Different forms of intelligent systems learn about their users to provide more personalized services through the using process. However, the learning processes of these systems are designed with little consideration of user agency, resulting in hindering users from making the best use of the system. In this project, we propose Teaching-Learning Interaction (TLI) as a new form of interaction that supports user agency by letting users reflectively shepherd an intelligent system’s manner of learning. To solidify TLI, we investigate user needs and experiences in teaching their intelligent systems from various domains (i.e., recommender systems, autonomous vehicles, etc.). For example, while users have little control of modifying the driving styles of current autonomous vehicles (AVs), the concept of TLI enables users to guide their driving-style preferences to adjust AV’s driving more fit to their needs. In our proposal, user agency is a core driving factor that is in control of AV’s learning process. While utilizing the empirical findings to develop and evaluate more detailed interaction scenarios and design guidelines for the intelligent systems, we aim to collect multiple design cases adequate for TLI proposal. Accompanied by TLI and user agency, users will be able to better personalize services for themselves.
Intelligent systems in everyday lives learn about their users to tailor services over time. However, these systems are often designed with little consideration of user agency on their learning processes, hindering users from taking full advantage of the systems. In this paper, we propose Teaching-Learning Interaction (TLI) as a new form of interaction that affords user agency by letting users reflectively shepherd an intelligent system’s manner of learning. Given such agency, users will be able to better personalize services for themselves. We first draw on Schön’s notion of knowing-in-action and reflective practice to theoretically ground our concept. We then present the resulting definition of TLI and three design qualities, which are further concretized with three design examples. We end with discussion on the implications of TLI for HCI design.
Matching the autonomous vehicle’s (AV) driving style to its user’s preference is core to a satisfactory user experience. The recent HCI community has undertaken a significant amount of research to understand user-preferred driving styles in AVs. Due to its multifaceted nature, understanding these driving preferences is difficult unless users take roles in an adaptive system and share their needs explicitly. However, there is a lack of a proper channel for users to express their driving-style needs in AVs. To bridge this gap, we suggest a user’s preferred driving-style guidance using voice as a novel input channel for human-centric AV control. We conducted a Wizard-of-Oz driving study on real roads, aiming to explore the guiding experience with the AV agent to reflect their driving-style preferences. This paper presents the value of driving-style guidance along with its burden to users, and concludes with its implications in designing a better AV-guiding experience.
Intelligent systems learn and evolve depending on what kinds of input are given and how people actually use them after deployment. While such a characteristic may be a troubling property for AI user experience designers, it also imbues an intelligent system with an open-ended quality, empowering end-users to ‘design’ their own system in use to achieve more desired experiences. In light of this, we conducted in-depth interviews with 16 users of various AI-based everyday recommender systems, investigating how people design their AI user experiences in actual use contexts. Exploring people’s current experiences of adopting and adapting those systems to achieve their own desired experiences, we discovered three styles of end-user design of their experiences: teaching, resisting, and repurposing. We end with a discussion of the implications of our findings, recognizing end-users’ motivation to challenge a prescribed experience of an intelligent system.