As trailblazer in streaming entertainment, Netflix has redefined how people consume media, captivating audiences with its original programming. Despite this, the streaming giant could not translate similar success with its video game offering. This paper is an outcome of a unique experimental study which examines the built-in features of interactive storytelling games and their effect on induced persuasion and exploration among game players. This research involves eighty- eight players playing two popular games from the Netflix library. It tests the effects of artificial intelligence, feature interactivity, device responsiveness and personalization features of the game on induced persuasion and exploration among gamers. Using Bayesian regression and correlation, the results help to deduce that while built-in features can effectively persuade players, they fall short in encouraging deeper exploration within the game’s narrative. It also illustrates the growing significance of artificial intelligence in human computer interaction. The study also highlights the critical role of storytelling for the gaming business and its significance in developing new visual culture.

Emphasizing problem formulation in AI literacy activities with children is vital, yet we lack empirical studies on their structure and affordances. We propose that participatory design involving teachable machines facilitates problem formulation activities. To test this, we integrated problem reduction heuristics into storyboarding and invited a university-based intergenerational design team of 10 children (ages 8-13) and 9 adults to co-design a teachable machine. We find that children draw from personal experiences when formulating AI problems; they assume voice and video capabilities, explore diverse machine learning approaches, and plan for error handling. Their ideas promote human involvement in AI, though some are drawn to more autonomous systems. Their designs prioritize values like capability, logic, helpfulness, responsibility, and obedience, and a preference for a comfortable life, family security, inner harmony, and excitement as end-states. We conclude by discussing how these results can inform the design of future participatory AI activities.

A machine teaching interface is any interface that lets anyone teach an algorithm how to classify a dataset. For my dissertation, I want to explore the use of interactive machine learning interfaces also known as teachable machines for introducing machine learning to children. At its core, such interfaces can be made accessible since they can use audio or images as input data which increases the alternate representations that can be used to communicate concepts. Specifically, I would be building interactive experiences that introduce sighted and blind children to basic concepts of machine learning.

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.

Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7–13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).

Datasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. Even when data are collected and are publicly available, it is often difficult to locate them. We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search.

Gaze based navigation with digital screens offer a hands-free and touchless interaction, which is often useful in providing a hygienic interaction experience in a public kiosk scenario. The goodness of such a navigation system depends not only on the accuracy of detecting the eye gaze but also on the ability to determine whether a user is interested in clicking a button or is just looking at the button. The time for which a user needs to gaze at a particular button before it is considered as a click action is called the dwell time. In this paper, we explore intelligent adjustment of dwell times, where mouse click events on the buttons of a given application are emulated with user gaze. A constant dwell-time for all buttons and for all users may not provide an efficient and intuitive interface. We thereby propose a model to dynamically adjust dwell-time values used to emulate user mouse click events, exploiting the user's experience with different portions of a given application. The adjustment happens at a per-user, per-button granularity, as a function of the user's (a) prior usage experience of the given button within the application and (b) Midas touch characteristics for the given button. We propose OptiDwell, inspired by the action-value method based solutions to the Multi-Armed Bandits problem, for dwell click time adaptation. We experiment OptiDwell using an interactive TV channel browsing interface application, constituting of a mix of text and image buttons, over 10 computer-savvy users generating over 9000 click tasks. We observe significant improvement of user comfort level over the sessions, quantified by (a) improved (reduced) dwell times and (b) reduced number of Midas touches in spite of faster dwell-clicks, as high as 10-fold reduction in the best case. Our work is useful for creating an interface, with accurate, fast and comfortable dwell-clicks for each interface element (e.g., buttons), and each user.

We present the design of Note Code -- a music programming puzzle game designed as a tangible device coupled with a Graphical User Interface (GUI). Tapping patterns and placing boxes in proximity enables programming these "note-boxes" to store sets of notes, play them back and activate different sub-components or neighboring boxes. This system provides users the opportunity to learn a variety of computational concepts, including functions, function calling and recursion, conditionals, as well as engage in composing music. The GUI adds a dimension of viewing the created programs and interacting with a set of puzzles that help discover the various computational concepts in the pursuit of creating target tunes, and optimizing the program made.