Characterizing large language model generative artificial intelligence variability in the production of objective structured clinical examination stations

Background. Designing high-quality Objective Structured Clinical Examination (OSCE) stations is a time-consuming process. Generative artificial intelligence (AI) represents a promising path to accelerate content creation by automating the generation of scenarios. A growing number of AI tools is now available for this purpose. Objective. To assess the variability between generative AI models in their ability to produce OSCE stations in the field of paediatrics. Methods. A structured prompt was developed based on the French national OSCE guidelines for medical education. Five distinct AI models were provided with this prompt, alongside the neonatal jaundice chapter from the French pediatric reference textbook, to generate 6 complete OSCE stations. Results. Prompt compliance was high for ChatGPT 5.1, ChatGPT 5.2, Gemini 3.0 Pro, and Claude Opus 4.5, while it was lower for Grok 4.1. Expert-rated quality was generally high, with few factual errors or missing information across models. However usability differed significantly between models. This was also true for several quality dimensions such as checklist clarity, embedding of checklist answers within vignettes, and ease of standardized patient formation. ChatGPT 5.1 required the most revisions and Gemini most often rated usable as is. Significant inter-model differences were observed in diagnostics, only with ChatGPT 5.1 sampling all three neonatal jaundice categories. Contextual variables showed systematic narrowing across models. Clinical grid density was consistent (10-12 items per station), but thematic distribution differed markedly. Soft skills coverage varied significantly across models (p=0.002), none of them consistently representing all communication competency domains. Conclusion. Large language models can generate structurally compliant OSCE stations, but surface compliance conceals substantive inter-model differences in diagnostic coverage, contextual diversity, and soft skills representation, that compromise content validity. No model currently meets the criteria for unsupervised deployment in a summative assessment bank. The choice of model carries pedagogical implications and expert curation remains essential before integration into high-stakes assessment workflows.