Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs
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. Extending a prior pipeline for typed responses, we integrate transcription and rubric-based evaluation of photographic submissions within a single LLM call, evaluating on student work from two university STEM courses. Comparing AI grad...
| Authors | Jacob Levine, Miguel Aenlle, Craig Zilles et al. |
|---|---|
| Primary category | cs.CY |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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