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

A study evaluated the efficacy of GPT-5.5-based multimodal AI in grading handwritten physics assessments, including Olympiad theory and experiment components and a university quantum mechanics examination. The AI graded 10,364 scanned pages from 520 submissions across 416 unique candidates. Initial AI grading was followed by a revised round incorporating feedback from disagreement analysis, with the AI operating without knowledge of human scores or previous AI-human comparisons.

The capability for large-scale AI grading of complex, handwritten assessments presents a potential paradigm shift in educational and evaluation processes. This technology could significantly enhance efficiency and scalability in high-stakes grading environments, impacting resource allocation and the fairness of selection outcomes.

Multimodal AI, specifically GPT-5.5, can process and grade handwritten physics solutions.

Forward consideration, not a verified fact.