Verifying or Clarifying? User Preferences for Mobile Crowdsourcing in Response to Seemingly Inconsistent Sensor DataIn the realm of smart cities, sensor technologies play a pivotal role in monitoring urban facilities and environments, providing real-time, site-specific information to residents. However, discrepancies often arise in sensor data due to variances in granularity, abstraction, and scope, which can foster uncertainty regarding the actual conditions on-site. This study explores whether, under these circumstances, individuals prefer on-site mobile crowds for verification purposes or for the provision of supplementary contextual information to aid in decision-making. Conducting an online study with 100 participants from our home country, who engaged in a think-aloud process while utilizing smart city sensor data for decision-making, our findings indicate that participants more often (54%) preferred seeking verification over supplementary contextual information (46%). Both pre-existing expectations and the sense of task urgency affected participants' choices between verification and supplementary contextual information. However, we found that the driving factor for seeking supplementary contextual information was not sensor data deviating from pre-existing expectations, but rather the absence of such pre-existing expectations. Our qualitative data also uncovered five primary motivations and four factors influencing the choice of crowdsourced information. Overall, these findings contribute to our understanding of how people leverage on-site mobile crowds to supplement sensor data in the context of smart cities.2025YCYou-Hsuan Chiang et al.Crowdsourcing & Peer ProductionCSCW
Understanding How Chatbot Phrasing Styles and Care Demonstration Influence Overweight Users’ Adherence Intention Towards Chatbots Supporting Weight Management Chatbots hold promise as a technology to aid in sustained weight management. However, determining the optimal way for chatbots to deliver advice to effectively change user behaviors remains a significant hurdle. This research investigates the effects of different chatbot communication styles and expressions of care on user satisfaction, misinterpretation, and intent to adhere to the advice in weight-related conversations. A mixed method study with 97 participants classified as overweight was conducted, dividing them into four groups based on explicit/implicit communication styles and the presence or absence of caring language. Surprisingly, the study found that most participants in the explicit communication groups viewed the chatbot as non-offensive. These participants also reported higher levels of enjoyment and a greater intention to follow the chatbot's recommendations. Utilizing caring language may diminish users' perception of the chatbot as a marketing tool, thereby increasing their willingness to interact. The article discusses the implications for the design of healthcare chatbots.2025WCWen-Hsuan Cheng et al.AI-Assisted HealthcareCSCW
Synthia: Visually Interpreting and Synthesizing Feedback for Writing RevisionWhile recent advances in HCI and generative AI have improved authors' access to feedback on their work, the abundance of critiques can overwhelm writers and obscure actionable insights. We introduce Synthia, a system that visually scaffolds feedback-based writing revision with LLM-powered synthesis. Synthia helps authors strategize their revisions by breaking down large feedback collections into interactive visual bubbles that can be clustered, colored, and resized to reveal patterns and highlight valuable suggestions. Bidirectional highlighting links each feedback unit to its original context and relevant parts of the text. Writers can selectively combine feedback units to generate alternative drafts, enabling rapid, parallel exploration of revision possibilities. These interactions support feedback curation, interpretation, and experimentation throughout the revision process. A within-subjects study (N=12) showed that Synthia helped participants identify more helpful feedback, explore more diverse revisions, and revise with greater intentionality and transparency than a GPT-4-based writing interface.2025CZRuidong Zhang et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationInteractive Data VisualizationUIST
JettingPointer: Enabling Skin-to-Pointer Midair Touch Interaction on Minimal Wearables Using Integrated Airflow Haptic CuesWe introduce JettingPointer, a skin-to-pointer interaction technique that enables accurate near-surface 2D touch input on minimal wearable devices, such as smart glasses. The core component is an airflow jet, embedded in the glasses frame, that functions as a haptic pointer by providing localized feedback to the finger skin during touch interactions performed above the frame. Users activate functions by aligning their finger phalanx with the airflow stream, guided by proprioception and a distinct point sensation. We optimized the airflow using fluid dynamics principles and characterized the required flow rate for stable tactile perception. In Study 1, we validated its perceptual clarity, confirming that a perceptible point sensation could be reliably achieved within 20 mm of the nozzle. In Study 2, participants performed eyes-free touch tasks with nearly three times greater accuracy when supported by haptic feedback (7.49<< vs. 21.85<< error). These findings demonstrate the potential of JettingPointer as a practical method for enabling proprioception-guided, near-surface interaction on compact wearables, with implications for expanding dense input in space-constrained form factors.2025YFYuan-Ling Feng et al.Mid-Air Haptics (Ultrasonic)Haptic WearablesMobileHCI
Surrogate Avatar: Enhancing Situated Co-Presence and User Mobility in Symmetric Telepresence ConversationsWe present Surrogate Avatar, an adaptive telepresence method that enhances user mobility and situated co-presence in symmetric avatar-mediated communication. The system enables a remote user’s avatar to autonomously position itself in socially and environmentally appropriate locations within the local user’s space—based on spatial affordances, interactional norms, and environmental constraints—supporting fluid interaction without requiring a shared environmental context. Through a formative study, we derived key adaptation objectives and implemented them using a distributed optimization framework based on the AUIT system. The framework distributes adaptation tasks across server and client to balance responsiveness and computational efficiency. A user study involving both stationary and nomadic scenarios demonstrated consistently high usability and presence, with some limitations observed under walking conditions. An additional exploratory field study in a semi-structured public setting demonstrated the system’s viability beyond controlled lab conditions. These findings motivate future designs of mobile telepresence systems that dynamically adapt to spatial and conversational context while mitigating misunderstandings that can arise from asymmetric environmental awareness and supporting privacy-sensitive interaction.2025SLSheng-Cian Lee et al.Teleoperation & TelepresenceMobileHCI
From Overwhelmed to Overview: Understanding Smartphone Users' Preferences and Expectations in Relieving Notification Overload via Text SummarizationTo help users manage the overwhelming influx of smartphone notifications, this study explores how large language models (LLMs) can be leveraged to generate notification summaries. We developed an Android application that integrates ChatGPT to summarize notifications and conducted an in-the-wild deployment to examine how users guided the model. To further understand user expectations for LLM-generated summaries, we interviewed 20 participants following a week-long engagement with the app. Our findings reveal five main strategies that users employed in their prompts for generating summaries. Additionally, interviewees expected summaries to prioritize three types of notifications, preferred three levels of information disclosure influenced by content anticipation and perceived criticality, and used three different approaches to synthesizing notifications based on their interrelationships. Finally, interviewees envisioned notification summarization functioning like a virtual assistant, desiring capabilities beyond simple information condensation, including support for task and information management, revisiting archived content, and tracking activities for reflection.2025UCUei-Dar Chen et al.Human-LLM CollaborationNotification & Interruption ManagementMobileHCI
Bridging Coaching Knowledge and AI Feedback to Enhance Motor Learning in Basketball Shooting Mechanics Through a Knowledge-Based SOP FrameworkWe present a methodology for designing an AI feedback system aimed at assisting basketball beginners in refining their shooting techniques during independent practice sessions. Mastering shooting mechanics requires consistent, precise repetition, which traditionally depends on coaching feedback and the breakdown of movements into steps during the early stages. However, due to limited coaching resources, this guidance is often unavailable, leading to ineffective and even detrimental motor learning. To bridge this gap, we propose a Standard Operating Procedure (SOP) framework grounded in expert human knowledge, or knowledge-based SOP, which allows our AI-driven system to verify and guide players' movements in real-time. Through a formative study involving interviews with 13 coaches and players, we identified key challenges faced by beginners, such as uncertainty in movement correctness and lack of guidance during unsupervised practice. Our AI system addresses these issues by providing immediate, actionable feedback using SOP tailored to individual players. In a study with 28 participants, we confirmed that our system improves shooting form, increases confidence in adjustments, and enhances self-awareness during practice. This work highlights the potential of integrating coaching expertise with AI to empower athletes with more effective tools for self-directed practice.2025JWJian-Jia Weng et al.National Tsing Hua University, Institute of Service ScienceMultiplayer & Social GamesFitness Tracking & Physical Activity MonitoringCHI
What Social Media Use Do People Regret? An Analysis of 34K Smartphone Screenshots with Multimodal LLMSmartphone users often regret aspects of their phone use, especially social media use. However, pinpointing specific ways in which the design of an interface contributes to regrettable use can be challenging due to the complexity of social media app features and user intentions. We conducted a one-week study with 17 Android users, using a novel method where we passively collected screenshots every five seconds, which we analyzed via a multimodal large language model to understand participants’ usage activity at a fine-grained level. Triangulating this data with data from experience sampling, surveys, and interviews, we found that regret varies based on user intention, with non-intentional and social media use being especially regrettable. Regret also varies by social media activity; participants were most likely to regret viewing algorithmically recommended content and comments. Additionally, participants frequently deviated to browsing social media when their intention was direct communication, which slightly increased their regret. Our findings provide guidance to designers and policy-makers seeking to improve users’ experience and autonomy.2025LGLongjie Guo et al.University of Washington, The Information SchoolExplainable AI (XAI)Social Platform Design & User BehaviorMisinformation & Fact-CheckingCHI
SeeThroughBody: Mitigating Occlusion through Body Transparency to Enhance Touch Interaction between the Foot and Interactive FloorOcclusion, often caused by the user's body or fingers, can significantly reduce the efficiency and usability of touch interfaces. As foot-based interactions in HMDs become more prevalent, self-occlusion becomes a more pronounced issue due to the involvement of the body and legs. This work presents SeeThroughBody, a body-rendering approach designed to mitigate occlusion and enhance touch interactions between the foot and interactive floor in virtual environments. Our user study unveiled twofold results. First, changing VisualizationStyles and BodyPartsVisibility can improve objective performance (e.g., time, movement) by reducing occlusion. Second, these modifications also affect the subjective user experience (e.g., embodiment, usability). Different VisualizationStyles and BodyPartsVisibility have varying impacts, presenting trade-offs between performance and experience. Based on these insights, we recommend Transparent-Foot and Outline-Foot for interactions focused on efficiency, and Transparent-All and Transparent-Thigh for enhancing overall user experience. Finally, we demonstrate the application of these recommendations in a map browsing scenario using foot touch.2025MSMeng Ting Shih et al.National Yang Ming Chiao Tung University, Institute of Computer Science and EngineeringFull-Body Interaction & Embodied InputFoot & Wrist InteractionCHI
Friction: Deciphering Writing Feedback into Writing Revisions through LLM-Assisted ReflectionThis paper introduces Friction, a novel interface designed to scaffold novice writers in reflective feedback-driven revisions. Effective revision requires mindful reflection upon feedback, but the scale and variability of feedback can make it challenging for novice writers to decipher it into actionable, meaningful changes. Friction leverages large language models to break down large feedback collections into manageable units, visualizes their distribution across sentences and issues through a co-located heatmap, and guides users through structured reflection and revision with adaptive hints and real-time evaluation. Our user study (N=16) showed that Friction helped users allocate more time to reflective planning, attend to more critical issues, develop more actionable and satisfactory revision plans, iterate more frequently, and ultimately produce higher-quality revisions, compared to the baseline system. These findings highlight the potential of human-AI collaboration to foster a balanced approach between maximum efficiency and deliberate reflection, supporting the development of creative mastery.2025CZChao Zhang et al.Cornell UniversityHuman-LLM CollaborationAI-Assisted Creative WritingCHI
Understanding How Psychological Distance Influences User Preferences in Conversational versus Web SearchConversational search offers an easier and faster alternative to conventional web search, while having downsides like a lack of source verification. Research has examined performance disparities between these two systems in various settings. However, little work has investigated how changes in the nature of a search task affect user preferences. We investigate how psychological distance - the perceived closeness of one to an event - affects user preferences between conversational and web search. We hypothesise that tasks with different psychological distances elicit different information needs, which in turn affect user preferences between systems. Our study finds that, under fixed condition ordering, greater psychological distances lead users to prefer conversational search, which they perceive as more credible, useful, enjoyable, and easy to use. We reveal qualitative reasons for these differences and provide design implications for search system designers.2025YYYitian Yang et al.National University of Singapore, Computer ScienceConversational ChatbotsExplainable AI (XAI)CHI
Exploring Effects of Chatbot's Interpretation and Self-disclosure on Mental Illness StigmaChatbots are increasingly being used in mental healthcare – e.g., for assessing mental-health conditions and providing digital counseling – and have been found to have considerable potential for facilitating people’s behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants’ statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants’ comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots’ conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants’ stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot’s self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users’ perceptions of the chatbots themselves, and how chatbots’ interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.2024YCYichao Cui et al.Session 3b: Bridging Technology and TherapyCSCW
BodyTouch: Investigating Eye-Free, On-Body and Near-Body Touch Interactions with HMDsCheng 等人提出 BodyTouch 系统，探索在佩戴 HMD 条件下无需视觉注视的本体触觉和近身触觉交互方式。2024WCWen-Wei Cheng et al.Hand Gesture RecognitionFull-Body Interaction & Embodied InputUbiComp
"I Want Lower Tone for Work-Related Notifications": Exploring the Effectiveness of User-Assigned Notification Alerts in Improving User Speculation of and Attendance to Mobile NotificationsResearch indicates that smartphone users often speculate about notifications upon sensing their arrival, aiding their decision to attend to them. This speculation, however, relies on the presence of sufficient clues to associate with the notification, which are not always available. To address this challenge, through an experience sampling study, we investigated the effectiveness of delivering user-assigned alerts in influencing users' speculation accuracy, attendance effectiveness, and perceived disturbance. Our findings suggest that while user-assigned alerts enhanced the accuracy of speculation and improved participants’ decisions to attend to notifications, the increased notification awareness sometimes led participants to view their decision to ignore notifications as less favorable. Moreover, we found that sporadic alert delivery disrupted the association between the alert and the notification, leading to no reduction in perceived disturbance nor improvement in speculation accuracy. In assigning alerts to notifications, participants considered five strategies: familiarity, distinctiveness, disturbance, emotional resonance, and dimension representation.2024TCTang-Jie Chang et al.Notification & Interruption ManagementWorkplace Wellbeing & Work StressMobileHCI
Investigating User-perceived Impacts of Contextual Factors on Opportune MomentsIn this exploratory experience sampling method (ESM) research, we examined the perceptions of 74 smartphone users regarding the opportuneness of moments for proceeding through a four-stage notification-response process: the phone generating an alert (Alert), the user roughly glancing at the notification (Glance), engaging with it (Engage), and acting on it (Act). We investigated how the moments perceived as opportune for each of the four stages related to users’ self-reported values of 20 contextual factors, and how these factors influenced users’ perceived opportuneness of the moments for each stage. Our results reveal that Alert and Glance stages were perceived as more distinct, with Alert being influenced by social-environmental related factors and Glance characterized by a lower threshold for what constitutes an opportune moment. The final two stages – Engage and Act – were the most similar to each other. The findings also indicated how the influence of contextual factors on perceived opportuneness of the moments varied across factors, notification types, stages, and how such variation was manifested in the likelihood, valence, and magnitude of their overall influence.2024YLYu-Jen Lee et al.Notification & Interruption ManagementMobileHCI
Seated-WIP: Enabling Walking-in-Place Locomotion for Stationary Chairs in Confined SpacesWe introduce Seated-WIP, a footstep-based locomotion technique tailored for users seated in confined spaces such as on an airplane. It emulates real-world walking using forefoot or rearfoot in-place stepping, enhancing embodiment while reducing fatigue for pro- longed interactions. Our footstep-locomotion maps users’ footstep motions to four locomotion actions: walking forward, turning-in- place, walking backward, and sidestepping. Our first study examined embodiment and fatigue levels across various sitting positions using forefoot, rearfoot, and fullfoot stepping methods. While all these methods effectively replicated walking, users favored the forefoot and rearfoot methods due to reduced fatigue. In our sec- ond study, we compared the footstep-locomotion to leaning- and controller-locomotion on a multitasking navigation task. Results indicate that footstep locomotion offers the best embodied sense of walking and has comparable fatigue levels to controller-locomotion, albeit with slightly reduced efficiency than controller-locomotion. In seated VR environments, footstep locomotion offers a harmonious blend of embodiment, fatigue mitigation, and efficiency.2024LCLiwei Chan et al.National Chiao Tung UniversityFull-Body Interaction & Embodied InputImmersion & Presence ResearchCHI
Enhancing ESL Learners' Experience and Performance through Gradual Adjustment of Video Speed during Extensive ViewingAdjusting video playback speed during extensive viewing is crucial for English-as-a-Second-Language (ESL) learners to enhance their learning experience. Since existing research suggests that abrupt speed changes might negatively impact the viewing experience, several novel speed-adjustment systems have been proposed to provide adaptive and optimal video playback speed for learners. However, empirical evidence is still sparse on whether gradual adjustments truly offer a superior experience compared to immediate changes. To delve into this, we conducted a study with 32 ESL participants, comparing direct and gradual adjustments on flow state, cognitive load, and behavioral measures. Employing both objective metrics, such as pupil diameter, and subjective feedback from surveys, our results strongly favor the gradual method. It not only enhanced flow state and video comprehension but was also less obtrusive to learners. These findings underscore the advantages of gradual speed adjustment for ESL learners, offering insights for the design of next-generation speed-adjustment systems.2024YCYu-Jung Chung et al.National Chung Cheng UniversityVoice User Interface (VUI) DesignOnline Learning & MOOC PlatformsCHI
“I Prefer Regular Visitors to Answer My Questions”: Users’ Desired Experiential Background of Contributors for Location-based Crowdsourcing PlatformThis three-phase study explores the experiential background of contributors to platforms that provide crowdsourced location-related information. Initially, we utilized interviews to understand users' expectations for location-related information and the contributors’ experiential background they believe would enhance this information's utility. We then deployed a survey to identify the top eight sought-after location-information types and their perceived characteristics. Then the concluding online scenario-based study provided quantitative evidence about the interrelationships of eight types of location-related information, ten crucial quality attributes, and aspects of the contributors' experiential background believed to enhance the utility of the descriptions they provide. Notably, although certain experiential background aspects were deemed universally advantageous across all information types, unique connections were identified among specific information types and distinct experiential background aspects seen as augmenting the contributor's descriptions' utility. These insights underline the importance of location-based crowdsourcing platforms incorporating contributors’ experiential background when assigning tasks.2024FLFang-Yu Lin et al.National Yang Ming Chiao Tung UniversityCitizen Science & Crowdsourced DataParticipatory DesignCHI
LapTouch: Using the Lap for Seated Touch Interaction with HMDs"Use of virtual reality while seated is common, but studies on seated interaction beyond the use of controllers or hand gestures have been sparse. This work present LapTouch, which makes use of the lap as a touch interface and includes two user studies to inform the design of direct and indirect touch interaction using the lap with visual feedback that guides the user touch, as well as eye-free interaction in which users are not provided with such visual feedback. The first study suggests that direct interaction can provide effective layouts with 95% accuracy with up to a 4×4 layout and a shorter completion time, while indirect interaction can provide effective layouts with up to a 4×5 layout but a longer completion time. Considering user experience, which revealed that 4-row and 5-column layouts are not preferred, it is recommended to use both direct and indirect interaction with a maximum of a 3×4 layout. According to the second study, increasing the eye-free interaction with support vector machine (SVM) allows for a 2×2 layout with a generalized model and 2×2, 2×3 and 3×2 layouts with personalized models." https://doi.org/10.1145/36108782023TMTzu-Wei Mi et al.Full-Body Interaction & Embodied InputUbiComp
"A feeling of déjà vu": The Effects of Avatar Appearance-Similarity on Persuasiveness in Social Virtual RealityThe similarity effect refers to the tendency for people to be more easily influenced by others who resemble them in appearance. This phenomenon has been found to have positive impacts, including on the building of trust, that enrich the quality of communication (e.g., fluency or collaboration performance). While research has shown that the similarity effect occurs in screen-based communication platforms, it remains unclear how this phenomenon impacts user perceptions, especially of others' persuasiveness, in immersive environments such as virtual reality (VR). In this study, we adopted a mixed-methods approach to exploring how interaction with avatars of similar appearance to one's own self-representation influences conversations. Such similarity was operationalized as having three levels: identicality, moderate similarity, and dissimilarity. The study found that avatars of moderate similarity have the greatest persuasiveness; however, in both identicality and moderate similarity conditions, participants felt it was easier to communicate with and lower eeriness rating to avatars than in the dissimilarity condition. Multiple linear regression further revealed that users who had relatively low self-esteem and/or were relatively conscientious were more susceptible to the positive effect of appearance similarity on persuasiveness. We conclude that the similarity effect, especially when the similarity in question is moderate, could be leveraged to support persuasiveness in VR-based communication.2023MSFaye Shih et al.AR/VRCSCW