CATCH – Collaborative AI for Time-Critical High-Complexity Scenarios

CATCH investigates how AI can better support people working under high cognitive load, time pressure, and uncertainty. Using emergency coordination as the primary context, the project develops adaptive human–AI systems that infer users’ functional state from interaction and physiological data and dynamically adjust automation, information filtering, and decision support. The project combines design science, experiments, AI development, and international collaboration.

Goals

The project aims to develop and validate methods for detecting functional state during demanding work, design AI systems that adapt their support in real time, and establish design principles for effective and resilient human–AI collaboration. The broader goal is to improve performance, situational awareness, trust calibration, and human oversight in high-stakes environments.

Project manager