Enterprise AI Analysis
TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation
An AI-powered multimodal learning analytics (MMLA) system capturing voice, body rotation, and positioning data to guide debriefs in healthcare simulation, fostering improved teamwork and communication skills.
Quantifying Team Performance & Engagement
TeamVision offers unprecedented clarity into complex team dynamics and communication, driving measurable improvements in critical healthcare training environments.
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Students in Study
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Debrief Sessions Analyzed
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Educator Confidence Boost
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Ward Map Accuracy (Educators)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
TeamVision's Multimodal Capabilities
TeamVision captures voice presence, automated transcriptions, body rotation, and positioning data using multimodal sensors. Educators access a dashboard for on-demand visualisations, while students view analytics on a shared screen, fostering reflective debriefing after team-based healthcare simulations. This holistic data capture provides a comprehensive view of team dynamics.
Human-Centered Design Framework
Our design process, guided by educators through iterative workshops, focused on four key considerations:
- Facilitating Constructive Discussions (D1): Enable sensitive topic discussions without shaming.
- Providing Nuanced Discussions (D2): Offer objective data for detailed exploration of team dynamics.
- Customising Data and Visualisations (D3): Allow educators to tailor presented data to specific learning objectives.
- Supporting Manual Tagging and Annotation (D4): Integrate tools for educators to highlight key moments during live observations.
Core Visualisation Insights
TeamVision features four core visualisations designed to enhance reflection and debriefing:
- The Priority Chart for task allocation and prioritisation strategies.
- The Speech and Location Ward Map for visualising spatial interaction and speaking time.
- The Speech Sociogram for representing communication patterns and roles.
- The Communication Network for analysing dialogue content and communication strategies.
TeamVision Design Process: Educator-Centric Iteration
Needs Elicitation
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Low-Fidelity Prototyping
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Mid-Fidelity Refinement
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High-Fidelity User Testing
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In-the-Wild Study Deployment
| Visualisation | Primary Insight | Educator Perception (Usefulness) | Student Perception (Accuracy/Trust) |
|---|---|---|---|
| Priority Chart | Task Prioritisation & Distribution | Useful for summarising team activities, task allocation | Generally accurate, some nuance issues (66% accurate) |
| Ward Map | Spatial Dynamics & Coordination | Most useful for team positions, movement, patient care | Highly accurate (100% educators, >66% students) |
| Sociogram | Communication Patterns & Roles | Favoured for multidisciplinary communication patterns | Generally effective, minor data issues (86% accurate) |
| Communication Network | Dialogue Content & Strategies | Useful for high-level strategies (e.g., closed-loop comms) | Mixed reviews, lacked context/details (53% accurate) |
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Educator Confidence Boost after Adoption
Initial unfamiliarity with new AI-powered tools posed a challenge, but consistent use led to a significant increase in educators' comfort and confidence with TeamVision, improving their teaching practices.
In-the-Wild Study: Real-World Impact in Healthcare Simulation
TeamVision was deployed in an undergraduate nursing course with 56 teams (221 students) across 26 debrief sessions. Educators used the system as part of regular curriculum, providing insights into its practical integration and effectiveness in supporting reflection on teamwork and communication. Post-hoc interviews with 15 students and 5 educators explored usefulness, accuracy, and trust, revealing that AI-powered systems hold significant potential for real-world educational settings.
Calculate Your Potential AI-Driven ROI
Estimate the efficiency gains and cost savings your organization could realize by implementing AI-powered learning analytics.
Estimated Annual Savings
Training Hours Reclaimed Annually
Your AI Implementation Roadmap
A phased approach to integrating TeamVision into your existing training and development workflows.
Phase 1: Discovery & Strategy
Initial consultation and assessment of your current training practices, identifying key areas where AI can drive the most impact. Define clear objectives and success metrics for TeamVision integration.
Phase 2: Pilot Program & Customization
Deploy TeamVision in a pilot environment, customising its features and visualisations to align with your specific learning scenarios and debriefing practices. Gather initial feedback from educators and learners.
Phase 3: Full-Scale Integration & Training
Roll out TeamVision across your organisation, providing comprehensive training for educators and ongoing support. Establish best practices for using AI-powered analytics in debriefing sessions.
Phase 4: Optimization & Advanced Analytics
Continuously monitor performance, collect feedback, and iterate on TeamVision's features. Explore advanced AI applications, such as predictive analytics for team performance or adaptive learning pathways.
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