Why do certain interfaces feel effortless while others feel exhausting? This paper proposes Timelines and Topographies as a conceptual framework to answer this question, reinterpreting interface design through the lens of human attention. We distinguish between timelines—a linear mode where the system serializes information to offload the cognitive cost of attentional control (e.g., social media feeds)—and topographies—a spatial mode where the system’s structural relations are directly presented to the user (e.g., folder hierarchies, canvas UIs). We apply this framework to analyze a wide spectrum of interfaces, ranging from classic desktop metaphors to modern Personal Information Management tools like Slack and Notion. Furthermore, we identify Large Language Models as bidirectional translators that bridge these modes by converting complex topographic structures into conversational timelines, and vice versa. Finally, we provide design guidelines for harnessing both modes, enabling interfaces that support fluid transitions between subjective timelines and objective topographies.

Maintaining social connections via informal gathering and communication in physical workspaces is challenging. To maintain connections without causing mental strain, we propose “Async Party,” an activity that reconfigures drinking rituals through asynchronicity by combining physical triggers with digital sharing. In this activity, participants record short videos of individual toasts, ask a nearby colleague to record their “cheers” moment, and upload the short video to the dedicated Slack channel. A three-month field deployment revealed that while the filming rule was designed to spark face-to-face interaction at the moment of recording, the encounters it produced were experienced not as procedural steps but as socially meaningful moments in their own right – a social quality that extended beyond our anticipation. Members derived a sense of community vitality not by watching every video, but by peripherally perceiving the accumulation of ritualistic content. Drawing on Research through Design, this study articulates a novel design space for "asynchronous rituals" – configurations in which synchrony and asynchrony are intentionally composed into a recurring temporal cycle – and derives three transferable design strategies for sustaining low-burden belonging in hybrid environments.

In this paper, we designed the augmented reality (AR) -based visualization of metaphor that instructors in an Aikido community of practice use in everyday teaching to explain techniques. We deployed these AR-based visual metaphors in regular Aikido club training over a two-month period and examined their use through ethnographic observations and interviews. Our findings reveal three key points. First, instructors felt AR metaphors reduced misunderstandings and stress, although using metaphors created by others diminished agency unless they incorporated their own interpretations. Second, learners grasped successful techniques more quickly, increasing motivation, yet some felt anxious about teaching others later because they had not reasoned through the mechanics. Third, AR metaphors strengthened ties to the originating instructor while weakening connections with on-site members. Based on these findings, we present three design takeaways for deploying AR-based visual metaphors into communities of practice in the real-world.

In social virtual reality (VR) platforms, players can embody “non-human” avatars, which are representations whose appearance or skeletal structure diverge from typical human characteristics. This capability fosters the emergence of distinctive cultures of social interaction. This paper reports on interviews with users who employ such avatars, investigating (1) motivations for their adoption, (2) their impact on social interactions, and (3) challenges encountered when employing them in social contexts. Our findings reveal that users adopt “non-human” avatars both to escape the expectations and norms associated with the human body—thereby enabling more relaxed social communication—and to gain access to new forms of embodied experience and creative self-expression. The study also provides empirical evidence and discussion on the cultures of social interaction mediated by alternative embodiments, changes in bodily perception resulting from prolonged use, functional and social challenges related to avatar use, and the design strategies and etiquette practices developed to overcome them.

While commercial metaverse platforms offer diverse usergenerated content, they lack effective navigation assistance that can dynamically adapt to users' interests and intentions. Although previous research has investigated on-demand agents in controlled environments, implementation in commercial settings with diverse world configurations and platform constraints remains challenging. We present Navigation Pixie, an on-demand navigation agent employing a loosely coupled architecture that integrates structured spatial metadata with LLM-based natural language processing while minimizing platform dependencies, which enables experiments on the extensive user base of commercial metaverse platforms. Our cross-platform experiments on commercial metaverse platform Cluster with 99 PC client and 94 VR-HMD participants demonstrated that Navigation Pixie significantly increased dwell time and free exploration compared to fixed-route and noagent conditions across both platforms. Subjective evaluations revealed consistent on-demand preferences in PC environments versus context-dependent social perception advantages in VRHMD. This research contributes to advancing VR interaction design through conversational spatial navigation agents, establishes cross-platform evaluation methodologies revealing environment-dependent effectiveness, and demonstrates empirical experimentation frameworks for commercial metaverse platforms.

This study aims to investigate the effects of sharing operations on the training of an avatar robot. In this study, we employed an operational platform for the avatar robot called OriHime-T, which integrates head and hand movements with wheel-based mobility. This platform allows both an expert and a learner to share the same control screen during training. Two tasks were conducted: the Time Attack and the Candy Delivered tasks in which candy is delivered to a customer. The experiments showed that compared to the conventional method in which experts observe learners’ operations and provide advice, our proposed method, which incorporates immediate intervention, led to a reduction in Time Attack completion times and an improvement in the success rate of the Candy Delivered task. These results suggest that sharing operations effectively facilitates the transmission of abstract judgment criteria and operational sensations.

The rapid development of artificial intelligence (AI) has fundamentally transformed creative work practices in the design industry. Existing studies have identified both opportunities and challenges for creative practitioners in their collaboration with generative AI and explored ways to facilitate effective human-AI co-creation. However, there is still a limited understanding of designers' collaboration with AI that supports creative processes distinct from generative AI. To address these gaps, this study focuses on understanding designers' collaboration with decision-making AI, which supports the convergence process in the creative workflow, as opposed to the divergent process supported by generative AI. Specifically, we conducted a case study at an online advertising design company to explore how professional graphic designers at the company perceive the impact of decision-making AI on their creative work practices. The case company incorporated an AI system that predicts the effectiveness of advertising design into the design workflow as a decision-making support tool. Findings from interviews with 12 designers identified how designers trust and rely on AI, its perceived benefits and challenges, and their strategies for navigating the challenges. Based on the findings, we discuss design recommendations for integrating decision-making AI into the creative design workflow.

Previous studies introduced an avatar body control sharing system known as "virtual co-embodiment," where control over bodily movements and external events, or agency, of a single avatar is shared among multiple individuals. However, how this virtual co-embodiment experience influences users' perception of agency, both explicitly and implicitly, and the extent to which they are willing to take responsibility for successful or failed outcomes, remains an imminent problem. In this research, we addressed this issue using: (1) explicit agency questionnaires, (2) implicit intentional binding (IB) effect, (3) responsibility attribution measured through financial gain/loss distribution, and (4) interview to evaluate this experience where agency over the right hand's fingers was fully transferred to a human partner. Given the distinction between two layers of agency (body agency: control over actions, and external agency: action's effect on external events), we also investigated the impact of sharing only the body-level of agency. In a ball-throwing task involving 24 participants, results showed that sharing body agency over the fingers negatively affected the feeling of having control over both the fingers and the entire right upper limb, as measured by the questionnaire. However, sharing external agency did not significantly diminish the participants' perceived control over the ball-throwing, as indicated by IB. Interestingly, while IB demonstrated that participants felt greater causality for failed ball-throwing attempts, they were reluctant to take responsibility and accept financial penalties. Additionally, responsibility attribution was found to be linked to the participants' personal trait-Locus of Control.

Virtual reality, which enables users to engage in physical activities in ways distinct from those in the real world, is increasingly recognized for its potential to enhance motor skill acquisition. Research on co-embodiment learning, in which instructors and learners utilize a single avatar that represents a weighted average of their movements, has demonstrated its efficacy in facilitating motor skill development. However, the current implementation of co-embodiment learning necessitates the real-time participation of instructors proficient in both virtual reality and co-embodiment, which poses challenges for its widespread adoption. To address this limitation, this study proposed a method for developing instructors trained on human motor data to effectively support motor skill learning through co-embodiment. The AI model was trained using supervised learning on data obtained from human motor learning sessions that employed co-embodiment. To evaluate the performance of the AI instructor, we compared the learning performance in co-embodiment learning with that of the AI instructor, recorded human instructor data, and a human instructor as well as in solo learning. The results showed that practicing with the AI instructor significantly improved learning efficiency compared with practicing alone or with recorded data and was comparable to that achieved by practicing with a human instructor.

Virtual reality is gaining attention as a tool to facilitate motor skill learning. Numerous studies have been conducted on virtual co-embodiment, in which the movements of the teacher and student are weighted and averaged into a single avatar for motor skill learning. Previous studies have shown that virtual co-embodiment with a human teacher enhances motor skill learning efficiency, and the behavior of the human teacher is important for effective learning. However, this system has some challenges, such as the human playing the teacher’s role must be skilled in teaching and using virtual co-embodiment, and the teacher can be adversely affected by the learner. To solve these problems, we created an AI teacher using long short-term memory, which outputs the behavior of the teacher based on the input of the learner’s behavior data and the state of the experimental environment and trained the AI teacher by supervised learning using behavior training data of a human virtual co-embodiment. We confirmed that this AI teacher can generate behaviors similar to those of the human teacher and investigated the efficiency of motor skill learning using virtual co-embodiment with the AI teacher. Co-embodiment with the AI teacher reduced performance during the learning phase but improved performance during subsequent independent task execution. We further analyzed the assist proportion of the teacher and observed that when the co-embodied with an AI teacher, the assist proportion is lower than when the co-embodied with a human, suggesting that learning efficiency may be enhanced when the co-embodied partner is a mixture of supportive and obstructive.

Robotic avatars and telepresence technology enable people with disabilities to engage in physical work. Despite the recent popularity of the metaverse, few studies have explored the use of virtual avatars and environments by people with disabilities. In this study, seven disabled participants working in a cafe where remote customer service is provided via robotic avatars, were engaged in the development and use of personalized virtual avatars displayed on a large screen in-situ in combination with existing physical robots, creating a hybrid cyber-physical space. We conducted longitudinal semi-structured interviews to investigate the psychological changes experienced by the participants. The results revealed that mass-produced robotic avatars allowed participants to not disclose their disability if they did not want to, but also backgrounded their identities; by contrast, customized virtual avatars shaped without physical constraints, highlighted their personalities. The combined use of robotic and virtual avatars complemented each other and can support pilots in redefining their identity.

This study investigates the impact of both self-and other’s avatars (virtual bodies) with complementary traits on self-image and behavior change, drawing on role theory: individuals adopt and internalize their social roles as expected by others. In our experiment, participants and a non-player character played a cooperative virtual reality action game together, embodying a “warrior” avatar and a “witch” avatar with complementary appearances and in-game abilities. Results revealed that role assignments based on the use of complementary avatars has significant interaction effects with participants’ personal characteristics on influencing individuals’ behavior and self-image. Furthermore, a risk of unpredicted change resulting from failed role assignments was discovered, suggesting the importance of achieving role assignments that match avatar complementarity. By providing a new perspective on the impact of interactions between multiple users with diverse avatars, these findings contribute to our understanding of the mechanisms of cognitive augmentation with avatars and have implications for the design of avatar-related experiences in the metaverse.

This study investigated the learning effect and skill retention when virtual co-embodiment, in which movements of people were weighted and averaged into a single avatar, was used to learn motor skills requiring declarative memory. Previous studies have shown that virtual co-embodiment promotes the efficiency of motor skill learning, which relies on procedural memory such as movement procedures. However, declarative memory, such as the connection between specific instructions and actions, plays an important role in learning motor skills. This study compared the learning efficiency and skill retention after one week of repeatedly performing the task of touching a specified combination of virtual spheres with both hands in accordance with the symbols presented, using virtual co-embodiment and in a condition in which the task was performed alone. The results showed that virtual co-embodiment improves learning efficiency for motor skills related to declarative memory as well as procedural memory and promotes long-term retention of skills.

Most studies on the Proteus Effect, which examines how avatars can influence users’ behavior through evoked stereotypes, have primarily manipulated only participants’ own avatars as the independent variable. However, in reality, there are numerous scenarios where individuals recognize their uniqueness by comparing themselves to others. Therefore, this study aimed to explore the impact of recognizing one’s distinctiveness by comparing one’s own avatar’s appearance with others on behavioral changes. In our experiment, participants and non-player characters engaged in playing the Japanese drum ‘Taiko’ together within a virtual environment. They utilized avatars dressed in suits or ‘Happi,’ which is a traditional Japanese festival costume. The results demonstrated that both the uniformity/distinctiveness and the type of avatar appearance played a joint role in influencing the speed and amplitude of arm swings during the taiko performance. This finding provides valuable insights into comprehending the mechanisms of behavior change in settings where multiple avatars interact, such as social virtual reality, and aids in designing virtual spaces that foster appropriate interactions among individuals.

Virtual reality (VR) is a promising tool for motor skill learning. Previous studies have indicated that observing and following a teacher's movements from a first-person perspective using VR facilitates motor skill learning. Conversely, it has also been pointed out that this learning method makes the learner so strongly aware of the need to follow that it weakens their sense of agency (SoA) for motor skills and prevents them from updating the body schema, thereby preventing long-term retention of motor skills. To address this problem, we propose applying "virtual co-embodiment" to motor skill learning. Virtual co-embodiment is a system in which a virtual avatar is controlled based on the weighted average of the movements of multiple entities. Because users in virtual co-embodiment overestimate their SoA, we hypothesized that learning using virtual co-embodiment with a teacher would improve motor skill retention. In this study, we focused on learning a dual task to evaluate the automation of movement, which is considered an essential element of motor skills. As a result, learning in virtual co-embodiment with the teacher improves motor skill learning efficiency compared with sharing the teacher's first-person perspective or learning alone.

Can you speak the way you desire without feeling the pressure to conform to standards of speaking? In this study, we investigated the impact of user-controlled voice manipulation and listening to recordings of model speakers on self-perceptions of voice and speech. Quantitative analysis showed that there was a significant improvement in the perceived confidence of tone by listening to model speakers, but there were no significant improvements due to voice manipulation. Qualitative analysis of interviews revealed that participants responded positively to the visual and auditory feedback provided by the voice manipulation software. The participants also evaluated the quality of model speakers to decide whether or not they wanted to refer to them for speech practice. Based on the results of these analyses, we summarized the design implications for a speech practice system that would allow further investigation of the impact of the system on self-perceptions of speech performance.

Virtual reality helps us learn complex motor skills by providing a situation in which we observe or follow a teacher’s movements from a first-person perspective. However, it has been suggested that if the learners themselves do not behave actively, their body schemes will not be updated and motor skills will be acquired temporarily, but will not be retained in the long term. As a solution to this problem, “co-embodiment” in which two people embody an avatar that reflects the weighted average of their movements was proposed, and it is shown that the user can feel an excessive sense of agency (SoA) even when their control weight is small. From the perspective of motor skill learning, the learner must feel as strong a SoA as possible while performing the exercise as close to the teacher as possible. Therefore, in this study, we propose a method to transitionally change the weights in a situation where co-embodiment is used, such that a strong SoA is felt despite the high weights of control by others. Considering the two-step account of the agency model, which states that the SoA is influenced by context, we tested the hypothesis that an initially strong SoA can maintain the SoA despite a gradually decreased control weight. The experimental results support this hypothesis, and it is expected that the proposed method will enhance the effectiveness of motor skill learning using co-embodiment.