AI-enhanced learning of teamskills in education for high-risk professions (SimAI)

Contact person: Crina Damsa

Status: Pre-project

SimAI examines how AI-supported technologies offer new ways to understand feedback, communication, coordination and stress during teamwork among students and professionals in healthcare and police training. High-stakes professions rely on simulation training to build teamwork and decision-making skills under pressure. SimAI develops an AI-enhanced solution that transforms critical team performance data into feedback moments and investigates whether and how this automated feedback fosters learning of team skills.

What makes this novel is combining a reflection methodology, multimodal data and generative AI to give participants a near-real-time, evidence-based picture of their own performance — including automated identification of team communication and actions, tagging of key events, and potentially AI-generated "best practice" video examples.

Our dream outcome is new knowledge on evidence-based learning of collaborative behaviour, and a validated, AI-enhanced feedback system that makes debriefing more objective, timely and pedagogically effective.

In practice, SimAI offers a methodology and digital platform for nursing and police education, with potential to redefine evidence-based, scalable learning across high-risk professional domains that use simulation training. Training programmes and other high-risk environments — including emergency services and defence — could potentially use SimAI to give students and professionals clearer, data-backed feedback on teamwork, improving both training quality and preparedness for real emergencies.