TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation
🤖 AI Summary
In medical simulation training, video debriefing is often hindered by inefficient manual analysis, insufficient data support, and the difficulty of quantifying team collaborative behaviors. To address these challenges, this study introduces the first lightweight, AI-driven multimodal learning analytics system specifically designed for healthcare team simulation education. The system enables non-intrusive, real-time quantification of team interactions by integrating voice activity detection, automatic speech recognition (ASR) transcription, markerless pose estimation, and spatial position tracking. It further provides an interactive visualization dashboard to support structured, instructor-led debriefing. A field study involving 56 healthcare teams (221 students) and 6 instructors demonstrated significant improvements in debriefing flexibility and data-driven decision-making. User feedback confirmed the system’s accuracy and trustworthiness, while also identifying opportunities for enhancing contextual understanding and clinical judgment support.
Application Category
📝 Abstract
Healthcare simulations help learners develop teamwork and clinical skills in a risk-free setting, promoting reflection on real-world practices through structured debriefs. However, despite video's potential, it is hard to use, leaving a gap in providing concise, data-driven summaries for supporting effective debriefing. Addressing this, we present TeamVision, an AI-powered multimodal learning analytics (MMLA) system that captures voice presence, automated transcriptions, body rotation, and positioning data, offering educators a dashboard to guide debriefs immediately after simulations. We conducted an in-the-wild study with 56 teams (221 students) and recorded debriefs led by six teachers using TeamVision. Follow-up interviews with 15 students and five teachers explored perceptions of its usefulness, accuracy, and trustworthiness. This paper examines: i) how TeamVision was used in debriefing, ii) what educators found valuable and challenging, and iii) perceptions of its effectiveness. Results suggest TeamVision enables flexible debriefing and highlights the challenges and implications of using AI-powered systems in healthcare simulation.
Problem
Research questions and friction points this paper is trying to address.
Medical Simulation Training
Video Review
Data Summarization and Guidance
Innovation
Methods, ideas, or system contributions that make the work stand out.
TeamVision
AI in medical education
Debriefing tool
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