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

TL;DR

This paper investigates how wearable eye tracking can serve as an implicit communication channel to convey user attention, enhancing collaboration with AI systems by providing contextual signals that improve understanding and reduce communication friction.

Contribution

It demonstrates the effectiveness of eye gaze signals in conveying user interests to AI, and explores their integration with vision-language models for improved interaction.

Findings

Eye tracking signals effectively map gaze to physical objects.

Gaze data improves AI understanding of user context.

Visual scanpath history enhances AI response accuracy.

Abstract

Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. Yet, these emerging contextual AI systems rely on explicit communication channels between the user and system. We hypothesize that implicit communication of the user's interests and intent would reduce friction and improve user experience when collaborating with AI agents. In this work, we explore the potential of wearable eye tracking to convey signals about user attention. We measure the eye tracking signal quality requirements to effectively map gaze traces to physical objects, then conduct experiments that provide visual scanpath history as additional context when querying vision language models. Our results show that eye tracking provides high value as a user attention signal and can convey important context about the user's current task and interests, improving understanding of contextual AI agents.