from a university-level STEM course. Utilizing the expert-verified verbatim transcriptions and grading reports of student solutions, we simultaneously evaluate various MLLMs' upstream recognition fidelity and downstream auto-grading performance. Our evaluation uncovers an astonishing scale of latent failures within MLLM-recognized student handwritten content, highlighting the models' insufficient reliability for auto-grading and other understanding-oriented applications in high-stakes educational settings. In solution, we present a case study demonstrating that leveraging identified error patterns to preemptively detect and rectify recognition errors, with only minimal human intervention (e.g., with 3.3% assignments routed to human graders while the rest to GPT-5.1 grader), can effectively enhance the robustness of the deployed AI-enabled grading system on unseen student solutions.

翻译：多模态大语言模型（MLLMs）在革新传统教育、降低教师工作负荷方面具有显著潜力。然而，由于缺乏真实且领域特定的基准数据集，准确解读包含数学公式、图表与文本推理交织的无约束STEM学生手写解答仍面临重大挑战。现有评估范式主要依赖下游任务（如自动评分）的结果，这类评估往往仅探测被识别内容的子集，未能全面捕捉MLLMs对手写复杂逻辑的理解。为填补这一空白，我们发布EDU-CIRCUIT-HW数据集，包含来自大学水平STEM课程的1300余份真实学生手写解答。利用经专家验证的学生解答逐字转录文本与评分报告，我们同时评估多种MLLM在上游识别保真度与下游自动评分性能两方面的表现。评估结果揭示了MLLMs识别学生手写内容时潜藏着惊人的失效规模，表明这些模型在自动评分及其他面向理解的现实教育应用场景中可靠性不足。为此，我们提出案例研究：通过利用识别的错误模式主动检测并修正识别错误，仅需极少量人工干预（例如将3.3%的作业分配给人工评分员，其余由GPT-5.1评分系统处理），即可有效提升已部署AI评分系统对未知学生解答的鲁棒性。