Smart products and services around us are increasingly becoming intelligent in supporting our everyday lives. Like personal assistants, they aim to provide personalized assistance so that individual users can more effectively manage their schedules and events, health and behaviors, and social relationships with others. In addition, these assistant-like systems have great potentials to evolve over time, as they can learn about their users and improve the services in a way that are more fit to individual users. Meanwhile, it is important to make such evolution not just led by technologies, but by deeply understanding users’ experiences of these evolving systems in a human-centered perspective, because algorithm-driven service evolution may not always be relevant, necessary, and pleasant in a given user’s own life contexts. However, research on designing interactions with evolving intelligent systems and user experience is still in its early stage. As a step forward to fill this gap, this research investigates what users think as a meaningful evolution of intelligent assistant systems and how they would like to collaborate with the systems to make such evolution possible. By doing so, this research aims to provide theoretical and practical knowledge for designing intelligent assistant systems, which truly support people and provide pleasant experiences of the systems.
Recently, self-tracking devices such as wearable activity trackers have become more available to end users. While these emerging products are imbued with new characteristics in terms of human-computer interaction, it is still unclear how to describe and design for user experience in such devices. In this paper, we present a three-week field study, which aimed to unfold users' experience with wearable activity trackers. Drawing from Knapp's model of interaction stages in interpersonal relationship development, we propose three stages of relationship development between users and self-tracking devices: initiation & experimentation, intensifying & integration, and stagnation & termination. We highlight the challenges in each stage and design opportunities for future self-tracking devices.