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.

This paper presents an empirical evaluation of a vision-capable LLM-based grader for handwritten mathematical work. Using a single LLM call that combines transcription and rubric-based evaluation, the system was tested on student work from two university STEM courses. The grader achieved high overall accuracy at the rubric-item level, with 87% of errors in the best model attributed to transcription failures rather than rubric misapplication. The authors categorize common error modes — image quality issues, hallucinated content, and incorrect handling of equivalent expressions — providing guidance for system design, prompt refinement, and deployment. This work extends the Automated Grading pipeline to the previously unexplored domain of handwritten mathematics, complementing systems like Lata Ferpa Compliant Local LLM Autograder that focus on typed LaTeX submissions. Unlike typed-response graders that achieve near-perfect rubric alignment, the vision pipeline introduces new failure modes around handwriting recognition that echo the diagnostic precision challenges identified in LLM Tutoring Feedback Diagnosis Gap. The finding that the LLM can correctly apply instructor rubrics when transcription succeeds suggests that Multimodal AI Tutoring approaches can bridge the gap between visual input and pedagogical assessment, while remaining sensitive to the limitations documented in Educational Vlm Evaluation.

Connected Concepts

Automated Grading

Connected Articles

Lata Ferpa Compliant Local LLM Autograder

LLM Tutoring Feedback Diagnosis Gap

Multimodal AI Tutoring

Educational Vlm Evaluation

Citation

Jacob Levine, Miguel Aenlle, Craig Zilles, Matthew West, Mariana Silva (2026). Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs. arXiv:2605.19043. International Conference on AI in Education (AIED 2026).