TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation

Vanessa Echeverria; Linxuan Zhao; Riordan Alfredo; Mikaela E. Milesi; Yueqiao Jin; Sophie Abel; Jie Xiang Fan; Lixiang Yan; Samantha Dix; Rosie Wotherspoon; Xinyu Li; Hollie A. Jaggard; Abra Osborne; Simon Buckingham Shum; Dragan Gasevic; Roberto Martinez-Maldonado

doi:10.1145/3706598.3713395

TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation

Vanessa Echeverria, Linxuan Zhao, Riordan Alfredo, Mikaela E. Milesi, Yueqiao Jin, Sophie Abel, Jie Xiang Fan, Lixiang Yan, Samantha Dix, Rosie Wotherspoon, Xinyu Li, Hollie A. Jaggard, Abra Osborne, Simon Buckingham Shum, Dragan Gasevic, Roberto Martinez-Maldonado

Pro Vice-Chancellor (Education)

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

13 Citations (Scopus)

3 Downloads (Pure)

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/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.

Original language	English
Title of host publication	CHI '25
Subtitle of host publication	Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Editors	Naomi Yamashita, Vanessa Evers, Koji Yatani, Xianghua (Sharon) Ding, Bongshin Lee, Marshini Chetty, Phoebe Toups-Dugas
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Number of pages	22
ISBN (Electronic)	9798400713941
DOIs	https://doi.org/10.1145/3706598.3713395
Publication status	Published - 2025
Event	Conference on Human Factors in Computing Systems (2025) - Yokohama, Japan Duration: 26 Apr 2025 → 1 May 2025

Conference

Conference	Conference on Human Factors in Computing Systems (2025)
Abbreviated title	CHI2025
Country/Territory	Japan
City	Yokohama
Period	26/04/25 → 1/05/25

Bibliographical note

Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Large-language models
sensors
teamwork
learning analytics
AI
healthcare

Access to Document

10.1145/3706598.3713395

Publisher version (open access)Final published version, 4.97 MBLicence: CC BY-NC-ND

Cite this

Echeverria, V., Zhao, L., Alfredo, R., Milesi, M. E., Jin, Y., Abel, S., Fan, J. X., Yan, L., Dix, S., Wotherspoon, R., Li, X., Jaggard, H. A., Osborne, A., Buckingham Shum, S., Gasevic, D., & Martinez-Maldonado, R. (2025). TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation. In N. Yamashita, V. Evers, K. Yatani, X. Ding, B. Lee, M. Chetty, & P. Toups-Dugas (Eds.), CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems Article Paper 309 Association for Computing Machinery (ACM). https://doi.org/10.1145/3706598.3713395

Echeverria, Vanessa ; Zhao, Linxuan ; Alfredo, Riordan et al. / TeamVision : an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation. CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. editor / Naomi Yamashita ; Vanessa Evers ; Koji Yatani ; Xianghua (Sharon) Ding ; Bongshin Lee ; Marshini Chetty ; Phoebe Toups-Dugas. New York : Association for Computing Machinery (ACM), 2025.

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title = "TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation",

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/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.",

keywords = "Large-language models, sensors, teamwork, learning analytics, AI, healthcare",

author = "Vanessa Echeverria and Linxuan Zhao and Riordan Alfredo and Milesi, \{Mikaela E.\} and Yueqiao Jin and Sophie Abel and Fan, \{Jie Xiang\} and Lixiang Yan and Samantha Dix and Rosie Wotherspoon and Xinyu Li and Jaggard, \{Hollie A.\} and Abra Osborne and \{Buckingham Shum\}, Simon and Dragan Gasevic and Roberto Martinez-Maldonado",

note = "Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; Conference on Human Factors in Computing Systems (2025), CHI2025 ; Conference date: 26-04-2025 Through 01-05-2025",

year = "2025",

doi = "10.1145/3706598.3713395",

language = "English",

editor = "Naomi Yamashita and Vanessa Evers and Koji Yatani and Ding, \{Xianghua (Sharon)\} and Bongshin Lee and Marshini Chetty and Phoebe Toups-Dugas",

booktitle = "CHI '25",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

}

Echeverria, V, Zhao, L, Alfredo, R, Milesi, ME, Jin, Y, Abel, S, Fan, JX, Yan, L, Dix, S, Wotherspoon, R, Li, X, Jaggard, HA, Osborne, A, Buckingham Shum, S, Gasevic, D & Martinez-Maldonado, R 2025, TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation. in N Yamashita, V Evers, K Yatani, X Ding, B Lee, M Chetty & P Toups-Dugas (eds), CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems., Paper 309, Association for Computing Machinery (ACM), New York, Conference on Human Factors in Computing Systems (2025), Yokohama, Japan, 26/04/25. https://doi.org/10.1145/3706598.3713395

TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation. / Echeverria, Vanessa; Zhao, Linxuan; Alfredo, Riordan et al.
CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ed. / Naomi Yamashita; Vanessa Evers; Koji Yatani; Xianghua (Sharon) Ding; Bongshin Lee; Marshini Chetty; Phoebe Toups-Dugas. New York: Association for Computing Machinery (ACM), 2025. Paper 309.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T2 - Conference on Human Factors in Computing Systems (2025)

AU - Echeverria, Vanessa

AU - Zhao, Linxuan

AU - Alfredo, Riordan

AU - Milesi, Mikaela E.

AU - Jin, Yueqiao

AU - Abel, Sophie

AU - Fan, Jie Xiang

AU - Yan, Lixiang

AU - Dix, Samantha

AU - Wotherspoon, Rosie

AU - Li, Xinyu

AU - Jaggard, Hollie A.

AU - Osborne, Abra

AU - Buckingham Shum, Simon

AU - Gasevic, Dragan

AU - Martinez-Maldonado, Roberto

N1 - Copyright the Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2025

Y1 - 2025

N2 - 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/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.

AB - 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/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.

KW - Large-language models

KW - sensors

KW - teamwork

KW - learning analytics

KW - AI

KW - healthcare

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A2 - Yamashita, Naomi

A2 - Evers, Vanessa

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ER -

Echeverria V, Zhao L, Alfredo R, Milesi ME, Jin Y, Abel S et al. TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation. In Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors, CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery (ACM). 2025. Paper 309 doi: 10.1145/3706598.3713395

TeamVision: an AI-powered learning analytics system for supporting reflection in team-based healthcare simulation

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