Effective communication is essential for safe, patient-centered care, yet traditional training—lectures, peer role-play, and standardized patient (SP) encounters—faces limits in frequency, cost, and scalability. Artificial intelligence (AI), particularly large language model (LLM)-based tools like ChatGPT, is emerging as an innovative way to address these challenges by providing accessible, repeatable, low-stakes opportunities to practice complex interactions. 

Our pilot study showed that ChatGPT can improve medical students’ confidence in delivering difficult news while highlighting limits such as reduced emotional realism. Building on this work, our follow-up study compares generative AI and SPs, exploring how AI can serve as scaffolding to prepare students for higher-stakes human interactions. Large-scale feedback identifies advantages (accessibility, immediate practice, reduced anxiety) and challenges (limited nuance, variable accuracy). 

This focus session uses these studies as a foundation and situates them within broader evidence on AI in medical education. Participants will actively engage with LLMs and explore other AI tools, including multimodal synthetic patients and virtual scenarios, to experience both possibilities and constraints. They will then collaborate in small groups to design prototype modules that integrate AI with traditional communication training, addressing pedagogy, assessment, and ethical safeguards. 

By the session’s end, participants will gain: (1) experience in AI-mediated communication exercises, (2) an understanding of evidence-based benefits and limitations, and (3) concrete, transferable strategies to implement AI-enhanced modules in their own curricula. This session emphasizes hands-on learning, active discussion, and innovation, equipping attendees with tools to thoughtfully integrate AI into medical education while improving student learning and outcomes. 

Learning Outcomes

1. Understand AI’s Role in Communication Training in Medical Education 
   • Describe current and emerging applications of AI in communication skills training in medical education. 
   • Recognize how AI complements traditional methods such as SPs and peer role-play. 
2. Evaluate AI’s Impact on Communication Training in Medical Education 
   • Critically assess strengths and limitations of AI-based communication training based on published and emerging research. 
   • Interpret student feedback on Generative AI vs. SPs, including perceptions of realism, accessibility, and effectiveness. 
3. Develop Actionable Plans
   • Design a prototype AI-enhanced educational module (e.g., breaking bad news, motivational interviewing, cultural competence).
   • Incorporate scaffolding strategies (e.g., Generative AI practice prior to SP encounters), assessment approaches, and ethical guardrails. 
4. Transfer Knowledge to Practice 
   • Identify actionable steps to pilot AI-supported communication training in their own institutions. 
   • Anticipate challenges (e.g., cost, realism, faculty buy-in) and propose strategies to address them.