AI RESEARCH
Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs
arXiv CS.AI
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ArXi:2605.19043v1 Announce Type: cross Automated grading systems have enabled scalable assessment for many response types, but handwritten mathematics remains a barrier due to the complexity of multi-step solutions. Vision-capable large language models (LLMs) offer new opportunities here, yet their reliability in authentic instructional settings remains poorly understood. We present an empirical evaluation of an LLM-based grader for handwritten mathematical work using instructor-defined rubrics.