We present CoStage, an AI-supported embodied co-creation platform that combines LLM-based story generation with multi-robot stage performance to support children’s narrative construction and spatial imagination. Unlike prior AI storytelling tools that are largely screen-based, CoStage allows children to direct robot actors on a 360° tangible stage, transforming written stories into spatially enacted performances. Informed by formative work with domain experts, we evaluated CoStage in a within-subjects study (N = 24) comparing a robot enactment condition with a screen-based animation condition. Our findings indicate that robot-stage performance can heighten immersion, strengthen children’s sense of directorial agency, support spatial sensemaking, and foster a planning–enactment coordination loop during storytelling. This work advances child–computer interaction by offering design implications and demonstrating how embodied AI co-creation can support children’s spatial cognition and narrative engagement.

While Large Language Models (LLMs) have significantly advanced design ideation and problem-solving, a fundamental tension persists between the discrete nature of current LLM interactions and the iterative, non-linear essence of design thinking. This “human-LLM misalignment” often forces designers to choose between accepting opaque “black-box” outputs or restarting the generation process entirely, thereby hindering the evolution of design intent and diminishing critical reflection. Based on a formative study with eight designers, we identify three core challenges in achieving reasoning-level alignment and propose a “Curated Reasoning” interaction approach. To instantiate this approach, we developed DesignerlyLoop, a prototype that introduces a nested two-layer diagram structure. This system allows users to externalize their evolving design intent while simultaneously inspecting, reorganizing, and selectively regenerating the underlying LLM reasoning chains. A within-subject user study with 20 designers demonstrated that curated reasoning significantly enhances design intent formulation, and output quality. By shifting the user’s role from a passive recipient of generated content to an active curator of AI reasoning, DesignerlyLoop fosters a more reflective and iterative human-AI collaborative process. Our contributions include: identifying key alignment challenges in creative design; the implementation of a dual-layer structure for reasoning curation; and empirical evidence validating how explicit curated reasoning supports more effective human-LLM alignment in creative tasks.

Dunhuang murals, a significant world cultural heritage, present substantial comprehension barriers for general audiences due to their intricate compositions and culturally distant narratives. Existing digital systems limit users’ ability to establish coherent cognitive connections between original visual compositions and narrative progression, while reliance on manual content curation restricts both scalability and generalizability. We present Luminara, an AI-powered system that automatically analyzes Dunhuang murals and generates interactive narratives. Luminara integrates vision-language models (VLMs) and large language models (LLMs) to establish visual-textual correspondences, and employs a multi-agent framework (Storytelling, Knowledge, and Reflective agents) to generate interactive narratives. This design addresses key barriers identified through our formative study (N=12): visual-textual correspondence challenges, narrative structure comprehension difficulties, and cultural knowledge gaps. A user study (N=17) demonstrated the system’s effectiveness in helping users comprehend complex compositions and storylines, resulting in clear and immersive viewing experiences. This research contributes an automated, generalizable approach and practical design insights for interactive narrative systems in digital cultural heritage.

Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human–pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.

Role-play is widely used to empower autistic children to explore social interaction and dynamics on their own terms, navigating neurotypical social conventions to shape social expressions in ways that align with their own traits and needs, fostering a stronger sense of agency. However, existing approaches typically rely on fixed content, requiring educators to design materials, which creates a significant burden on manual preparation. According to insights from a formative study, we developed GenRole, a generative AI system that enables educators to design personalized role play class activities. GenRole supports a progression from simple to complex interactions and allows for personalization of characters, settings, and dialogues that meet the needs of autistic learners. We conducted a pilot study with 16 educators, followed by a two-week evaluation study with 11 autistic children and their teachers. Results show that GenRole enhances the efficiency and flexibility of role play design while improving instructional support, offering design insights for creating personalized components that help educators deliver more engaging and individualized social interaction learning for autistic children.

This paper investigates how large language models (LLMs) are reshaping competitive programming. The field functions as an intellectual contest within computer science education and is marked by rapid iteration, real-time feedback, transparent solutions, and strict integrity norms. Prior work has evaluated LLMs performance on contest problems, but little is known about how human stakeholders—contestants, problem setters, coaches, and platform stewards—are adapting their workflows and contest norms under LLMs-induced shifts. At the same time, rising AI-assisted misuse and inconsistent governance expose urgent gaps in sustaining fairness and credibility. Drawing on 37 interviews spanning all four roles and a global survey of 207 contestants, as well as an API-based crawl of Codeforces contest logs (2022–2025) for quantitative analysis, we contribute: (i) an empirical account of evolving workflows, (ii) an analysis of contested fairness norms, and (iii) a chess-inspired governance approach with actionable measures—real-time LLMs checks in online contests, peer co-monitoring and reporting, and cross-validation against offline performance—to curb LLMs-assisted misuse while preserving fairness, transparency, and credibility.

Advances in large language models (LLMs) are profoundly reshaping the field of human–robot interaction (HRI). While prior work has highlighted the technical potential of LLMs, few studies have systematically examined their human-centered impact (e.g., human-oriented understanding, user modeling, and levels of autonomy), making it difficult to consolidate emerging challenges in LLM-driven HRI systems. Therefore, we conducted a systematic literature search following the PRISMA guideline, identifying 86 articles that met our inclusion criteria. Our findings reveal that: (1) LLMs are transforming the fundamentals of HRI by reshaping how robots sense context, generate socially grounded interactions, and maintain continuous alignment with human needs in embodied settings; and (2) current research is largely exploratory, with different studies focusing on different facets of LLM-driven HRI, resulting in wide-ranging choices of experimental setups, study methods, and evaluation metrics. Finally, we identify key design considerations and challenges, offering a coherent overview and guidelines for future research at the intersection of LLMs and HRI.

Large Language Models are reshaping task automation, yet remain limited in complex, multi-step real-world tasks that require aligning with vague user intent and enabling dynamic user override. From a formative study with 12 participants, we found that end-users actively seek to shape task-oriented interfaces rather than relying on one-shot outputs. To address this, we introduce the human-agent co-generation paradigm, materialized in DuetUI. This LLM-empowered system unfolds alongside task progress through a bidirectional context loop—the agent scaffolds the interface by decomposing the task, while the user’s direct manipulations implicitly steer the agent’s next generation step. In a technical ablation study and a user study with 24 participants, DuetUI improved task efficiency and interface usability, supporting more seamless human-agent collaboration. Our contributions include the proposal of this novel paradigm, the design of a proof-of-concept DuetUI prototype embodying it, and empirical and technical insights from an initial evaluation of how this bidirectional loop may help align agents with human intent and inform future development.

Human–robot creative collaboration is often constrained by command–response paradigms that position robots as tools rather than partners. While expressive robotics has shown social values, its role and behaviors in shaping creative partnerships with humans remains underexplored. Therefore, we investigate how robots’ expressive behaviors influence co-creative engagement. In a formative study with 5 participants, we identified design insights for users to perceive a robot arm’s expressive behaviors. We then implemented these expressive behaviors and conducted a within-subject study with 18 participants, comparing functional-only and expressive conditions in figure drawing tasks. Results showed that expressive behaviors significantly enhanced human-robot collaboration where they shifted from viewing the robot as a tool to a partner with a stronger emotional connection and collaborative satisfaction. Our contributions include empirical evidence of partnership transformation and design insights for facilitating human-robot creative collaboration.

Autistic children exhibit heterogeneous oral language impairments, necessitating educators to implement personalized teaching content. However, preparing personalized materials remains time-intensive and difficult to maintain coherence, while generative AI’s recent advances in creating customized content show potential to support this process. We first conducted video analysis from educators’ one-on-one classes with autistic students and conducted interviews with therapists to understand their challenges in current teaching practices. Then, we developed a generative AI-empowered prototype, LingoLift, which supports educators to create interest-based, ability-adapted, and coherent teaching materials according to children’s profiles. Finally, we conducted a three-week deployment study with 10 educator-student dyads completing 30 lessons with LingoLift in a specialized education school. Results showed that LingoLift significantly improved lesson preparation efficiency, reduced educators’ workload, and enabled children to achieve positive learning outcomes. We observed educators’ adaptive extensions and innovations, revealing insights into design considerations and future opportunities for AI-assisted inclusive education.

This experience report presents the development and evaluation of a virtual reality (VR)-based, gamified platform designed to teach accessibility concepts in an engaging and immersive manner. The system integrates interactive scenarios and game elements to enhance student motivation and support knowledge retention. To assess its effectiveness, we conducted a study with 35 students who used the VR platform and provided feedback through a post-intervention survey. The findings indicate positive perceptions of the learning experience, with participants reporting increased awareness and understanding of accessibility principles. Drawing on the design and evaluation process, this report highlights the value of an embodied learning approach that integrates VR and gamification to enhance the impact of accessibility education. It offers practical insights into the design, implementation, and pedagogical potential of VR-based, gamified tools for enhancing accessibility in computer learning education.

We present RoboTheater, a multi-robot storytelling platform, designed to explore and demonstrate the expressive capabilities of robots in stage execution. RoboTheater bridges computational narrative generation with embodied robotic execution, enabling robots to perform with varied dialogues and movements to create immersive stage narratives. The system uses large language models (LLMs) to generate structured scripts that are mapped into robots’ movements, speech, and visual projections, with all commands sent over a wireless network. A user study with 13 participants was conducted to evaluate the system’s usability and gather feedback. Our findings indicate that the RoboTheater effectively conveys emotion and character through multimodal cues, sustaining audience engagement and immersion. The work expands multi-robot storytelling and paves the way for creative, narrative applications.

We demonstrate PufFab, an interactive design-to-fabrication pipeline that enables customizable edible shape transformation using Vietnamese rice paper. The pipeline combines a user interface with a modified 3D printer to allow users to customize and fabricate 2D patterns that transform into 3D structures when fried. Users can modify rice paper through two distinct manipulation techniques: moistening, which locally softens and flattens the material—resulting in reduced expansion and controlled shrinkage during frying—and cutting, which introduces appearance differences and structural discontinuities that cause the paper to curve or deform along edges. When fried, the rice paper morphs according to the pattern because of its unique material response to heat and hydration. Our preliminary work explores an accessible and culturally grounded approach to human-food interaction and demonstrates new possibilities for customizable computational gastronomy, playful edible interaction, and artistic food transformation.

While virtual reality (VR) enables emotional engagement in cultural heritage (CH) context, its real-time effects on emotional responses throughout the experience and their potential influence on heritage identity remain unexplored. In this study, we present a case of Azheke Village at Hani Rice Terraces in China, co-designing a CH experience in VR with multi-stakeholders, and evaluate how it evokes emotional responses during the whole experience and enhances heritage identity among young local residents and visitors. Our findings reveal that the VR experience effectively evoked emotional responses and enhanced heritage identity across all groups, with changes of emotional arousal showing the strongest relationship with social-value-based heritage identity enhancement among the local residents. This research contributes to human-computer interaction (HCI) and heritage research field by offering empirical findings, which also provides rich design reflections and implications for future research practices to develop interactive experiences to engage young generations in CH inheritance.

Integrating Large Language Models (LLMs) into Interactive Digital Narratives (IDNs) enables dynamic storytelling where user interactions shape the narrative in real time, challenging traditional authoring methods.This paper presents the design study of Orchid, a creative approach for authoring LLM-driven IDNs.Orchid allows users to structure the hierarchy of narrative stages and define the rules governing LLM narrative generation and transitions between stages.The development of Orchid consists of three phases.1) Formulating Orchid through desk research on existing IDN practices.2) Implementation of a technology probe based on Orchid.3) Evaluating how IDN authors use Orchid to design IDNs, verify Orchid's hypotheses, and explore user needs for future authoring tools.This study confirms that authors are open to LLM-driven IDNs but desire strong authorial agency in narrative structures, highlighted in accuracy in branching transitions and story details.Future design implications for Orchid include introducing deterministic variable handling, support for trans-media applications, and narrative consistency across branches.

While several digital tools exist for children's creative storytelling, few have explored how generative AI can enhance storytelling quality. Our formative research identified five design requirements for AI-powered storytelling tools for elementary students. We developed a system named StoryPrompt that enables children to co-create stories and comics with AI, boosting literacy and creativity. Pilot tests with children and HCI experts demonstrated good usability and positive learning experiences. In a mixed-methods evaluation with 40 children from Grades 2-6, we found that StoryPrompt significantly improved storytelling creativity and richness, compared to the storyboard method. Observations indicated more purposeful planning and strategic use of AI-generated words and images, facilitating efficient exploration of storytelling alternatives. While children preferred AI images, they recognized the limitations in representing storytelling details. Teacher interviews highlighted the system's motivational potential and classroom flexibility. We discuss the benefits and considerations of using generative AI to enhance creative storytelling for children.

One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.

Graffiti has long documented the socio-cultural landscapes of urban spaces, yet increasing global regulations have constrained artists’ creative freedom, prompting exploration of digital alternatives. Augmented Reality (AR) offers opportunities to extend graffiti into digital environments while retaining spatial and cultural significance, but prior research has largely centered on audience engagement rather than the embodied creative processes of graffiti artists. To address this, we developed GestoBrush, a mobile AR prototype that turns smartphones into virtual spray cans, enabling graffiti creation through embodied gestures. A co-design workshop underscored the role of embodiment—physical engagement with surroundings and body-driven creative processes—in digital workflows. We evaluated GestoBrush with six graffiti artists and findings suggested that embodied AR interactions supporting artists bypass real-world constraints and explore new artistic possibilities, whose AR artworks created enhanced senses of intuitiveness, immersion, and expressiveness. This work highlight how embodied AR tools can bridge the gap between physical graffiti practice and digital expression, suggesting pathways for designing immersive creative systems that respect the cultural ethos of street art while expanding its possibilities in virtual spaces.

Neurodivergent individuals, such as those with autism spectrum disorder (ASD), often experience sensory overload—hypersensitivity or hyperreactivity—that can lead to distress, anxiety, fatigue, and difficulties in daily functioning. This paper presents Threshold Perception (2025), a multisensory art installation that leverages generative artificial intelligence (GenAI) to compose narrative montages and orchestrate synchronized audio–visual–tactile experiences. The system reconstructs static visual artworks created by ASD artists and, through GenAI-driven scene generation and pacing control and transforms them into immersive narratives via dynamically coordinated lighting, soundscapes, and motion cues. Our design simulates characteristic perceptual patterns reported in neurodivergent communities while providing adjustable intensity and predictable timing to mitigate overload. We describe the end-to-end pipeline (prompting, scene assembly, cross-modal synchronization, and safety constraints) and distill design guidelines on contrast management, temporal chunking, and fallback modalities. By bridging art and technology, the project seeks to promote inclusive understanding of neurodiversity and to provoke reflection on shared challenges in hyper‑stimulated societies.

Ink Blossom is an interactive installation combining generative AI with the intangible cultural heritage of traditional ethnic embroidery from China. Integrating a tangible wooden embroidery frame with a dynamic digital canvas, the project invites participants to co-create with AI in real-time, transforming hand-drawn sketches into authentic embroidery designs. Utilizing custom-trained LoRA models, the system preserves the distinct visual languages of Miao and Qiang embroidery, allowing users to creatively engage with traditional forms. Participant feedback reveals that generative AI deepens their personal connections with these ancient crafts, proposing an innovative collaborative future for the preservation of intangible cultural heritage.

Curators face challenges in selecting, ideating, coordinating, and publicizing exhibited works. Recent advances in GenAI has shaped the role of curators in the exhibition design and production process, leading to new adaptations in the exploration, design, planning, and dissemination phases. To explore the impact of GenAI on curatorial practice, we interviewed 13 curators and followed an additional 3 curators in real-world online exhibition production process. We found that GenAI can support theme selection, space design, topic research, artwork selection, and element production. However, GenAI may also stifling critical thinking, perpetuate inaccuracies, leading to fragmented workflows, misperceptions of feasbility, and concerns about copyright. The effectiveness of GenAI tools often depends on the curator’s understanding of their roles and limitations. This work offer practical design recommendations for curators and suggest future research to design for adopting the use of GenAI in technology-mediated curatorial workflows.

Sense of co-presence refers to the perceived closeness and interaction between participants in a collaborative context, which critically impacts the collaboration experience and task performance. With the emergence of Mixed Reality (MR) technologies, we would like to investigate the effect of MR immersive collaboration environment on promoting co-presence in a remote setting by comparing it with non-MR methods, such as video conferencing. We conduct a comparison study, where we invited 14 dyads of participants to collaborate on block assembly tasks with video conferencing, MR system, and in a physically co-located scenario. Each participant of a dyad was assigned either a local worker to assemble the blocks or a remote helper to give the instructions. Results show that MR system can create comparable sense of co-presence with co-located situation, and allow users to interact more naturally with both the environment and each other. The adoption of mixed reality enhances collaboration and task performance by reducing reliance on verbal communication and favoring action-based interactions through gestures and direct manipulation of virtual objects.

Micro Advice is a mobile platform for democratic governance in China, allowing access to voice social issues and advice to the government with the aim of improving people's livelihood. However, due to the lack of first-hand experience, the current understanding of how end-users utilize Micro Advice to participate in democratic governance is incomplete. We interviewed 12 users to understand their practices and challenges in using the platform. Specifically, we illustrate the user's experience, introduce what difficulties they encountered, and how they strategically use the platform to improve people's livelihood. We also investigate the socio-technical aspects of Micro Advice within the Chinese political context, discussing how to accept and utilize Micro Advice in China's social environment, and develop technological solutions adapted to these backgrounds. Finally, we propose some design implications for civic technology participation platforms. Micro Advice provides a novel, open, and real-time channel for civic engagement, showcasing the practical effects and impact of digitized civic engagement in China. It offers researchers a new perspective for expressing and addressing societal issues. We believe that the innovation and insights of Micro Advice can extend to other types of digitized civic engagement initiatives. We will continue to explore the interactive processes between the government and the public, along with innovative technological approaches.

Virtual co-embodiment in virtual reality (VR) allows two users to share an avatar, enabling skill transfer from teachers to learners and influencing their Sense of Ownership (SoO) and Sense of Agency (SoA). However, mismatches between actual movements and displayed actions in VR can impair user experience, posing challenges to learning effectiveness. Although previous studies have addressed the influence of virtual bodies' visual factors on SoO and SoA, the impact of co-embodied hands' appearances remains underexplored. We conducted two user studies to examine the effects of virtual self-hands' existence and their visual factors (transparency and congruency) on SoO, SoA, and social presence. Study One showed significant improvements in SoO and SoA with the existence of virtual self-hands. In Study Two, we kept the self-hands and further focused on hand transparency and congruency. We found that identical appearances between self-hands and co-embodied hands significantly enhanced SoO. These findings stressed the importance of visual factors for virtual hands, offering valuable insights for VR co-embodiment design.

Autism Spectrum Disorder (ASD) presents challenges in social interaction skill development, particularly in turn-taking. Digital interventions offer potential solutions for improving autistic children’s social skills but often lack addressing specific collaboration techniques. Therefore, we designed a prototype of a turn-taking collaborative tablet game, StarRescue, which encourages children’s distinct collaborative roles and interdependence while progressively enhancing sharing and mutual planning skills. We further conducted a controlled study with 32 autistic children to evaluate StarRescue’s usability and potential effectiveness in improving their social skills. Findings indicated that StarRescue has great potential to foster turn-taking skills and social communication skills (e.g., prompting, negotiation, task allocation) within the game and also extend beyond the game. Additionally, we discussed implications for future work, such as including parents as game spectators and understanding autistic children’s territory awareness in collaboration. Our study contributes a promising digital intervention for autistic children’s turn-taking social skill development via a scaffolding approach and valuable design implications for future research.

Video-sharing platforms such as TikTok have offered new opportunities for d/Deaf and hard-of-hearing (DHH) people to create public-facing content using sign language – an integral part of DHH culture. Besides sign language, DHH creators deal with a variety of modalities when creating videos, such as captions and audio. However, hardly any work has comprehensively addressed DHH creators’ multimodal practices with the lay public’s reactions taken into account. In this paper, we systematically analyzed 308 DHH-authored TikTok videos using a mixed-methods approach, focusing on DHH TikTokers’ content, practices, pitfalls, and viewer engagement. Our findings highlight that while voice features such as synchronous voices are scant and challenging for DHH TikTokers, they may help promote viewer engagement. Other empirical findings, including the distributions of topics, practices, pitfalls, and their correlations with viewer engagement, further lead to actionable suggestions for DHH TikTokers and video-sharing platforms.

Designing and using technologies to support Non-Pharmacological Interventions (NPI) for People with Dementia (PwD) has drawn increasing attention in HCI, with the potential expectations of higher user engagement and positive outcomes. Yet, technologies for NPI can only be valuable if practitioners successfully incorporate them into their ongoing intervention practices beyond a limited research period. Currently, we know little about how practitioners experience and perceive these technologies in practical NPI for PwD. In this paper, we investigate this question through observations of five in-person NPI activities and interviews with 11 therapists and 5 caregivers. Our findings elaborate the practical NPI workflow process and characteristics, and practitioners’ attitudes, experiences, and perceptions to technology-mediated NPI in practice. Generally, our participants emphasized practical NPI is a complex and professional practice, needing fine-grained, personalized evaluation and planning, and the practical executing process is situated, and multi-stakeholder collaborative. Yet, existing technologies often fail to consider these specific characteristics, which leads to limitations in practical effectiveness or sustainable use. Drawing on our findings, we discuss the possible implications for designing more useful and practical NPI intervention technologies.

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided VR reconstruction of the murals in Mogao Grottoes, Pilgrimage To Pureland, to offer the general public an accessible and engaging way to experience the Dunhuang murals. We explored an immersive VR reconstruction approach that can efficiently convert complex 2D mural art into 3D digital VR content while maintaining its originality. Specifically, we reconstructed the Mt. Wutai pilgrimage mural in Cave 61 into a plot-based interactive VR environment that offers users a more accessible and immersive narrative to visit and appreciate Dunhuang murals’ complex religious, historical, and artistic value. We conducted a user study with twenty participants to further investigate how this reconstruction approach and the interactive narratives would affect users’ immersive experiences and knowledge acquisition, compared with other forms of virtual access as the control group. Findings from our user study revealed that participants from the VR experimental group reported a significantly higher level of immersion and understanding of the knowledge. To conclude, our 2D-to-VR reconstruction methods provided a VR reconstruction approach and insights for creating engaging user experiences and improving users understanding of cultural and historical concepts while preserving the originality of the cultural heritage.

Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.

The IDC research community has a growing interest in designing AI interfaces for children with special educational needs. Nonetheless, little research has explored the research and design issues, rationale, challenges, and opportunities in this field. Therefore, we propose to host a half-day workshop to bring together researchers and practitioners from the Learning & Education, Accessibility, and Intelligent User Interfaces sub-fields to discuss and identify existing design issues, challenges, and collaboration barriers, to establish consensus on the design of a pragmatic framework, as well as explore future innovation and research opportunities. We aim to foster mutual understanding and in-depth collaboration among researchers in the IDC community.

Food is essential for human health, growth, and development. However, children need more learning materials and motivation to receive food literacy education or know the fundamental food processes from farm to table. In this work, we explored the design of a prototype named WooGu with tangible user interfaces (TUI) and embodied interactions, which aims to improve young children’s food literacy. WooGu presents three design features: a cube displaying user interfaces, step-by-step tasks guiding children to learn food from farm to table, and hands-on props made by cardboard empowering embodied interactions. We evaluated WooGu with two families in a pilot test, and the findings suggested that WooGu provides children with the embodied experience of food production, improving their food literacy, logical thinking, and practical ability. This research contributed to the human-food interaction area and provided a novel way of learning food literacy for children through embodied interactions with WooGu.

The proliferation of online dating platforms has provided new opportunities for deaf and hard of hearing (DHH) individuals to seek, build, and maintain romantic relationships. However, challenges such as deception, harassment, and aggression widely exist, closely tied to their practices of self-presentation and disability disclosure. Yet, these practices of DHH individuals in online dating communities remain largely unknown. Our study presents a preliminary qualitative analysis of 1,200 posts from a leading DHH online dating community. Our findings reveal that DHH individuals hesitate to reveal their disability while actively disclosing their relationship challenges, especially with respect to hearing-DHH relationships. We have also identified significant research gaps in understanding online DHH dating activities, calling for future research to make online dating environments more inclusive for DHH individuals.

Virtual exhibitions have long been regarded as an extension of information delivery for physical exhibitions. However, what virtual exhibitions can offer audiences as a novel experience independently from physical exhibitions has been largely unexplored. In this study, we aim to understand the promises and challenges of experiencing and curating exhibitions in VR by interviewing nine expert curators. Drawing from expert insights, we summarized a set of design guidelines to inform what we can learn and adapt from physical exhibitions when curating in VR. Then, using an autobiographical design approach, we curated an interactive exhibition in VRChat to explore novel interaction techniques. We also hosted an open tour guide in the user study to validate our design guidelines with thirty participants. Results show that our approach of curating an exhibition in VRChat provided the participants with engaging and novel experiences interacting with the exhibits and other audiences.

Increasingly popular social virtual reality (VR) platforms like VRChat created new ways for people to interact with each other, generating dedicated user communities with unique idioms of socializing in an alternative world. In VRChat, users frequently gather in front of mirrors en masse during online interactions. Understanding how user communities deal with the mirror’s unique interactions can generate insights for supporting communication in social VR. In this study, we investigated the mirror’s synergistic effect with avatars on behaviors and dedicated user conversational performance. Qualitative findings indicate that avatar-mediated communication through mirrors provides functions like ensuring synchronization of incarnations, increasing immersion, and enhancing idealized embodiment to express bolder behaviors anonymously. Quantitative studies show that while mirrors improve self-perception, it has a potentially adverse effect on conversational performance, similar to the role of self-viewing in video conferencing. Studying how users interact with mirrors in an immersive environment allows us to explore how digital environments affect spatialized interactions when transported from physical to digital domains.

Information access is one of the most significant challenges faced by d/Deaf signers due to a lack of sign language information. Given the challenges in machine-driven solutions, we seek to understand how d/Deaf communities can support the growth of sign language content. Based on interviews with 12 d/Deaf people in China, we found that d/Deaf videos, i.e., sign language videos created by and for d/Deaf people, can be crucial information sources and educational materials. Combining content analysis of 360 d/Deaf videos to better understand this type of video, we show how d/Deaf communities co-create information accessibility through collaboration in content creation online. We uncover two major challenges that creators need to address, e.g., difficulties in interpretation and inconsistent content qualities. We propose potential design opportunities and future research directions to support d/Deaf people’s needs for sign language content through collaboration within d/Deaf communities.

Modern stress management techniques have been shown to be effective, particularly when applied systematically and with the supervision of an instructor. However, online workers usually lack sufficient support from therapists and learning resources to self-manage their stress. To better assist these users, we implemented a browser-based application, Home Sweet Office (HSO), to administer a set of stress micro-interventions which mimic existing therapeutic techniques, including somatic, positive psychology, meta cognitive, and cognitive behavioral categories. In a four-week field study, we compared random and machine-recommended interventions to interventions that were self-proposed by participants in order to investigate effective content and recommendation methods. Our primary findings suggest that both machine-recommended and self-proposed interventions had significantly higher momentary efficacy than random selection, whereas machine-recommended interventions offer more activity diversity compared to self-proposed interventions. We conclude with reflections on these results, discuss features and mechanisms which might improve efficacy, and suggest areas for future work.

People with color vision deficiency (CVD) often encounter color-related challenges in their daily life, which are difficult for those with non-CVD to comprehend fully. Therefore, we designed a Virtual Reality (VR) serious game, BlueVR , to simulate challenging scenarios encountered by people with CVD and facilitate understanding from people with non-CVD. We conducted an empirical study with thirty participants with non-CVD and six participants with CVD to evaluate the opportunities and challenges of BlueVR . Our findings suggest that BlueVR increased people with non-CVD’s understanding, awareness, and perspective-taking abilities towards people with CVD. Moreover, interviews with participants with CVD revealed that BlueVR accurately depicts their real-life discomforts and meets their expectations to improve potential social awareness. This research contributes valuable insights into the mechanisms underlying the effectiveness of VR serious games in promoting understanding and design implications for future game development.

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.

Virtual reality (VR) has shown great potential in enhancing users’ empathy towards vulnerable populations. Previous work has demonstrated that navigation modes relaying different spatial information can affect a user’s presence and understanding. In this research, we designed a 360° VR video-based prototype environment to depict real-life scenarios of a refugee camp in Southern Bangladesh. Our study consists of 2 conditions: active (i.e., selecting thematic video clips freely) and passive (i.e., watching sequence-determined clips passively). Thirty-six participants evaluated the prototype’s usability and its effects on their empathy towards refugees. The results showed that active navigation resulted in higher kindness and usability scores. Finally, we provide empirical insights into navigation modes in future VR design. This research should help refugee groups receive more attention and promote recognition and empathy towards refugees and their families.

Older adults' technology learning is a long-term process, during which family members often play significant roles. Although much research has emphasized how family support is important, little research has dove into the evolution of family dynamics when older adults are learning to use new technology. Drawing on the results from a qualitative study that performed semi-structured interviews with 20 older adults and 18 younger adults in China, we unpack how family members were involved in technology learning over time. Our findings suggest that younger family members play transformative roles throughout older adults' learning stages, i.e., as influencers, supporters, protectors, and monitors. Younger family members' roles co-evolve with not only older adults' changing needs but also their perceptions of older adults' learning abilities and online behaviors. They may struggle to adjust their teaching strategies to accommodate older adults' needs and abilities during the process. They may also worry about older adults' online benefits and safety as many older adults become far more active online than anticipated. Challenges while teaching and tensions regarding protection may thus emerge during the support process. With these findings, we suggest that older adults' technology learning should be treated as a collaborative activity with family members rather than an activity they pursue alone. We also highlight older adults' technology learning as a recurrent, dynamic, and evolving process, and call attention to the unique culture of "xiaoshun" in China that acts as a buffer to the burdens and tensions found with family support.

Animal Crossing is an online multiplayer game that supports social communication and collaboration. Its recent version, New Horizons, is immensely popular having sold over 32 million copies worldwide, with many players attracted to the opportunities it provides to remotely socialize during the COVID-19 pandemic. To understand players' increased positive emotions and social interactions, we surveyed 119 of them betweenMay and December 2020 and conducted remote interviews with 25 respondents. We identified the social dynamics among players and with non-player characters (NPCs), and analyzed how positive social interactions were facilitated under player-generated narratives and game-determined narratives. Based on our empirical analyses, we have extended our understanding of how to create positive, safe, and friendly interactions: (1)the design of mood-improving game worlds with flexible game tasks, (2) implementation of game-determined activities with social implications, (3) provision of player rewards to reinforce their social interactions, and (4)creation of opportunities to integrate NPCs' game-determined narratives into player-generated narratives.

Because the nature of chronic pain is complex, pharmacological analgesics are often not enough to achieve an ideal treatment plan. Virtual Reality (VR) technologies have emerged within medical research in recent years for treating acute pain, and proved to be an effective strategy based on pain distraction. This paper describes a VR system designed for chronic pain patients. The system incorporates biofeedback sensors, an immersive virtual environment, and stereoscopic sound titled the "Virtual Meditative Walk" (VMW). It was designed to enable chronic pain patients to learn Mindfulness-based stress reduction (MBSR), a form of meditation. By providing real-time visual and sonic feedback, VMW enables patients to learn how to manage their pain. A proof-of-concept user study was conducted to investigate the effectiveness of the VR system with chronic pain patients in clinical settings. Results show that the VMW was more effective in reducing perceived pain compared to the non-VR control condition.