Evaluating AI Grading on Real-World Handwritten College Mathematics: A Large-Scale Study Toward a Benchmark

• URL: http://arxiv.org/abs/2603.00895v1
• Date: Sun, 01 Mar 2026 03:32:51 GMT
• Title: Evaluating AI Grading on Real-World Handwritten College Mathematics: A Large-Scale Study Toward a Benchmark
• Authors: Zhiqi Yu, Xingping Liu, Haobin Mao, Mingshuo Liu, Long Chen, Jack Xin, Yifeng Yu,
• Abstract summary: We present a large-scale empirical study of AI grading on real, handwritten calculus work from UC Irvine.<n>Using OCR-conditioned large language models with structured, rubric-guided prompting, our system produces scores and formative feedback for thousands of free-response quiz submissions.<n>In a setting with no single ground-truth label, we evaluate performance against official teaching-assistant grades, student surveys, and independent human review.
• Score: 9.922581736690159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Grading in large undergraduate STEM courses often yields minimal feedback due to heavy instructional workloads. We present a large-scale empirical study of AI grading on real, handwritten single-variable calculus work from UC Irvine. Using OCR-conditioned large language models with structured, rubric-guided prompting, our system produces scores and formative feedback for thousands of free-response quiz submissions from nearly 800 students. In a setting with no single ground-truth label, we evaluate performance against official teaching-assistant grades, student surveys, and independent human review, finding strong alignment with TA scoring and a large majority of AI-generated feedback rated as correct or acceptable across quizzes. Beyond calculus, this setting highlights core challenges in OCR-conditioned mathematical reasoning and partial-credit assessment. We analyze key failure modes, propose practical rubric- and prompt-design principles, and introduce a multi-perspective evaluation protocol for reliable, real-course deployment. Building on the dataset and evaluation framework developed here, we outline a standardized benchmark for AI grading of handwritten mathematics to support reproducible comparison and future research.

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