Obscuring Undesirable Individuals to Alleviate Social Discomfort Using Diminished RealityIn interpersonal interactions, individuals often exhibit avoidance behaviors toward others they find unpleasant, which can undermine the comfort of everyday social experiences. Existing human-computer interaction (HCI) research has primarily focused on promoting social connections, while support for avoidance-oriented social situations remains underexplored. To address this gap, we propose leveraging Diminished Reality (DR) technology to obscure perceptual cues of undesirable individuals. We designed and implemented a mixed reality prototype system and conducted experiments manipulating both the occlusion method and social distance. Results indicate that DR significantly reduces users' social anxiety and sense of social presence. Moreover, participants generally expressed positive attitudes toward usage intention and ethical considerations. This work extends HCI research on social comfort, shifting the focus from "facilitating connection" to "supporting avoidance".2026JZJun Zhang et al.Hubei Institute of Fine ArtsImmersion & Presence ResearchIdentity & Avatars in XREmpathy & Emotional DesignCHI
AI as We Describe It: How Large Language Models and Their Applications in Health are Represented Across Channels of Public DiscourseRepresentation shapes public attitudes and behaviors. With the recent advances and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news, research press, YouTube, TikTok, and Reddit) over a two-year period on lexical style, informational content, and symbolic representation. Discussions were generally positive and episodic, with positivity increasing over time. Risk communication was unthorough and often reduced to information quality incidents, while explanations of LLMs' generative nature were rare. Compared with professional outlets, TikTok and Reddit highlighted wellbeing applications and showed greater variations in tone and anthropomorphism but little attention to risks. We discuss implications for public discourse as a diagnostic tool in identifying literacy and governance gaps, and for communication and design strategies to support more informed LLM engagement.2026JZJiawei Zhou et al.Georgia Institute of TechnologyHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityMental Health Apps & Online Support CommunitiesCHI
Proactive AI as a Catalyst for Creativity? Balancing Human Agency and AI Contribution in Collaborative Story WritingLarge Language Models (LLMs) hold promise in supporting creative writing, yet the role of proactive AI in collaborative writing remains underexplored due to concerns around human agency and disruption. To investigate effective strategies for proactive AI support, we conducted a Wizard-of-Oz study simulating two suggestion styles: intrusive suggestions (next-sentence completions) and non-intrusive suggestions (exploratory proposals), where participants completed two story outlining tasks under each style, receiving real-time proactive suggestions from a human wizard acting as the AI. Both quantitative and qualitative results show that proactive AI can enhance creativity and accelerate writing. However, we observed a trade-off between AI involvement and perceived human agency. This trade-off was moderated by how strongly AI stimulated users—greater inspiration led to stronger perceived agency even under high AI involvement. Based on wizards' behavior, we offer guidance on suggestion style and timing to better balance creativity and agency for future proactive AI writing systems.2026YYYiwen Yin et al.Tsinghua UniversityHuman-LLM CollaborationAI-Assisted Creative WritingAI-Assisted Writing & Text GenerationCHI
MlondaCam: Designing Context-Aware Smart Cameras for Supporting Domestic Security and Privacy in Malawian HomesDomestic insecurity remains a pressing concern in many parts of sub-Saharan Africa, where limited policing leaves households vulnerable. While smart cameras could potentially reduce burglary, they risk exacerbating household surveillance and patriarchal norms. In this paper, we present MlondaCam, a context-aware camera whose design was shaped by participants’ recommendations from prior studies in Malawi. Unlike conventional smart cameras, MlondaCam disables itself when placed indoors, mitigating risks of patriarchal misuse while remaining active outdoors. We deployed the system in 15 households in Lilongwe, Malawi, over four weeks. We used semi-structured interviews, diaries, and observations to collect data from participants. Our findings suggest that participants appropriated cameras in situated ways—from deterring theft to protecting food crops—while negotiating household hierarchies, gender-powered relations, and infrastructural limitations. We contribute empirical insights into how families in marginalized contexts navigate domestic security–privacy tensions and introduce design considerations for context-aware camera technologies.2026GCGeorge Chidziwisano et al.University of TennesseeSmart Home Privacy & SecurityDeveloping Countries & HCI for Development (HCI4D)Participatory DesignCHI
A Design Space for Intelligent and Interactive Writing AssistantsIn our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.2024MLMina Lee et al.Microsoft ResearchHuman-LLM CollaborationAI-Assisted Creative WritingCreative Collaboration & Feedback SystemsCHI
SmarCyPad: A Smart Seat Pad for Cycling Fitness Tracking Leveraging Low-cost Conductive Fabric Sensors"Cycling is an efficient and effective way to improve one's overall fitness level, such as cardiovascular fitness, stamina, lower body strength, and body fat percentage. To improve fitness performance, real-time cycling fitness tracking can not only allow cyclists to better control their energy outputs but also help push workout intensity and keep users accountable for their fitness progress. However, existing bike sensors (e.g., the ones mounted to bike's wheel hub or crank arm) are only limited to measuring cycling cadence and speed. Although several recent studies relying on on-body sensors or cameras can provide more fine-grained information (e.g., riding position and knee joint angle), they would either require inconvenient setups or raise serious privacy concerns. To circumvent these limitations, in this paper, we propose SmarCyPad, an innovative smart seat pad that can continuously and unobtrusively track five cycling-specific metrics, including cadence, per-leg stability, leg strength balance, riding position, and knee joint angle of the cyclist. Specifically, we embed conductive fabric sensors in the seat pad to sense the pressure applied to the bike's seat exerted by the cyclist's gluteal muscles. A series of signal processing algorithms are developed to estimate the pedaling period from the sensed pressure signal and further derive the cycling cadence, per-leg stability, and leg strength balance. Additionally, we leverage a deep learning model to detect the cyclist's riding position and reconstruct the cyclist's knee joint angles via linear regression. The sensors and the system prototype are manufactured from scratch leveraging off-the-shelf materials, and the total cost is less than $50. Extensive experiments involving 15 participants demonstrate that SmarCyPad can accurately estimate the cycling cadence with an average error of 1.13 rounds per minute, quantify the cycling stability for each leg, detect cycling imbalance, distinguish five riding positions with an accuracy of 96.60%, and continuously track the knee joint angle with an average mean error as low as 9.58 degrees." https://doi.org/10.1145/36109272023YWYi Wu et al.Micromobility (E-bike, E-scooter) InteractionBiosensors & Physiological MonitoringOn-Skin Display & On-Skin InputUbiComp
Motivated to Work or Working to Stay Motivated: A Diary and Interview Study on Working From HomeWorking from home has become common practice for many, especially since the global pandemic has forced many office workers to relocate their work spaces to a home environment. While working from home can have benefits, it requires self-discipline and can be a challenge to stay motivated. Changes in motivation about work may impact people's sense of productivity and well-being. We used a mixed-methods study using diaries and interviews with 25 informants to investigate perceived challenges during remote work from home. A grounded theory analysis revealed that people's work motivation had shifted from being people-centric to being work-centric. In the office, informants were motivated by working and interacting with others and being at their desk signaled work engagement to others. At home, motivation was mainly driven by personal work responsibilities. We identify four clusters of worker strategies to address the shift in work motivation. While some informants' perspectives on motivation made them reflect inward on their work performance and enjoyment, other informants' perspectives were outward-facing and involved performance and enjoyment in relation to others. We conclude that there needs to be better support for sustaining work motivation at home that can be tailored to different individuals, specifically in terms of managing time and detaching from work. We conclude by considering new pathways for supporting remote work.2022JBJudith Willemijn Borghouts et al.Remote Work, Motivation, and Burnout; Remote Work, Motivation, and BurnoutCSCW
“It Basically Started Using Me:” An Observational Study of Password Manager UsageThere is limited information regarding how users employ password managers in the wild and why they use them in that manner. To address this knowledge gap, we conduct observational interviews with 32 password manager users. Using grounded theory, we identify four theories describing the processes and rationale behind participants' usage of password managers. We find that many users simultaneously use both a browser-based and a third-party manager, using each as a backup for the other, with this new paradigm having intriguing usability and security implications. Users also eschew generated passwords because these passwords are challenging to enter and remember when the manager is unavailable, necessitating new generators that create easy-to-enter and remember passwords. Additionally, the credential audits provided by most managers overwhelm users, limiting their utility and indicating a need for more proactive and streamlined notification systems. We also discuss mobile usage, adoption and promotion, and other related topics.2022SOSean Oesch et al.University of TennesseePasswords & AuthenticationNotification & Interruption ManagementCHI
Mobilizing Crowdwork: A Systematic Assessment of the Mobile Usability of HITsThere is a growing interest in extending crowdwork beyond traditional desktop-centric design to include mobile devices (e.g., smartphones). However, mobilizing crowdwork remains significantly tedious due to a lack of understanding about the mobile usability requirements of human intelligence tasks (HITs). We present a taxonomy of characteristics that defines the mobile usability of HITs for smartphone devices. The taxonomy is developed based on findings from a study of three consecutive steps. In Step 1, we establish an initial design of our taxonomy through a targeted literature analysis. In Step 2, we verify and extend the taxonomy through an online survey with Amazon Mechanical Turk crowdworkers. Finally, in Step 3 we demonstrate the taxonomy's utility by applying it to analyze the mobile usability of a dataset of scraped HITs. In this paper, we present the iterative development of the taxonomy, highlighting the observed practices and preferences around mobile crowdwork. We conclude with the implications of our taxonomy for accessibly and ethically mobilizing crowdwork not only within the context of smartphone devices, but beyond them.2022SDSenjuti Dutta et al.University of Tennessee, KnoxvilleCrowdsourcing Task Design & Quality ControlAlgorithmic Fairness & BiasComputational Methods in HCICHI
What's Wrong with Computational Notebooks? Pain Points, Needs, and Design OpportunitiesComputational notebooks — such as Azure, Databricks, and Jupyter — are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document. Nevertheless, as data scientists incorporate more of their activities into notebooks, they encounter unexpected difficulties, or pain points, that impact their productivity and disrupt their workflow. Through a systematic, mixed-methods study using semi-structured interviews (n=20) and survey (n=156) with data scientists, we catalog nine pain points when working with notebooks. Our findings suggest that data scientists face numerous pain points throughout the entire workflow — from setting up notebooks to deploying to production — across many notebook environments. Our data scientists report essential notebook requirements, such as supporting data exploration and visualization. The results of our study inform and inspire the design of computational notebooks.2020SCSouti Chattopadhyay et al.Oregon State UniversityIdentity & Avatars in XRInteractive Data VisualizationComputational Methods in HCICHI
Cross-Platform Immersive Web Browsing for Online 3D Neuron Database ExplorationWeb services have become one major way for people to obtain and explore information nowadays. However, web browsers currently only offer limited data analysis capabilities, especially for large-scale 3D datasets. This project presents a method of immersive web browsing (ImWeb) to enable effective exploration of multiple datasets over the web with augmented reality (AR) techniques. The ImWeb system allows inputs from both the web browser and AR and provides a set of immersive analytics methods for enhanced web browsing, exploration, comparison, and summary tasks. We have also integrated 3D neuron mining and abstraction approaches to support efficient analysis functions. The architecture of ImWeb system flexibly separates the tasks on web browser and AR and supports smooth networking among the system, so that ImWeb can be adopted by different platforms, such as desktops, large displays, and tablets. We use an online 3D neuron database to demonstrate that ImWeb enables new experiences of exploring 3D datasets over the web. We expect that our approach can be applied to various other online databases and become one useful addition to future web services.2019WFWillis Fulmer et al.AR Navigation & Context AwarenessInteractive Data VisualizationIUI
CrowdNavi: Last-mile Outdoor Navigation for Pedestrians Using Mobile CrowdsensingNavigation services using digital maps make people's travel much easier. However, these services often fail to provide specific routes to those destinations that lack micro data in digital maps, such as a small laundry store in a shopping area. In this paper, we propose CrowdNavi, a last mile navigation service in outdoor environments using crowdsourcing based on the guider-follower model. First, we collect trajectories of guiders and images of reference objects along trajectories. To guide followers by reference objects along the route, we design a Semantic Crowd Navigation model to generate fine-grained maps by integrating guiders' data. Second, we design two score functions to fulfill two main requirements and plan hints. Last, we provide context-aware navigation for followers based on the fine-grained map and detect deviation in real-time. Real world experiments conducted in three different areas show that our proposed system in combination with images of reference objects is efficient.2018QWQianru Wang et al.Urban SpacesCSCW