Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students’ clinical and communication skills through structured patient interviews. In France, however, the organization of training sessions is limited by human and logistical constraints, restricting students’ access to repeated practice and structured feedback. Recent advances in Natural Language Processing (NLP) and Large Language Models (LLMs) now offer the opportunity to automatically evaluate such medical interviews, thereby alleviating the need for human examiners during training. Yet, real French OSCE annotated transcripts remain extremely scarce, limiting reproducible research and reliable benchmarking. To address these challenges, we investigate the use of LLMs for both generating and evaluating French OSCE dialogues in a low-resource context. We introduce a controlled pipeline that produces synthetic doctor–patient interview transcripts guided by scenario-specific evaluation criteria, combining ideal and perturbed performances to simulate varying student skill levels. The resulting dialogues are automatically silver-labeled through an LLM-assisted framework supporting adjustable evaluation strictness. Benchmarking multiple open-source and proprietary LLMs shows that mid-size models (≤32B parameters) achieve accuracies comparable to GPT-4o (~90%) on synthetic data, highlighting the feasibility of locally deployable, privacy-preserving evaluation systems for medical education.

“语义依存图是NLP处理语义的深层分析方法,能够对句子中词与词之间的语义进行分析。该文针对古代汉语特点,在制定古代汉语语义依存图标注规范的基础上,以《二十四史》为语料来源,完成标注了规模为3000句的古代汉语语义依存图库,标注一致性的kappa值为78.83%。通过与现代汉语语义依存图库的对比,对依存图库基本情况进行统计,分析古代汉语的语义特色和规律。统计显示,古代汉语语义分布宏观上符合齐普夫定律,在语义事件描述上具有强烈的历史性叙事和正式文体特征,如以人物纪传为中心,时间、地点等周边角色描述细致,叙事语言冷静客观,缺少描述情态、语气、程度、时间状态等的修饰词语等。 "