Cognitive Workload of Humans Using Artificial Intelligence Systems: Towards Objective Measurement Applying Eye-Tracking Technology

Replying to corresponding research calls I experimentally investigate whether a higher level of artificial intelligence support leads to a lower user cognitive workload. Applying eye-tracking technology I show how the user’s cognitive workload can be measure more objectively by capturing eye movements and pupillary responses. Within a laboratory environment which adequately reflects a realistic working situation, the probands use two distinct systems with similar user interfaces but very different levels of artificial intelligence support. Recording and analyzing objective eye-tracking data (i.e. pupillary diameter mean, pupillary diameter deviation, number of gaze fixations and eye saccade speed of both left and right eyes) – all indicating cognitive workload – I found significant systematic cognitive workload differences between both test systems. My results indicated that a higher AI-support leads to lower user cognitive workload.