Agreement, calibration, and failure of three large language models as high-stakes multimodal ospe graders: a comparative psychometric analysis.

BACKGROUND: Medical education faces increasing demand for scalable grading solutions. Large language models have been proposed as automated graders for high-stakes assessments, but evidence for their reliability in multimodal practical examinations remains limited. METHODS: This retrospective inter-rater reliability study compared three LLMs: ChatGPT-4o, Gemini 2.5 Flash, and Claude 3.5 Haiku with original human reference scores on 19 integrated anatomy, histology, and physiology objective structured practical examination items completed by 309 pre-medical students. Human reference scores were assigned during live summative grading by two content experts who divided items, with each item marked by one expert across all students; no human inter-rater reliability estimate was available. Items required simultaneous image interpretation and short-answer responses. Models were evaluated using zero-shot prompting reflecting deployment-realistic conditions. Agreement was assessed using Spearman's ρ, Cohen's κ, intraclass correlation coefficients, and Bland-Altman analysis. Item-level gap analysis compared student success rates with LLM performance across items. RESULTS: Rank-order correlations were strong across all models (ρ = 0.784-0.921), but categorical agreement diverged substantially. Pass/fail agreement ranged from 52.1% (ChatGPT; κ = 0.067, slight) to 84.1% (Claude; κ = 0.680, substantial). Bland-Altman analysis showed inconsistent systematic bias: Claude over-scored by +2.5 points, Gemini under-scored by -5.0 points, and ChatGPT by -10.0 points. Item-level gap analysis highlighted three recurring divergence patterns: non-standard histological staining, three-dimensional spatial reasoning from two-dimensional images, and semantic inflexibility in short-answer evaluation. The most extreme case was a pelvic three-dimensional model item on which 93.3% of students succeeded by human grading but all three LLMs assigned a mean score of zero. CONCLUSIONS: Strong rank-order correlation alone does not support LLM grading for categorical decisions in high-stakes assessment contexts. LLMs may serve as assessment assistants for first-pass ranking and discrepancy flagging, but item-level human review of flagged discordances, error-profile monitoring, and preserved human authority over pass/fail decisions are required before high-stakes deployment.