Human or LLM as Standardized Patients? A Comparative Study in Medical Education

ACL ARR 2026 January Submission7567 Authors

Keywords: Virtual Standardized Patients, Multi-agent Framework, Large Language Models

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Standardized Patient Simulation, Multi-Agent Framework, Medical Education

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Data resources, Data analysis

Languages Studied: Chinese, English

Submission Number: 7567