TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917912139268096 |
|---|---|
| author | Echeverria, Vanessa Zhao, Linxuan Alfredo, Riordan Milesi, Mikaela Jin, Yuequiao Abel, Sophie Fan, Jie Yan, Lixiang Li, Xinyu Dix, Samantha Wotherspoon, Rosie Jaggard, Hollie Osborne, Abra Shum, Simon Buckingham Gasevic, Dragan Martinez-Maldonado, Roberto |
| author_facet | Echeverria, Vanessa Zhao, Linxuan Alfredo, Riordan Milesi, Mikaela Jin, Yuequiao Abel, Sophie Fan, Jie Yan, Lixiang Li, Xinyu Dix, Samantha Wotherspoon, Rosie Jaggard, Hollie Osborne, Abra Shum, Simon Buckingham Gasevic, Dragan Martinez-Maldonado, Roberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation Echeverria, Vanessa Zhao, Linxuan Alfredo, Riordan Milesi, Mikaela Jin, Yuequiao Abel, Sophie Fan, Jie Yan, Lixiang Li, Xinyu Dix, Samantha Wotherspoon, Rosie Jaggard, Hollie Osborne, Abra Shum, Simon Buckingham Gasevic, Dragan Martinez-Maldonado, Roberto Human-Computer Interaction 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. |
| title | TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.09930 |