How We Type with Word Suggestions: Understanding Visual Attention and Checking Behavior during Mobile Text Input

Word suggestions are commonly used when people type on mobile devices. However, how users adjust their typing behavior and visual attention to integrate the use of word suggestions and whether they are effective in doing so remains unclear, mainly due to the lack of gaze data in realistic settings. In this paper, we conduct an eye-tracking study of word suggestion users transcribing and composing text on their own phones and keyboards. Our analysis reveals that users frequently checked the suggestion list without picking a suggestion, yielding a 68% failure rate. Screen recordings show that only about half of these Failed Suggestions can be attributed to the algorithm's performance. In 43.6% of cases, users typed the word manually even though they fixated on the correctly suggested word. We analyze the dynamics of users' checking behavior and quantify the time cost of checking for word suggestions. Overall, we find that despite using word suggestions on a daily basis, users' checking behavior is not well aligned with the performance of the suggestion algorithm, resulting in a decrease of typing speed. These findings have implications for the design of intelligent text entry systems and AI support in general, and our WS-Gaze dataset will support future research in this important direction.