Real time prediction of workload in complex dynamic environments (2026-2030)

Aim: The aim of this project is to develop a computational model that can be used in real time to predict the point at which a human operator is likely to become cognitively overloaded. Significance: Cognitive overload is a critical safety risk that needs to be managed in modern work settings, yet it is extremely difficult to predict the onset of overload, because of the variability in the strategies that people use to manage task demands. Outcomes: The expected outcome is a model that uses advanced computational methods to estimate workload in real time and predict overload before it occurs. Benefits: The model can be used to ensure that workload of human operators remains within safe limits, reducing the risk of catastrophic failure.