它说话像病人，但感觉不同：与医学学习者共同设计AI标准化病人
"It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners
AI总结：
本文探讨了AI标准化病人在医学沟通培训中的应用，通过调研发现学习者对AI-SP的期望，并提出以学习者为中心的设计需求，强调教学实用性而非对话真实性的重要性。
摘要：
标准化病人（SPs）在临床沟通培训中起核心作用，但成本高、难以扩展且不一致。基于大型语言模型（LLM）的AI标准化病人（AI-SPs）提供了灵活、按需的练习方式，但学习者常反馈AI-SP说话像病人但感觉不同。我们采访了12名临床年医学学生，并进行了三次协同设计工作坊，探讨学习者在SP互动中的限制以及对AI-SP的期望。我们识别出六个以学习者为中心的需求，将其转化为AI-SP设计要求，并综合出一个概念流程。我们的发现将AI-SPs定位为有意识练习的工具，并表明教学实用性而非对话真实性单独驱动学习者的信任、参与和教育价值。
英文摘要：
Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large language model (LLM) based AI standardized patients (AI-SPs) promise flexible, on-demand practice, yet learners often report that they talk like a patient but feel different. We interviewed 12 clinical-year medical students and conducted three co-design workshops to examine how learners experience constraints of SP encounters and what they expect from AI-SPs. We identified six learner-centered needs, translated them into AI-SP design requirements, and synthesized a conceptual workflow. Our findings position AI-SPs as tools for deliberate practice and show that instructional usability, rather than conversational realism alone, drives learner trust, engagement, and educational value.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
