Help-seeking and coping strategies for technology-facilitated abuse experienced by youthTechnology provides youth (ages 10--17) with near-constant opportunities for learning, communication, and self-expression. It can also expose them to technology-facilitated abuse: harassment, coercion, fraud, and more. The ability of youth to navigate such abuse is crucial for their well-being and development. A recent advisory by the U.S. Surgeon General called for better support of youth, including that youth should ``reach out for help.'' However, little is known about how youth seek help or otherwise cope with technology-facilitated abuse. Through a qualitative study in the U.S., we examine how youth engage in self-reliance, seek help from others, and how others seek help on a youth's behalf. We discuss these strategies and outline opportunities for how the HCI community can better support youth who experience technology-facilitated abuse.2025DFDiana Freed et al.Supporting YouthCSCW
Towards Designing Social Interventions for Online Climate Change Denialism DiscussionsAs conspiracy theories gain traction, it has become crucial to research effective intervention strategies that can foster evidence and science-based discussions in conspiracy theory communities online. This study presents a novel framework using insider language to contest conspiracy theory ideology in climate change denialism on Reddit. Focusing on discussions in two Reddit communities, our research investigates reactions to pro-social and evidence-based intervention messages for two cohorts of users: climate change deniers and climate change supporters. Specifically, we combine manual and generative AI-based methods to craft intervention messages and deploy the interventions as replies on Reddit posts and comments through transparently labeled bot accounts. On the one hand, we find that evidence-based interventions with neutral language foster positive engagement, encouraging open discussions among believers of climate change denialism. On the other, climate change supporters respond positively, actively participating and presenting additional evidence. Our study contributes valuable insights into the process and challenges of automatically delivering interventions in conspiracy theory communities on social media, and helps inform future research on social media interventions.2025RZRuican Zhong et al.Mitigating MisinformationCSCW
Towards Equitable Community-Industry Collaborations: Understanding the Experiences of Nonprofits' Collaborations with Tech CompaniesCommunity-based partnerships are essential to creating inclusive and equitable technologies and design practices. Though recent scholarship in HCI focuses on equitable design practices, there is less focus on understanding the experiences of community-based nonprofit organizations (CBOs) when partnering with technology companies. In this paper, we focus on understanding the perspectives of CBOs by answering the following research question: What are the experiences of CBOs that have collaborated with technology companies? Through a series of design workshops with 18 participants who work at community-based nonprofits that have collaborated with technology firms, we identified four elements of community-industry collaborations that collectively shape the overall experience: divergences in cultural and organizational norms, "setting the table," project relationship dynamics, and affective qualities. We conclude by discussing the power structures that impact community-industry collaboration and suggest reflective practices to guide equitable collaborations between CBOs and tech companies.2025SESheena Erete et al.Community Engaged ResearchCSCW
Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning ModelsSupervised machine-learning models often underperform in predicting user behaviors from conversational text, hindered by poor crowdsourced label quality and low NLP task accuracy. We introduce the Metadata-Sensitive Weighted-Encoding Ensemble Model (MSWEEM), which integrates annotator meta-features like fatigue and speeding. First, our results show MSWEEM outperforms standard ensembles by 14% on held-out data and 12% on an alternative dataset. Second, we find that incorporating signals of annotator behavior, such as speed and fatigue, significantly boosts model performance. Third, we find that annotators with higher qualifications, such as Master's, deliver more consistent and faster annotations. Given the increasing uncertainty over annotation quality, our experiments show that understanding annotator patterns is crucial for enhancing model accuracy in user behavior prediction.2025LNLynnette Hui Xian Ng et al.Communicating properly, interpreting signsCSCW
ForcePinch: Force-Responsive Spatial Interaction for Tracking Speed Control in XRSpatial interaction in 3D environments requires balancing efficiency and precision, which requires dynamic tracking speed adjustments. However, existing techniques often couple tracking speed adjustments directly with hand movements, reducing interaction flexibility. Inspired by the natural friction control inherent in the physical world, we introduce ForcePinch, a novel force-responsive spatial interaction method that enables users to intuitively modulate pointer tracking speed and smoothly transition between rapid and precise movements by varying their pinching force. To implement this concept, we developed a hardware prototype integrating a pressure sensor with a customizable mapping function that translates pinching force into tracking speed adjustments. We conducted a user study with 20 participants performing well-established 1D, 2D, and 3D object manipulation tasks, comparing ForcePinch against the distance-responsive technique Go-Go and speed-responsive technique PRISM. Results highlight distinctive characteristics of the force-responsive approach across different interaction contexts. Drawing on these findings, we highlight the contextual meaning and versatility of force-responsive interactions through four illustrative examples, aiming to inform and inspire future spatial interaction design.2025CZChenyang Zhang et al.In-Vehicle Haptic, Audio & Multimodal FeedbackForce Feedback & Pseudo-Haptic WeightImmersion & Presence ResearchUIST
Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR AgentsProactive AR agents promise context-aware assistance, but their interactions often rely on explicit voice prompts or responses, which can be disruptive or socially awkward. We introduce Sensible Agent,a framework designed for unobtrusive interaction with these proactive agents. Sensible Agent dynamically adapts both “what” assistance to offer and, crucially, “how” to deliver it, based on real-time multimodal context sensing. Informed by an expert workshop (n=12) and a data annotation study (n=40), the framework leverages egocentric cameras, multimodal sensing, and Large Multimodal Models (LMMs) to infer context and suggest appropriate actions delivered via minimally intrusive interaction modes. We demonstrate our prototype on an XR headset through a user study (n=10) in both AR and VR scenarios. Results indicate that Sensible Agent significantly reduces perceived intrusiveness and interaction ef-fort compared to voice-prompted baseline, while maintaining high usability.2025GLGeonsun Lee et al.AR Navigation & Context AwarenessMixed Reality WorkspacesContext-Aware ComputingUIST
Thing2Reality: Enabling Spontaneous Creation of 3D Objects from 2D Content using Generative AI in XR MeetingsDuring remote communication, participants often share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, conventional 2D representations of digital objects limits spatial referencing in immersive environments. To address this, we propose Thing2Reality, an Extended Reality (XR) meeting platform that facilitates spontaneous discussions of both digital and physical items during remote sessions. With Thing2Reality, users can quickly materialize ideas or objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Thing2Reality enables users to interact with remote objects or discuss concepts in a collaborative manner. Our user studies revealed that the ability to interact with and manipulate 3D representations of objects significantly enhances the efficiency of discussions, with the potential to augment discussion of 2D artifacts.2025EHErzhen Hu et al.Social & Collaborative VRIdentity & Avatars in XRGenerative AI (Text, Image, Music, Video)UIST
AmbigChat: Interactive Hierarchical Clarification for Ambiguous Open-Domain Question AnsweringWhen conversing with large language models, it is common that users ask an ambiguous open-domain question that could lead to multiple answers, especially when exploring new topics. For example, “Who won the US Open?” can result in different athletes according to the referred events and years. We propose AmbigChat, an automated approach that hierarchically disambiguates a factual question and guides users to navigate answers via UI widgets in a multi-turn conversational interface. Using the ambiguity taxonomy we generated from an analysis of 5,000 queries, AmbigChat identifies ambiguous facets of a question and constructs a disambiguation tree, where each level corresponds to a facet. Users can traverse the tree to explore answers via interactive disambiguation widgets and expand the conversation by referencing tree nodes through drag-and-drop. We developed our interaction design with six design professionals and demonstrate the effectiveness of the disambiguation tree generation algorithm on a variety of factual queries. Our evaluation with 16 participant showed that AmbigChat not only helped participants find answers more easily and efficiently, but also facilitated structured explorations of the topic space.2025JMJiaju Ma et al.Conversational ChatbotsHuman-LLM CollaborationUIST
HandOver: Enabling Precise Selection & Manipulation of 3D Objects with Mouse and Hand TrackingWe present HandOver, an extended reality (XR) interaction technique designed to unify the precision of traditional mouse input for object selection with the expressiveness of hand-tracking for object manipulation. With HandOver, the mouse is used to drive a depth-aware 3D cursor enabling precise and restful targeting — by hovering their hand over the mouse, the user can then seamlessly transition into direct 3D manipulation of the target object. In a formal user study, we compare HandOver against two ray-based techniques: traditional raycasting (Ray) and a hybrid method (Ray+Hand) in a 3D docking task. Results show HandOver yields lower task errors across all distances, and moreover improves interaction ergonomics as highlighted by a RULA posture analysis and self-reported measures (NASA-TLX). These findings illustrate the benefits of blending traditional precise input devices with the expressive gestural inputs afforded by hand-tracking in XR, leading to improved user comfort and task performance. This blended paradigm yields a unified workflow allowing users to leverage the best of each input modality as they interact in immersive environments.2025ETEsen K. Tütüncü et al.Hand Gesture RecognitionFull-Body Interaction & Embodied InputMixed Reality WorkspacesUIST
DialogLab: Authoring, Simulating, and Testing Dynamic Human-AI Group ConversationsDesigning compelling multi-party conversations involving both humans and AI agents presents significant challenges, particularly in balancing scripted structure with emergent, human-like interactions. We introduce DialogLab, a prototyping toolkit for authoring, simulating, and testing hybrid human-AI dialogues. DialogLab provides a unified interface to configure conversational scenes, define agent personas, manage group structures, specify turn-taking rules, and orchestrate transitions between scripted narratives and improvisation. Crucially, DialogLab allows designers to introduce controlled deviations from the script—through configurable agents that emulate human unpredictability—to systematically probe how conversations adapt and recover. DialogLab facilitates rapid iteration and evaluation of complex, dynamic multi-party human-AI dialogues. An evaluation with both end users and domain experts demonstrates that DialogLab supports efficient iteration and structured verification, with applications in training, rehearsal, and research on social dynamics. Our findings show the value of integrating real-time, human-in-the-loop improvisation with structured scripting to support more realistic and adaptable multi-party conversation design.2025EHErzhen Hu et al.Conversational ChatbotsHuman-LLM CollaborationUIST
NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video UnderstandingUsers often take notes for instructional videos to access key knowledge later without revisiting long videos. Automated note generation tools enable users to obtain informative notes efficiently. However, notes generated by existing research or off-the-shelf tools fail to preserve the information conveyed in the original videos comprehensively, nor can they satisfy users’ expectations for diverse presentation formats and interactive features when using notes digitally. In this work, we present NoteIt, a system, which automatically converts instructional videos to interactable notes using a novel pipeline that faithfully extracts hierarchical structure and multimodal key information from videos. With NoteIt’s interface, users can interact with the system to further customize the content and presentation formats of the notes according to their preferences. We conducted both a technical evaluation and a comparison user study (N=36). The solid performance in objective metrics and the positive user feedback demonstrated the effectiveness of the pipeline and the overall usability of NoteIt.2025RZRunning Zhao et al.Voice User Interface (VUI) DesignData StorytellingOnline Learning & MOOC PlatformsUIST
EI-Lite: Electrical Impedance Sensing for Micro-gesture Recognition and Pinch Force EstimationMicro-gesture recognition and fine-grain pinch press enables intuitive and discreet control of devices, offering significant potential for enhancing human-computer interaction (HCI). In this paper, we present EI-Lite, a lightweight wrist-worn electrical impedance sensing device for micro-gesture recognition and continuous pinch force estimation. We elicit an optimal and simplified device architecture through an ablation study on electrode placement with 13 users, and implement the elicited designs through 3D printing. We capture data on 15 participants on (1) six common micro-gestures (plus idle state) and (2) index finger pinch forces, then develop machine learning models that interpret the impedance signals generated by these micro-gestures and pinch forces. Our system is capable of accurate recognition of micro-gesture events (96.33% accuracy), as well as continuously estimating the pinch force of the index finger in physical units (Newton), with the mean-squared-error (MSE) of 0.3071 (or mean-force-variance of 0.55 Newtons) over 15 participants. Finally, we demonstrate EI-Lite's applicability via three applications in AR/VR, gaming, and assistive technologies.2025JZJunyi Zhu et al.Vibrotactile Feedback & Skin StimulationFoot & Wrist InteractionUIST
Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract RepresentationsInteracting with real-world objects in Mixed Reality (MR) often proves difficult when they are crowded, distant, or partially occluded, hindering straightforward selection and manipulation. We observe that these difficulties stem from performing interaction directly on physical objects, where input is tightly coupled to their physical constraints. Our key insight is to decouple interaction from these constraints by introducing proxies–abstract representations of real-world objects. We embody this concept in Reality Proxy, a system that seamlessly shifts interaction targets from physical objects to their proxies during selection. Beyond facilitating basic selection, Reality Proxy uses AI to enrich proxies with semantic attributes and hierarchical spatial relationships of their corresponding physical objects, enabling novel and previously cumbersome interactions in MR-such as skimming, attribute-based filtering, navigating nested groups, and complex multi object selections—all without requiring new gestures or menu systems. We demonstrate Reality Proxy’s versatility across diverse scenarios, including office information retrieval, large-scale spatial navigation, and multi-drone control. An expert evaluation suggests the system’s utility and usability, suggesting that proxy-based abstractions offer a powerful and generalizable interaction paradigm for future MR systems.2025XLXiaoan Liu et al.Mixed Reality WorkspacesGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationUIST
Enhancing XR Audio Realism via Multimodal Scene-Aware Acoustic RenderingIn Extended Reality (XR), rendering sound that accurately simulates real-world acoustics is pivotal in creating lifelike and believable virtual experiences. However, existing XR spatial audio rendering methods often struggle with real-time adaptation to diverse physical scenes, causing mismatches between visual and auditory cues. This sensory conflict can induce cognitive dissonance, disrupting user immersion. Drawing from domain knowledge and prior works, we explore three key scene-based audio realism enhancement strategies in the design space: 1) room geometry approximation, 2) material segmentation, and 3) semantic-driven acoustic parameter estimation. We introduce Samosa, a novel on-device system designed to render spatially accurate sound for XR by dynamically adapting to its physical environment. Leveraging multimodal Room Impulse Response (RIR) synthesis, Samosa estimates scene acoustic properties—informed by room geometry, surface materials, and acoustic context—and subsequently renders highly realistic acoustics suitable for diverse settings. We validate our system through technical evaluation using acoustic metrics for RIR synthesis across various room configurations and sound types, alongside a human expert evaluation (N=12). Evaluation results demonstrate Samosa feasibility and efficacy in enhancing XR audio realism.2025TXTianyu Xu et al.Social & Collaborative VRAR Navigation & Context AwarenessImmersion & Presence ResearchUIST
StreetViewAI: Making Street View Accessible Using Context-Aware Multimodal AIInteractive streetscape mapping tools such as Google Street View (GSV) and Meta Mapillary enable users to virtually navigate and experience real-world environments via immersive 360° imagery but remain fundamentally inaccessible to blind users. We introduce StreetViewAI, the first-ever accessible street view tool, which combines context-aware, multimodal AI, accessible navigation controls, and conversational speech. With StreetViewAI, blind users can virtually examine destinations, engage in open-world exploration, or virtually tour any of the over 220 billion images and 100+ countries where GSV is deployed. We iteratively designed StreetViewAI with a mixed-visual ability team and performed an evaluation with eleven blind users. Our findings demonstrate the value of an accessible street view in supporting POI investigations and remote route planning. We close by enumerating key guidelines for future work.2025JFJon E. Froehlich et al.Visual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Interactive Data VisualizationGeospatial & Map VisualizationUIST
WatchWithMe: LLM-Based Interactive Guided Watching of Review VideosVideos are a popular way for viewers to follow topics of interest. In areas such as product and technology reviews, videos often present in-depth perspectives in a compact fashion, driving viewers to look for additional explanations. We propose WatchWithMe, an automatic approach that provides viewers in-context guided watching during video playback. Powered by large language models, WatchWithMe generates guided materials from the video transcript as if creating a reading guide, including summaries, highlights, and question prompts. WatchWithMe reveals relevant information responsive to the spoken content in a review video. Viewers skim and prompt in our text-based conversational UI, to which we automatically expand the video viewing context to the model for contextual responses. We evaluated WatchWithMe with public videos and collected feedback from 20 participants. Findings showed that our method encouraged viewers to seek out viewpoints or confirmations related to the video topics.2025PCPeggy Chi et al.Conversational ChatbotsHuman-LLM CollaborationCUI
The GenUI Study: Exploring the Design of Generative UI Tools to Support UX Practitioners and BeyondAI can now generate high-fidelity UI mock-up screens from a high-level textual description, promising to support UX practitioners' work. However, it remains unclear how UX practitioners would adopt such Generative UI (GenUI) models in a way that is integral and beneficial to their work. To answer this question, we conducted a formative study with 37 UX-related professionals that consisted of four roles: UX designers, UX researchers, software engineers, and product managers. Using a state-of-the-art GenUI tool, each participant went through a week-long, individual mini-project exercise with role-specific tasks, keeping a daily journal of their usage and experiences with GenUI, followed by a semi-structured interview. We report findings on participants' workflow using the GenUI tool, how GenUI can support all and each specific roles, and existing gaps between GenUI and users' needs and expectations, which lead to design implications to inform future work on GenUI development.2025XCXiang 'Anthony' Chen et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationAI-Assisted Creative WritingDIS
Gemini at Work: Knowledge Workers' Perceptions and Assessment of Productivity GainsThe rise of Generative AI (GenAI) presents a paradigm shift in knowledge work. To examine its impact on productivity, we conducted seven focus groups (n=37) with employees across diverse job functions in an enterprise setting, where workers engaged with a large language model (LLM) embedded in a suite of productivity applications. Our study identified four categories of GenAI-facilitated work activities: information management, content generation, problem solving, and communication and collaboration. These findings offer a grounded framework of GenAI-enabled practices for both researchers and practitioners, while also surfacing key challenges in realizing GenAI's promised productivity gains. Beyond conventional metrics like time savings or output volume, participants attributed productivity improvements to time redistribution, enhanced decision-making, and reduced reliance on traditional intermediaries. We contribute actionable insights for designing GenAI systems that support context-aware productivity in the evolving landscape of knowledge work.2025NSNa Sun et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationAI-Assisted Decision-Making & AutomationDIS
RestfulRaycast: Exploring Ergonomic Rigging and Joint Amplification for Precise Hand Ray Selection in XRHand raycasting is widely used in extended reality (XR) for selection and interaction, but prolonged use can lead to arm fatigue (e.g., "gorilla arm"). Traditional techniques often require a large range of motion where the arm is extended and unsupported, exacerbating this issue. In this paper, we explore hand raycast techniques aimed at reducing arm fatigue, while minimizing impact to precision selection. In particular, we present Joint-Amplified Raycasting (JAR)---a technique which scales and combines the orientations of multiple joints in the arm to enable more ergonomic raycasting. Through a comparative evaluation with the commonly used industry standard---Shoulder-Palm Raycast (SP) and two other ergonomic alternatives---Offset Shoulder-Palm Raycast (OSP) and Wrist-Palm Raycast (WP)---we demonstrate that JAR results in higher selection throughput and reduced fatigue. A follow-up study highlights the effects of different JAR joint gains on target selection and shows users prefer JAR over SP in a representative UI task.2025HMHongyu Mao et al.Hand Gesture RecognitionFull-Body Interaction & Embodied InputDIS
Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and ReasoningMultimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sensors capable of reasoning about complex situations. A user could describe a desired sensing task in natural language (e.g., "let me know if my toddler is getting into mischief in the living room"), with the MLLM analyzing the camera feed and responding within just seconds. In a formative study, we found that users saw substantial value in defining their own sensors, yet struggled to articulate their unique personal requirements to the model and debug the sensors through prompting alone. To address these challenges, we developed Gensors, a system that empowers users to define customized sensors supported by the reasoning capabilities of MLLMs. Gensors 1) assists users in eliciting requirements through both automatically-generated and manually created sensor criteria, 2) facilitates debugging by allowing users to isolate and test individual criteria in parallel, 3) suggests additional criteria based on user-provided images, and 4) proposes test cases to help users "stress test" sensors on potentially unforeseen scenarios. In a 12-participant user study, users reported significantly greater sense of control, understanding, and ease of communication when defining sensors using Gensors. Beyond addressing model limitations, Gensors supported users in debugging, eliciting requirements, and expressing unique personal requirements to the sensor through criteria-based reasoning; it also helped uncover users' own "blind spots" by exposing overlooked criteria and revealing unanticipated failure modes. Finally, we describe insights into how unique characteristics of MLLMs–such as hallucinations and inconsistent responses–can impact the sensor-creation process. Together, these findings contribute to the design of future MLLM-powered sensing systems that are intuitive and customizable by everyday users.2025MLMichael Xieyang Liu et al.Eye Tracking & Gaze InteractionContext-Aware ComputingUbiquitous ComputingIUI