{"ID":2842766,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14783","arxiv_id":"2511.14783","title":"Human or LLM as Standardized Patients? A Comparative Study for Medical Education","abstract":"Standardized patients (SPs) are indispensable for clinical skills training but remain expensive and difficult to scale. Although large language model (LLM)-based virtual standardized patients (VSPs) have been proposed as an alternative, their behavior remains unstable and lacks rigorous comparison with human standardized patients. We propose EasyMED, a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. We also introduce SPBench, a human-grounded benchmark with eight expert-defined criteria for interaction-level evaluation. Experiments show that EasyMED more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. A four-week controlled study further demonstrates learning outcomes comparable to human SP training, with stronger early gains for novice learners and improved flexibility, psychological safety, and cost efficiency.","short_abstract":"Standardized patients (SPs) are indispensable for clinical skills training but remain expensive and difficult to scale. Although large language model (LLM)-based virtual standardized patients (VSPs) have been proposed as an alternative, their behavior remains unstable and lacks rigorous comparison with human standardiz...","url_abs":"https://arxiv.org/abs/2511.14783","url_pdf":"https://arxiv.org/pdf/2511.14783v3","authors":"[\"Bingquan Zhang\",\"Xiaoxiao Liu\",\"Yuchi Wang\",\"Lei Zhou\",\"Qianqian Xie\",\"Benyou Wang\"]","published":"2025-11-12T11:05:41Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.CY\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}