  Gaze tracking devices have the potential to greatly expand interactivity, yet
miscalibration remains a significant barrier to use. As devices miscalibrate,
people tend to compensate by intentionally offsetting their gaze, which makes
detecting miscalibration from eye signals difficult. To help address this
problem, we propose a novel approach to seamless calibration based on the
insight that the system's model of eye gaze can be updated during reading (user
does not compensate) to improve calibration for typing (user might compensate).
To explore this approach, we built an auto-calibrating gaze typing prototype
called EyeO, ran a user study with 20 participants, and conducted a
semi-structured interview with 6 ALS community stakeholders. Our user study
results suggest that seamless autocalibration can significantly improve typing
efficiency and user experience. Findings from the semi-structured interview
validate the need for autocalibration, and shed light on the prototype's
potential usefulness, desired algorithmic and design improvements for users.
