In resistance training, trainers employ touch guidance to help trainees control posture and activate muscles. Haptic feedback can extend this support to solitary workouts, but translating the nuances of touch into effective haptic patterns remains challenging. In this paper, we categorize the instructional messages conveyed through trainers’ touch guidance and design electrical stimulation patterns to replicate them. A preliminary study with six trainers and six trainees identified six core messages underlying touch guidance. We then designed electrical stimulation patterns for each message and refined them with two sports scientists and a UX designer, ensuring usability and grounding. Finally, sixteen gymgoers evaluated these patterns in a controlled exercise task. Participants reliably distinguished the feedback and used the instructed muscles accordingly, achieving accuracies of 97.14% and 99.22% across two sessions, cross-checked with EMG and pose estimation. These findings demonstrate that the proposed electrical stimulation feedback is intuitive and learnable.

Designing user experiences for group recommendation systems (GRS) is challenging, requiring a nuanced understanding of the influence of social interactions between users. Using Spotify Blend as a real-world case of music GRS, we conducted empirical studies to investigate intricate social interactions among South Korean users in GRS. Through a preliminary survey about Blend experiences in general, we narrowed the focus for the main study to relationships between two users who are acquainted or close. Building on this, we conducted a 21-day diary study and interviews with 30 participants (15 pairs) to probe more in-depth interpersonal dynamics within Blend. Our findings reveal that users engaged in implicit social interactions, including tacit understanding of their companions and indirect communication. We conclude by discussing the newly discovered value of GRS as a social catalyst, along with design attributes and challenges for the social experiences it mediates.

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.

Interactions with voice-based conversational agents (VCAs) in non-humanoid forms are becoming increasingly pervasive, and researches on non-humanoid VCAs engaging diverse human traits have been conducted. However, there have never been studies employing living body's physiological states to be expressed solely through the voice of such VCAs yet. As physiological expressions of such VCAs can have potential for manifesting health-related issues in a human-like way, we selected healthcare scenarios as a case for exploring novel user experiences that they can induce. We conducted design workshops for identifying design considerations and design opportunities for the physiologically expressible VCAs in the healthcare service domain. Following these findings, we designed the new concept of physiologically expressible healthcare VCAs and conducted a Wizard-of-Oz user study. Finally, we summarize the unique user experiences on physiologically expressible VCA's healthcare services and user perceptions of its physiological expressions, and discuss design implications for physiologically expressible VCAs.

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.

The adoption of self-tracking services to improve health-related behavior is increasing. Although psychologists claim that thinking about the future has a motivational impact on current behavior and cognition, few studies have explored using future forecast in self-tracking services. In this paper, we explore how future forecast information can be used in the design of self-tracking services. We conducted a four-week study that qualitatively investigated 11 participants' perceptions of and practices with future forecast information. Participants used the FutureSelf app that we developed, which forecasts dieters' future weights and expected goal achievement rates based on their current behavior. The findings reveal that predicting future weight based on prior performance induced participants to imagine their future selves and reminded them of their ultimate goals. In fact, the predictions became the participants' primary source of motivation. We also suggest design implications for self-tracking services that forecast users' future statuses.