AI-assisted usability analysis can potentially reduce the time and effort of finding usability problems, yet little is known about how AI's perceived expertise influences evaluators' analytic strategies and perceptions over time. We ran a within-subjects, five-session study (six hours per participant) with 12 professional UX evaluators who worked with two conversational assistants designed to appear novice- or expert-like (differing in suggestion quantity and response accuracy). We logged behavioral measures (number of passes, suggestion acceptance rate), collected subjective ratings (trust, perceived efficiency), and conducted semi-structured interviews. Participants experienced an initial novelty effect and a subsequent dip in trust that recovered over time. Their efficiency improved as they shifted from a two-pass to a one-pass video inspection approach. Evaluators ultimately rated the experienced CA as significantly more efficient, trustworthy, and comprehensive, despite not perceiving expertise differences early on. We conclude with design implications for adapting AI expertise to enable calibrated human-AI collaboration.

This work presents Dual-Stick, a novel controller with two sticks connected at the end that innovates a Dual-Ray interaction paradigm to enrich raycasting input in Virtual Reality (VR). Dual-Stick leverages the inherent human dexterity in using everyday tools such as clamps and tweezers to adjust the relative angle between two sticks. This design supports Dual-Ray interactions that provide with a heuristics-based enhanced mechanism. It also offers more flexible manipulation by taking advantages of additional degrees of freedom provided by clamping angle. We conducted two studies to evaluate the effectiveness of Dual-Ray in target selection and manipulation tasks. The results indicated that Dual-Ray significantly improved efficiency in target selection compared to single-ray input but did not outperform the enhanced single-ray technique. In terms of manipulation, Dual-Ray effectively reduced completion time and mode switching compared to single-ray input.

Museums have traditionally been places of learning, evolving into spaces that also facilitate socializing. This shift is particularly evident in virtual reality (VR) museums, which have become popular venues for activities like friend gatherings. However, education has long established the social norm that museums need to “maintaining silence.” Even in virtual environments, this can influence visitor behavior. This perception often prevents people from using verbal communication, leading them to prefer quieter forms of interaction. In museums, including the VR museums that now largely replicate the layout of physical museums, this preference may be reinforced by specific features, such as spaciousness, quietness, and block-based layouts, which may create visual obstructions, restricting interaction modes dependent on shared view, such as gesture interaction. These limitations necessitate the introduction of an additional interaction mode. Emojis, with their capacity for rapid message exchange and adjustable positioning, emerge as a suitable interaction mode in this context. Thus, we introduce EmojiChat, an innovative VR museum experience designed to respect the social norms of traditionally keeping quiet while promoting natural interaction. We first design and iterate a customized emoji set for the museum context through semi-structured interviews, participatory design, and an online survey. Then, this emoji set is integrated into a VR museum to facilitate interaction between visitors. Finally, we conduct a comparative study to evaluate the performance of EmojiChat. Our results show that the integration of emojis can improve communication enjoyment and efficiency. Additionally, we identify usage patterns for interaction modes and the advantages offered by emojis. We also identify several challenges that point toward future directions for enhancing emoji integration and facilitating social interaction.

Virtual Reality (VR) offers a unique collaborative experience, with parallel views playing a pivotal role in Collaborative Virtual Environments by supporting the transfer and delivery of items. Sharing and manipulating partners' views provides users with a broader perspective that helps them identify the targets and partner actions. We proposed TeamPortal accordingly and conducted two user studies with 72 participants (36 pairs) to investigate the potential benefits of interactive, shared perspectives in VR collaboration. Our first study compared ShaView and TeamPortal against a baseline in a collaborative task that encompassed a series of searching and manipulation tasks. The results show that TeamPortal significantly reduced movement and increased collaborative efficiency and social presence in complex tasks. Following the results, the second study evaluated three variants: TeamPortal+, SnapTeamPortal+, and DropTeamPortal+. The results show that both SnapTeamPortal+ and DropTeamPortal+ improved task efficiency and willingness to further adopt these technologies, though SnapTeamPortal+ reduced co-presence. Based on the findings, we proposed three design implications to inform the development of future VR collaboration systems.

Artificial Intelligence-Generated Content (AIGC) tools have gradually been integrated into the daily workflow of UX practitioners. While existing research has explored the integration of AIGC tools in daily workflow, little is known about their impact on social dynamics within UX collaboration. We conducted four focus groups and eight semi-structured interviews with 26 UX practitioners to investigate how AIGC tools influence social dynamics in UX collaboration. Our findings indicated that AIGC tools not only mitigated conflicts but also introduced potential new conflicts. AIGC tools expanded the roles of UX practitioners and fostered a team culture characterized by exploring and discussing. Participants have higher expectations for AI-assisted design in user understanding and prototype evaluation, and team-motivated AI tools learning. Based on these findings, we discussed the benefits and concerns of conflict resolution through AIGC and the importance of teams in AI learning. Finally, we proposed several suggestions for future AI design research.

Haptic simulation of hand tools like wrenches, pliers, scissors and syringes are beneficial for finely detailed skill training in VR, but designing for numerous hand tools usually requires an expert-level knowledge of specific mechanism and protocol. This paper presents HapLinkage, a prototyping framework based on linkage mechanism, that provides typical motion templates and haptic renderers to facilitate proxy design of virtual hand tools. The mechanical structures can be easily modified, for example, to scale the size, or to change the range of motion by selectively changing linkage lengths. Resistant, stop, release, and restoration force feedback are generated by an actuating module as part of the structure. Additional vibration feedback can be generated with a linear actuator. HapLinkage enables easy and quick prototypting of hand tools for diverse VR scenarios, that embody both of their kinetic and haptic properties. Based on interviews with expert designers, it was confirmed that HapLinkage is expressive in designing haptic proxy of hand tools to enhance VR experiences. It also identified potentials and future development of the framework.