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"It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners

2026·0 Zitationen·arXiv (Cornell University)Open Access
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0

Zitationen

9

Autoren

2026

Jahr

Abstract

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.

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Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic SkillsPatient-Provider Communication in Healthcare
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