Disengagement plays an important role in the overall game experience. However, extensive game research has focused on creating engaging experiences, whereas how players disengage remains insufficiently understood. Emerging studies have outlined characteristics of disengagement in screen-based video games. Little is known about how virtual reality (VR) shapes players’ disengagement process and what strategies might support positive disengagement experiences in VR games. Therefore, we conducted a co-design workshop (n = 18) and an online survey (n = 115) with VR game players. Our findings show that disengagement in VR games is often driven by factors such as physical discomfort and emotional overload. Participants adopt different disengagement strategies depending on the situation, such as restoring physical-world awareness to assist disengagement decisions. Then, we summarize three strategies for fostering positive disengagement experiences. Finally, we discuss these strategies, such as MR-based narrative space, extending the understanding of virtual-to-real transitions from a game experience perspective.

Over 100 million retired women in China engage in dance, but their performances are constrained by limited resources and age-related decline. While interactive dance technologies can enhance artistic expression, existing systems are largely inaccessible to non-professional older dancers. This paper explores how interactive dance technologies can be designed with an age-sensitive approach to support retired women in enhancing their stage performance. We conducted two workshops with community-based retired women dancers, employing interactive dance and LLM-powered video generation probes in co-design activities. Findings indicate that age-sensitive adaptations, such as low-barrier keyword input, motion-aligned visual effects, and participatory scaffolds, lowered technical barriers and fostered a sense of authorship. These features enabled retired women to empower their stage, transitioning from passive recipients of stage design to empowered co-creators of performance. We outline design implications for incorporating interactive dance and artificial intelligence-generated content (AIGC) into the cultural practices of retired women, offering broader strategies for age-sensitive creative technologies.

Computer numerical control machine tools (CNC) play a crucial role in modern manufacturing and education. However, traditional industrial virtual training is typically a solitary experience, lacking social interaction. While virtual reality (VR) education offers immersive learning, it often limits opportunities for real-world collaboration, potentially diminishing social and cooperative skills. Additionally, group-based training requires numerous devices, resulting in high costs and low efficiency. To address these challenges, we propose a new educational framework: Cognition-Operation-Assessment. This framework introduces a group-oriented, highly interactive, and evaluable mixed reality (MR) system for CNC machine tool education. By integrating collaborative and interactive functionalities, the system enhances the teaching effectiveness of virtual classrooms, surpassing traditional paper-based and single-player MR learning. Its effectiveness has been validated through comprehensive courses and user experiments.

Psychological factors such as low self-efficacy are found to challenge older adults' smartphone usage. However, there is a lack of studies trying to frame how older adults' mental barriers relate to interaction problems, response behaviors, and challenges of dealing with them in the context of smartphone interaction tasks. We conducted a two-phase qualitative user study to explore such questions. We summarize 4 common interaction problems, 4 types of help-seeking challenges, and 4 typical response behaviors to them. We also present 5 common mental barriers of older adults that could stem from and result in their interaction problems and help-seeking challenges, and affect their response behaviors. Our work contributes a conceptual model of how older adults' interaction problems, response behaviors, and help-seeking challenges relate to their mental barriers. We highlight the importance of supporting older adults to overcome their mental barriers in helping them tackle smartphone interaction problems.

Typing is essential for communication, yet the input behavior of individuals with cerebral palsy (CP) remains underexplored. We investigated 31 CP typists and 31 non-disabled controls using keystroke logging, eye tracking, and motion capture. Our study found that CP typists were slower and less rhythmically stable, but by prioritizing accuracy, their overall keyboard efficiency was comparable to controls. They adopted compensatory visual strategies such as shorter and more frequent fixations, greater reliance on the keyboard, and more gaze shifts, and displayed diverse finger usage strategies from single-finger to multi-finger input. We found that using more fingers did not necessarily result in faster typing. Subtype analysis showed spastic CP typists followed a "slow but steady" rhythm with consistent inter-key intervals, whereas athetoid CP typists exhibited a "fast but unstable" rhythm with greater variability, highlighting distinct mechanisms of typing in CP and providing insights for personalized assistive technologies.

Technologies designed to support reminiscence, defined as the practice of engaging with one’s personal past, have become a significant area of inquiry within HCI. Although this has generated a diverse range of creative systems, the field still lacks a systematic account of the design principles that guide them. In this paper, we review 60 studies to examine both the psychosocial functions these technologies target and the mechanisms through which they operate. Our analysis suggests a predominant emphasis on positive identity construction and social connection, with comparatively less focus on functions related to everyday problem solving. To synthesize the mechanisms identified, we propose a cue-centered framework that treats mnemonic cues (e.g., photographs) as the basic unit of design. The framework organizes design mechanisms into a four-stage lifecycle: cue generation, augmentation, interaction, and sharing. It provides a conceptual vocabulary for analyzing reminiscence technologies and highlights underexplored opportunities for future research and design.

Reconstructing realistic digital twins has become crucial as advances in mixed reality, metaverse, and robotics demand more accurate simulations for the physical world. Despite technical progress, building high-fidelity digital twins from a systematic and human-centered perspective remains underexplored. Drawing from the human processing model, we decompose human-centric reality into perception, motion, and cognition, and define a reality-preserving digital twin (RPDT) as a reconstruction integrating these dimensions. We present RealTwin, an attribute-graph-based representation and inference framework for RPDT. Leveraging the grounding capabilities of Multimodal Large Language Models (MLLMs), RealTwin chains AI tools to construct attribute graphs that faithfully encode real-world properties. We validate RealTwin through both technical evaluation, showing promising success in graph parsing and attribute inference, and a user study, assessing its applicability across diverse user groups. Enlightened by RealTwin, we discuss critical issues, including ecology, interaction space, and real-world adoption, for future end-to-end, fine-grained, and scalable digital twin reconstruction.

Mentorship effectiveness is highly context-dependent, necessitating empirical investigation to inform the design of intelligent support systems. The design industry's subjectivity and rapid tool iteration intensify reliance on experienced mentors, a demand amplified by the high-pace environment and feature-rich products of Chinese design teams. We conducted in-depth interviews with 23 Chinese designers to investigate this underexplored context. Our findings reveal a fundamental bifurcation of Kram's functions: career functions are instrumentalized for project efficiency, while psychosocial support suffers a systemic deficit driven by a lack of psychological safety. This impairment is rooted in structural challenges like individual traits and educational gaps. Participants proposed an intelligent mentoring system, leveraging technologies both to reduce mentor workload and to mitigate relational risks. Our research contributes an unique account of functional impairment under high-pace contexts and provides actionable design implications for supporting mentoring in the Chinese design industry.

The growing use of artificial intelligence (AI) across industries has spurred interest in its application to usability analysis. Yet, standardized criteria for evaluating AI-generated usability test results are lacking, and the impact of strategies such as role prompting and human review on the quality of results remains underexplored. To address these gaps, we first developed seven evaluation criteria grounded in user experience (UX) literature and a survey of 40 UX professionals. Second, we recruited UX experts to apply these criteria to usability findings generated under five conditions: human-only, baseline AI-only, tailored AI-only, baseline AI with human review, and tailored AI with human review. Expert evaluations show that human-AI collaboration, especially tailored AI combined with human review, produced significantly higher-quality results than either humans or AI alone. These findings provide practical methodologies and empirical evidence to support human-AI collaboration in usability analysis, informing system design for complex human-computer interaction tasks.

Stroke rehabilitation is prolonged and emotionally demanding, yet family members cannot provide continuous companionship or coaching. We explore a family-resembling avatar that offers psychological support and training guidance when relatives cannot be present. We conducted an empirical user study with six stroke patients and their family members (N=12). Our qualitative findings suggest such companions may reduce loneliness and increase perceived warmth, while action-relevant guidance can complement caregivers’ limited rehabilitation knowledge. Participants also raised concerns about emotional dependence, privacy, and the need for stronger clinical alignment to avoid inappropriate guidance. We discuss implications for designing family-resembling companion-coach avatars with clear boundaries, privacy-preserving personalization, and supervised deployment in rehabilitation workflows.

Poor posture during desk-based learning activities can lead to many health issues, such as spinal problems, musculoskeletal discomfort, and myopia. While traditional posture correction systems use immediate feedback to notify users of their wrong posture, they often disrupt users’ concentration. This study explores an ambient projection notification system for non-intrusive posture correction notifications during desk-based learning scenarios. Through a notification elicitation study and an expert co-design workshop, we investigated users’ perception towards basic elements of projection notification and derived a design space for desk-based projection notification. We then implemented and evaluated PosProjector, an ambient projection notification system, by applying two notification strategies that embody the most representative dimensions of the design space. Results showed that PosProjector can improve users’ posture with little task interference and support various media, including paper and tablets. We further discussed the implications of how to design the least intrusive projection notification system for posture correction.

Children often encounter safety hazards, such as sharp table corners or exposed electrical outlets, which are out of their radar. Childcare providers may lack awareness of these hazards and the technical expertise required to design effective protective solutions. To address this, we propose SafetyBuilder, an AR-based framework that enables childcare providers to detect safety hazards in the environment and create customized protective devices for 3D printing. The framework comprises three core components: real-time environmental hazard detection, AI-assisted suggestions for protective measures, and in-situ customization of 3D printable protective devices. We then evaluated SafetyBuilder via design workshops involving 10 participants and user testing of a proof-of-concept prototype system with nine participants. The results showed that the framework effectively supports users in identifying potential hazards, creating customized protective devices, and improving their confidence in managing child safety risks.

Data videos are an increasingly popular storytelling medium, effectively communicating data-driven insights to diverse audiences. However, designing compelling data videos requires not only domain knowledge of the data but also expertise in cinematic design, posing substantial challenges for non-experts in design. To study how AI can support non-experts in addressing such challenges during early-stage design, we introduce ideate-through-cocreate, a design concept in which users' ideation is scaffolded through co-creation with generative AI. We instantiate this concept by proposing Data Video Designer (DVD), a proof-of-concept system that supports the ideation of data video opening scenes. DVD facilitates non-experts' ideation processes by generating exemplary video scripts and visuals from high-level topic and data summaries, referencing relevant design guidelines, and integrating accurate visualizations derived from their own data into the generated scenes. Through this workflow, DVD turns abstract cinematic guidelines and AI-generated materials into actionable ideation support. A workshop and a comparative study demonstrated the usefulness of the structured co-creation support provided by DVD within the scope of opening-scene design and revealed the creative potential in balancing the data authenticity and stylized expression. These findings provide initial evidence for the potential of structured human-AI co-creation to support data video ideation.

Although remote assistance is a common resource older adults turn to when learning new digital technology, remote helpers face difficulty in assisting older adults in learning new physical devices as they are unable to perform trial-and-error operations or demonstrations remotely. Through a formative study with 8 older adults and 8 younger helpers, we identified key challenges in remote assistance, including poor camera placement, difficulty in communicating motion, and the limitations of remote trial-and-error methods. We designed a telepresence robotic system that augments remote assistance with gesture-based cues to address key limitations of video-call support, and to potentially facilitate in-the-moment learning during device use.

Target selection is a fundamental interaction in virtual reality (VR). But the act of confirming a selection, such as a button press or pinch, can disturb the tracked pose and shift the intended target, which is referred to as the Heisenberg Effect. Prior research has mainly investigated controller input. However, it remains unclear how the effect manifests in the bare-hand input and how score-based techniques may mitigate the effect in different spatial variations. To fill the gap, we conduct a within-subject study to examine the Heisenberg Effect across two input modalities (i.e., controller and hand) and two selection mechanisms (i.e., direct and score-based). Our results show that hand input is more susceptible to the Heisenberg Effect, with direct selection more influenced by target width and score-based selection more sensitive to target density. Based on previous vote-oriented technique and our temporal analysis, we introduce weighted VOTE, a history-based intention accuracy model for target voting, that reweights recent interaction intent to counteract input disturbances. Our evaluation shows the method improves selection accuracy compared to baseline techniques. Finally, we discuss future directions for adaptive selection methods.