Using Eye-tracking data for High-Level User Modeling in Adaptive Interfaces
By Kasia Muldner
In recent years, there has been substantial research on exploring how AI can
contribute to Human-Computer Interaction 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 interface actions. We present our results on using
eye-tracking data to model such mental states during interaction with adaptive
educational software. We then discuss the implications of our research for
Intelligent User Interfaces.