Web search is a key digital literacy skill that can be particularly challenging for people with dyslexia, a common learning disability that affects reading and spelling skills in about 15% of the English-speaking population. In this paper, we collected and analyzed eye-tracking, search log, and self-report data from 27 participants (14 with dyslexia) to confirm that searchers with dyslexia struggle with all stages of the search process and have markedly different gaze patterns and search behavior that reflect the strategies used and challenges faced. Based on these findings, we discuss design implications to improve the cognitive accessibility of web search.

Individuals' personal information collections (their emails, files, appointments, web searches, contacts, etc) offer a wealth of insights into the organization and structure of their everyday lives. In this paper we address the task of learning representations of personal information items to capture individuals' ongoing activities, such as projects and tasks: Such representations can be used in activity-centric applications like personal assistants, email clients, and productivity tools to help people better manage their data and time. We propose a graph-based approach that leverages the inherent interconnected structure of personal information collections, and derive efficient, exact techniques to incrementally update representations as new data arrive. We demonstrate the strengths of our graph-based representations against competitive baselines in a novel intrinsic rating task and an extrinsic recommendation task.

As AI technology advances, it offers promising opportunities to improve educational outcomes when integrated with an overall learning experience. We investigate forward-looking interactive reading experiences that leverage both automatic question generation and analysis of attention signals, such as gaze tracking, to improve short- and long-term learning outcomes. We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior. We also compare the effectiveness of manually-written questions with those produced by Automatic Question Generation (AQG). We further investigate gaze and reading patterns indicative of low vs. high learning in both short- and long-term scenarios (one-week followup). We show AQG-generated adjunct questions have promise as a way to scale to a wide variety of reading material where the cost of manually curating questions may be prohibitive.

Gaze-tracking technologies can enable computer access for users who are unable to use standard input devices. However, using gaze as input poses challenges for interactions that require visual planning, like playing a digital instrument. We explore how multimodality can support eye-controlled musical expression by designing different multi-modal gaze interactions around a digital instrument we call Dueto. We tackle three design goals: creating an instrument that is explorable, easy to learn, and allows feature controllability. We showcase three different multimodal interactions for music playing such as eye gaze only, gaze + switch, and gaze + partner mode.

A novel gaze-assisted reading technique uses the fact that in linear reading, the looking behavior of the reader is readily predicted. We introduce the AutoPager "page turning" technique, where the next bit of unread text is rendered in the periphery, ready to be read. This approach enables continuous gaze-assisted reading without requiring manual input to scroll: the reader merely saccades to the top of the page to read on. We demonstrate that when the new text is introduced with a gradual cross-fade effect, users are often unaware of the change: the user's impression is of reading the same page over and over again, yet the content changes. We present a user evaluation that compares AutoPager to previous gaze-assisted scrolling techniques. AutoPager may offer some advantages over previous gaze-assisted reading techniques, and is a rare example of exploiting "change blindness" in user interfaces.

In this paper, we study how to leverage calendar information to help with email re-finding using a zero-query prototype, Calendar-Aware Proactive Email Recommender System (CAPERS). CAPERS proactively selects and displays potentially useful emails to users based on their upcoming calendar events with a particular focus on meeting preparation. We approach this problem domain through a survey, a task-based experiment, and a field experiment comparing multiple email recommenders in a large technology company. We first show that a large proportion of email access is related to meetings and then study the effects of four email recommenders on user perception and engagement taking into account four categories of factors: the amount of email content, email recency, calendar-email content match, and calendar-email people match. We demonstrate that these factors all positively predict the usefulness of emails to meeting preparation and that calendar-email content match is the most important. We study the effects of different machine learning models for predicting usefulness and find that an online-learned linear model doubles user engagement compared with the baselines, which suggests the benefit of continuous online learning.

For eye tracking to become a ubiquitous part of our everyday interaction with computers, we first need to understand its limitations outside rigorously controlled labs, and develop robust applications that can be used by a broad range of users and in various environments. Toward this end, we collected eye tracking data from 80 people in a calibration-style task, using two different trackers in two lighting conditions. We found that accuracy and precision can vary between users and targets more than six-fold, and report on differences between lighting, trackers, and screen regions. We show how such data can be used to determine appropriate target sizes and to optimize the parameters of commonly used filters. We conclude with design recommendations and examples how our findings and methodology can inform the design of error-aware adaptive applications.

We present cascading dwell gaze typing, a novel approach to dwell-based eye typing that dynamically adjusts the dwell time of keys in an on-screen keyboard based on the likelihood that a key will be selected next, and the location of the key on the keyboard. Our approach makes unlikely keys more difficult to select and likely keys easier to select by increasing and decreasing their required dwell times, respectively. To maintain a smooth typing rhythm for the user, we cascade the dwell time of likely keys, slowly decreasing the minimum allowable dwell time as a user enters text. Cascading the dwell time affords users the benefits of faster dwell times while causing little disruption to users' typing cadence. Results from a longitudinal study with 17 non-disabled participants show that our dynamic cascading dwell technique was significantly faster than a static dwell approach. Participants were able to achieve typing speeds of 12.39 WPM on average with our cascading technique, whereas participants were able to achieve typing speeds of 10.62 WPM on average with a static dwell time approach. In a small evaluation conducted with five people with ALS, participants achieved average typing speeds of 9.51 WPM with our cascading dwell approach. These results show that our dynamic cascading dwell technique has the potential to improve gaze typing for users with and without disabilities.