Traditional methods of presenting clinical cases within TBL Application Exercises often involve lengthy, text-based narratives, which can be overwhelming and lead to passive learning, hindering the effectiveness of collaborative problem-solving. This project addresses the drawbacks of such static case presentations in TBL, including decreased student engagement, limited opportunities for active learning within teams, and a lack of personalized feedback for both individual and team performance.

We have introduced an innovative approach using AI-powered patient personas to create interactive and dynamic clinical simulations specifically designed for TBL Application Exercises.

These AI personas simulate real patient encounters, allowing student teams to conduct medical assessments through direct conversation, collaboratively asking questions, and exploring different diagnostic avenues. This interactive methodology significantly improves learning outcomes in TBL by enhancing student engagement and motivation through active, team participation. Furthermore, it offers a safe and controlled environment for developing crucial communication and interpersonal skills within the team context, as students practice patient-centered communication and adapt their approach based on the AI persona's responses.

The AI can also provide personalized feedback to team performance during the Application Exercise, offering insights into their collaborative diagnostic reasoning and communication techniques. Preliminary findings suggest that this AI-driven approach within TBL fosters deeper understanding of clinical concepts, improves diagnostic accuracy within teams, and cultivates essential professional skills, ultimately better preparing students for real-world clinical practice. This project demonstrates the potential of AI to revolutionize medical education and enhance the TBL framework by creating more engaging, effective, and personalized learning experiences within collaborative settings.