Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

In workplace settings, individuals' cognitive load is closely tied to both performance and safety. As collaboration between humans and artificial intelligence (AI) becomes a growing trend, the ability to capture cognitive load dynamics in a real-time and objective manner during collaborative processes has become increasingly critical. Traditionally, cognitive load has been assessed through questionnaires; however, such retrospective methods are limited in their capacity to accurately capture the moment-to-moment fluctuations in cognitive load throughout task execution. In contrast, physiological signals allow for continuous monitoring of an individual's real-time state, thereby providing a more objective measure of cognitive load. Task difficulty may influence participants' expectations, which subsequently shape their behaviors and ultimately affect cognitive load. This study implements a generalizable human-AI collaborative task that incorporates multiple levels of difficulty. The average accuracy of the AI system was disclosed to participants prior to the start of the task. In each trial, participants were given the option either to complete the task themselves or to delegate it to the AI. The aim of this protocol is to use electrocardiographic (ECG) data as physiological indicators to examine how individuals' cognitive load changes when they have the authority to allocate tasks and collaborate with AI under different difficulty levels. This study further explores the internal state dynamics revealed by physiological data during human-AI collaboration. The findings provide a scientific basis for improving collaboration models, enhancing efficiency, and leveraging the complementary strengths of humans and AI to achieve better resource integration in complex tasks.