Large Scale AI Grading of Handwritten Physics Assessments: Score Agreement and Olympiad Team Selection Outcomes

Abstract:Multimodal AI can read handwritten physics solutions, but high-stakes grading requires agreement with official scores and outcomes. This study evaluated GPT-5.5-based grading on 10364 scanned pages from 520 handwritten submissions by 416 unique candidates or students across three assessments: a national Physics Olympiad theory examination, the final Olympiad selection camp with theory and experiment components, and a university quantum-mechanics examination. Each submission was graded twice by AI using the official rubrics. The second round used revised page-by-page and evidence-location instructions developed after first-round disagreement analysis. During grading, AI did not see official human marks or AI--human comparisons. Total-score correlations with official marks were high (0.91--0.97). For the final Olympiad selection, AI recovered the same five-student team as official grading. The second round improved aggregate question-part agreement, especially where first-round disagreements were larger. The main difficulty remained exact partial-credit grading, especially in experimental work. Reliable AI grading therefore depends on detailed rubrics and should be used as a second reader or audit tool under examiner control.

Physics Education (physics.ed-ph); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2608.20521 [physics.ed-ph]

(or arXiv:2608.20521v1 [physics.ed-ph] for this version)

https://doi.org/10.48550/arXiv.2608.20521 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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