Eye Gaze as a Signal for Conveying User Attention in Contextual AI Systems

Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. We explore eye tracking's role in such interaction to convey a user's attention relative to the physical environment. We hypothesize that this knowledge improves contextual understanding for AI agents. By observing hours of human-object interactions, we first measure the relationship between an eye tracker's signal quality and its ability to reliably place gaze on nearby physical objects. We then conduct experiments which relay the user's scanpath history as additional context querying multimodal agents. Our results show that eye tracking provides high value as a user attention signal and can convey information about the user's current task and interests to the agent.