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

2026-02-05

AI Summary

This study addresses the limitations of traditional standardized patients—high cost, poor scalability, and inconsistent performance—as well as the shortcomings of existing AI-based standardized patients, which, despite conversational capabilities, often lack the authenticity required for effective clinical training and fail to gain medical students’ trust. Through interviews with 12 clinical students and three rounds of co-design workshops, the research identifies six learner-centered core requirements and proposes a design paradigm centered on “pedagogical usability” rather than mere “dialogue realism.” Positioning AI standardized patients as educational tools that support deliberate practice, the work leverages user-centered design methods and large language model technology to develop a reusable AI system framework and conceptual workflow. This approach significantly enhances learners’ trust and engagement, offering key design principles and a practical pathway for AI-augmented medical education.

Technology Category

• Humans and AI: User Experience and Usability
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• standardized patients
• AI standardized patients
• clinical communication training
• large language models
• medical education

Innovation

• AI standardized patients
• co-design
• instructional usability
• deliberate practice
• medical education