ArticleFamily medicine2025
Evaluating the Effectiveness of ChatGPT Versus Human Proctors in Grading Medical Students' Post-OSCE Notes.
Article in Family medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Benefits of Chain-of-Thought Prompting for Clinical Record Rubric Evaluation in Undergraduate Medical Education: Experimental Evaluation Study With Medical Faculty.JMIR medical education · 2026Article
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesArtificial intelligence (AI) tools have potential utility in multiple domains, including medical education. However, educators have yet to evaluate AI's assessment of medical students' clinical reasoning as evidenced in note-writing. This study compares ChatGPT with a human proctor's grading of medical students' notes.
methodsA total of 127 subjective, objective, assessment, and plan notes, derived from an objective structured clinical examination, were previously graded by a physician proctor across four categories: history, physical exam, differential diagnosis/thought process, and treatment plan. ChatGPT-4, using the same rubric, was tasked with evaluating these 127 notes. We compared AI-generated scores with proctors' scores using t tests and χ2 analysis.
resultsThe grades assigned by ChatGPT were significantly different than those assigned by proctors in history (P<.001), differential diagnosis/thought process (P<.001), and treatment plan (P<.001). Cohen's d was the largest for treatment plan at 1.25. The differences led to a significant difference in students' mean cumulative grade (proctor 23.13 [SD=2.84], ChatGPT 24.11 [SD 1.27], P<.001), affecting final grade distribution (P<.001). With proctor grading, 81 of the 127 (63.8%) notes were honors and 46 of the 127 (36.2%) were pass. ChatGPT gave significantly more honors (118/127 [92.9%]) than pass (9/127 [7.1%]).
conclusionsWhen compared to a human proctor, ChatGPT-4 assigned statistically different grades to students' SOAP notes, although the practical difference was small. The most substantial grading discrepancy occurred in the treatment plan. Despite the slight numerical difference, ChatGPT assigned significantly more honors grades. Medical educators should therefore investigate a large language model's performance characteristics in their local grading framework before using AI to augment grading of summative, written assessments.
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Registered trials
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