Using eye-tracking data for high-level user modeling in adaptive interfaces
Cristina Conati, Christina Merten, Saleema Amershi, Kasia Müldner · 2007
In recent years, there has been substantial research on ex-ploring how AI can contribute to Human-Computer In-teraction by enabling an interface to understand a user’s needs and act accordingly. Understanding user needs is especially challenging when it involves assessing the user’s high-level mental states not easily reflected by in-terface actions. In this paper, we present our results on using eye-tracking data to model such mental states dur-ing interaction with adaptive educational software. We then discuss the implications of our research for Intelli-gent User Interfaces. Introduction1 One of the main challenges in devising agents that can act intelligently is to endow them with the capability of un-