Mind Games! Exploring the Impact of Dark Patterns in Mixed Reality ScenariosMixed Reality (MR) integrates virtual objects with the real world, offering potential but raising concerns about misuse through dark patterns. This study explored the effects of four dark patterns, adapted from prior research, and applied to MR across three targets: places, products, and people. In a two-factorial within-subject study with 74 participants, we analyzed 13 videos simulating MR experiences during a city walk. Results show that all dark patterns significantly reduced user comfort, increased reactance, and decreased the intention to use MR glasses, with the most disruptive effects linked to personal or monetary manipulation. Additionally, the dark patterns of Emotional and Sensory Manipulation and Hiding Information produced similar impacts on the user in MR, suggesting a re-evaluation of current classifications to go beyond deceptive design techniques. Our findings highlight the importance of developing ethical design guidelines and tools to detect and prevent dark patterns as immersive technologies continue to evolve.2025LMLuca-Maxim Meinhardt et al.Mixed Reality WorkspacesDark Patterns RecognitionMobileHCI
No Silver Bullet: Towards Demonstrating Secure Software Development for Small and Medium Enterprises in a Business-to-Business ModelSoftware developing small and medium enterprises (SMEs) play a crucial role as suppliers to larger corporations and public administration. It is therefore necessary for them to be able to demonstrate that their products meet certain security criteria, both to gain trust of their customers and to comply to standards that demand such a demonstration. In this study we have investigated ways for SMEs to demonstrate their security when operating in a business-to-business model, conducting semi-structured interviews (N=16) with practitioners from different SMEs in Denmark and validating our findings in a follow-up workshop (N=6). Our findings indicate five distinctive security demonstration approaches, namely: Certifications, Reports, Questionnaires, Interactive Sessions and Social Proof. We discuss the challenges, benefits, and recommendations related to these approaches, concluding that none of them is a one-size-fits all solution and that more research into relative advantages of these approaches and their combinations is needed.2025RARaha Asadi et al.IT University of Copenhagen, Computer SciencePrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Light My Way. Developing and Exploring a Multimodal Interface to Assist People With Visual Impairments to Exit Highly Automated VehiclesThe introduction of Highly Automated Vehicles (HAVs) has the potential to increase the independence of blind and visually impaired people (BVIPs). However, ensuring safety and situation awareness when exiting these vehicles in unfamiliar environments remains challenging. To address this, we conducted an interactive workshop with N=5 BVIPs to identify their information needs when exiting an HAV and evaluated three prior-developed low-fidelity prototypes. The insights from this workshop guided the development of PathFinder, a multimodal interface combining visual, auditory, and tactile modalities tailored to BVIP's unique needs. In a three-factorial within-between-subject study with N=16 BVIPs, we evaluated PathFinder against an auditory-only baseline in urban and rural scenarios. PathFinder significantly reduced mental demand and maintained high perceived safety in both scenarios, while the auditory baseline led to lower perceived safety in the urban scenario compared to the rural one. Qualitative feedback further supported PathFinder's effectiveness in providing spatial orientation during exiting.2025LMLuca-Maxim Meinhardt et al.Institute of Media Informatics, Ulm UniversityIn-Vehicle Haptic, Audio & Multimodal FeedbackVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)CHI
Unknown Word Detection for English as a Second Language (ESL) Learners using Gaze and Pre-trained Language ModelsEnglish as a Second Language (ESL) learners often encounter unknown words that hinder their text comprehension. Automatically detecting these words as users read can enable computing systems to provide just-in-time definitions, synonyms, or contextual explanations, thereby helping users learn vocabulary in a natural and seamless manner. This paper presents EyeLingo, a transformer-based machine learning method that predicts the probability of unknown words based on text content and eye gaze trajectory in real time with high accuracy. A 20-participant user study revealed that our method can achieve an accuracy of 97.6%, and an F1-score of 71.1%. We implemented a real-time reading assistance prototype to show the effectiveness of EyeLingo. The user study shows improvement in willingness to use and usefulness compared to baseline methods.2025JDJiexin Ding et al.Tsinghua University, Key Laboratory of Pervasive Computing, Ministry of Education, Department of Computer Science and Technology, Global Innovation Exchange (GIX) Institute; University of Washington, Paul G. Allen School of Computer Science & EngineeringHuman Pose & Activity RecognitionHuman-LLM CollaborationCHI
Improving External Communication of Automated Vehicles Using Bayesian OptimizationThe absence of a human operator in automated vehicles (AVs) may require external Human-Machine Interfaces (eHMIs) to facilitate communication with other road users in uncertain scenarios, for example, regarding the right of way. Given the plethora of adjustable parameters, balancing visual and auditory elements is crucial for effective communication with other road users. With N=37 participants, this study employed multi-objective Bayesian optimization to enhance eHMI designs and improve trust, safety perception, and mental demand. By reporting the Pareto front, we identify optimal design trade-offs. This research contributes to the ongoing standardization efforts of eHMIs, supporting broader adoption.2025MCMark Colley et al.Ulm University; UCL Interaction CentreExternal HMI (eHMI) — Communication with Pedestrians & CyclistsExplainable AI (XAI)CHI
Tracking and its Potential for Older Adults with Memory ConcernsMuch research on older people with memory concerns is focused on tracking and informed by the priorities of others. In this paper, we seek to understand the potential that people with memory concerns see in tracking. We conducted interviews with 29 participants with concerns about their memory and engaged in an affective writing approach. We find a range of potentials that can be traced to how participants are already self-tracking. Emotions associated with these potentials vary: from acceptance to resistance, and positive anticipation to aversion. Participants are emotionally motivated to foreclose possibilities in some instances and keep them open in others. While individual and unique, potential is structured by forces that include individual routines, relationships with others, and macro-level institutions and cultural contexts. We reflect on these findings in the context of research on self-tracking with older adults, designing with ambiguity, and forces that structure the experience of living with memory concerns.2025ASAmelia Short et al.University of MarylandMental Health Apps & Online Support CommunitiesElderly Care & Dementia SupportSleep & Stress MonitoringCHI
A Pandemic for the Good of Digital Literacy? An Empirical Investigation of Newly Improved Digital Skills during COVID-19 LockdownsThis research explores whether the rapid digital transformation due to COVID-19 managed to close or exacerbate the digital divide concerning users’ digital skills. We conducted a pre-registered survey with N = 1,143 German Internet users. Our findings suggest the latter: younger, male, and higher educated users were more likely to improve their digital skills than older, female, and less educated ones. According to their accounts, the pandemic helped Internet users improve their skills in communicating with others by using video conference software and reflecting critically upon information they found online. These improved digital skills exacerbated not only positive (e.g., feeling informed and safe) but also negative (e.g., feeling lonely) effects of digital media use during the pandemic. We discuss this research's theoretical and practical implications regarding the impact of challenges, such as technological disruption and health crises, on humans’ digital skills, capabilities, and future potential, focusing on the second-level digital divide.2025GNGerman Neubaum et al.University of Duisburg-EssenUser Research Methods (Interviews, Surveys, Observation)Sustainable HCICHI
PlantPal: Leveraging Precision Agriculture Robots to Facilitate Remote Engagement in Urban GardeningUrban gardening is widely recognized for its numerous health and environmental benefits. However, the lack of suitable garden spaces, demanding daily schedules and limited gardening expertise present major roadblocks for citizens looking to engage in urban gardening. While prior research has explored smart home solutions to support urban gardeners, these approaches currently do not fully address these practical barriers. In this paper, we present PlantPal, a system that enables the cultivation of garden spaces irrespective of one's location, expertise level, or time constraints. PlantPal enables the shared operation of a precision agriculture robot (PAR) that is equipped with garden tools and a multi-camera system. Insights from a 3-week deployment (N=18) indicate that PlantPal facilitated the integration of gardening tasks into daily routines, fostered a sense of connection with one's field, and provided an engaging experience despite the remote setting. We contribute design considerations for future robot-assisted urban gardening concepts.2025AZAlbin Zeqiri et al.Ulm University, Institute of Media InformaticsHuman-Robot Collaboration (HRC)Community Engagement & Civic TechnologyCHI
OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User ExperienceAutomated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.2025PJPascal Jansen et al.Ulm University, Institute of Media InformaticsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AI-Assisted Decision-Making & AutomationCHI
The Role of Expertise in Effectively Moderating Harmful Social Media ContentSocial media platforms played a significant role in spreading genocidal content in the 2020-2022 Tigray war, where the deadliest genocide of the 21st century was committed. While linguistic expertise is clearly needed to adequately moderate such content, we ask: What additional expertise is needed? Why and to what extent do experts disagree on what constitutes harmful content, and what is the best way to resolve these disagreements? What do social media platforms do instead? We examine these questions through a 4 month study with 7 experts labeling 340 X (formerly Twitter) posts, and by interviewing 15 commercial content moderators. We find in-depth cultural knowledge and dialects to be most important for accurate hate speech annotation – knowledge which social media platforms do not prioritize. Even amongst experts, disagreements are high (71%), dropping to 40% after deliberation meetings. Based on these results, we present 7 recommendations to improve hate speech annotation and moderation practices.2025NANuredin Ali Abdelkadir et al.University of Minnesota, Computer Science and Engineering; The Distributed AI Research InstituteAI Ethics, Fairness & AccountabilityContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
Encounter with the Giants: Understanding Interaction with Large-scale Inflatable Soft RobotsSoft robots, constructed from compliant materials, offer unique flexibility and adaptability. However, most research has focused on small-scale interactions, leaving the potential of large-scale soft robots largely unexplored. This research explores how humans engage with inflatable soft robots that are large in size and created for fun and artistic expression. We conducted 22 hours of video analysis (N=30) and thematic interviews (N=20) to understand user engagement and explore their motivations. Our findings revealed a range of interactions, from delicate touches to immersive full-body engagement, driven by trust, safety, and emotional connection. Participants frequently compared the robots to peaceful creatures like plants and sea animals, fostering playful and therapeutic interactions. These insights highlight the potential of giant soft robots in enhancing emotional well-being, therapeutic applications, and immersive experiences. This paper aims to inspire future designs that leverage the unique attributes of large-scale soft robots for trust-centered, interactive human-robot relationships.2025BBBijetri Biswas Biswas et al.University of Bristol, Faculty of Engineering ; University of Bristol, Bristol Medical SchoolShape-Changing Interfaces & Soft Robotic MaterialsCHI
PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language ModelsIn client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration and development, the client often fails to reflect their needs and expectations into a concrete actionable plan. To facilitate pre-collaboration planning, we introduce PlanTogether, a system that generates tailored client support using large language models and a Planning Information Graph, whose nodes and edges represent information in the plan and the information dependencies. Using the graph, the system links and presents information that guides client's reasoning; it provides tips and suggestions based on relevant information and displays an overview to help understand the progression through the plan. A user study validates the effectiveness of PlanTogether in helping clients navigate information dependencies and write actionable plans reflecting their domain expertise.2025DKDae Hyun Kim et al.Yonsei University, Department of Computer Science and Engineering; KAIST, Information & Electronics Research InstituteHuman-LLM CollaborationData StorytellingCHI
Social Media as Marginalisation Machine: The Trans Desire for Solidarity SpacesAs a marginalised group at increased risk of violence, trans people's perspectives on social media aid us in a nuanced understanding of current issues and consideration of more just futures. We conducted in-depth design interviews along participatory speculative activities around a utopian social media application with seven young trans participants to explore desirable and meaningful social media. Participants reported experiences of algorithmic and other forms of violence, and discussed frictions between safety and freedom as they described their embodied experiences of shifting spaces. We identify scale, commercialisation and automation as core issues, and challenge the potential of large-public, profile-centric social media spaces to support human flourishing. Drawing from aspects of social media participants consider desirable and meaningful, we discuss the idea of a shift towards interest-centric, community-oriented spaces that prioritise interactions based on solidarity over those based on identity.2025KKKay Kender et al.TU Wien, Human Computer Interaction GroupInclusive DesignGender & Race Issues in HCIEmpowerment of Marginalized GroupsCHI
When Do We Feel Present in a Virtual Reality? Towards Sensitivity and User Acceptance of Presence QuestionnairesPresence is an important and widely used metric to measure the quality of virtual reality (VR) applications. Given the multifaceted and subjective nature of presence, the most common measures for presence are questionnaires. But there is little research on their validity regarding specific presence dimensions and their responsiveness to differences in perception among users. We investigated four presence questionnaires (SUS, PQ, IPQ, Bouchard) on their responsiveness to intensity variations of known presence dimensions and asked users about their consistency with their experience. Therefore, we created five VR scenarios that were designed to emphasize a specific presence dimension. Our findings showed heterogeneous sensitivity of the questionnaires dependent on the different dimensions of presence. This highlights a context-specific suitability of presence questionnaires. The questionnaires' sensitivity was further stated as lower than actually perceived. Based on our findings, we offer guidance on selecting these questionnaires based on their suitability for particular use cases.2025ADAnnalisa Degenhard et al.University of Ulm, Media informaticsImmersion & Presence ResearchCHI
Doing the Feminist Work in AI: Reflections from an AI Project in Latin AmericaThe contemporary AI development landscape is dominated by big corporations, lacks diversity, and mostly centres the Global North, or applies extractivist logics in the South. This paper showcases a feminist process of AI development from Latin America, where we created an interactive, AI-powered tool that helps criminal court officers open justice data, addressing a data gap on gender-based violence. Through a collaborative autoethnography, drawing from Latin American feminisms, we unpack and visibilize the feminist work that was required, as a crucial step to counter hegemonic narratives. Foregrounding the subjugated knowledges of our experiences, we offer a concrete example of a feminist approach to AI development grounded in practice. With this, we aim to critically inspire those who consider building technology in service of social justice causes, or who choose to build AI systems otherwise.2025MFMarianela Ciolfi Felice et al.KTH Royal Institute of TechnologyAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasGender & Race Issues in HCICHI
I Want to Break Free: Enabling User-Applied Active Locomotion in In-Car VR through Contextual CuesWe explore the feasibility of active user-applied locomotion in virtual reality (VR) within in-car environments, diverging from previous in-car VR research that synchronized virtual motion with the car's movement. Through a two-step study, we examined the effects of locomotion methods on user experience in dynamic vehicle environments and evaluated contextual cues designed to mitigate sensory mismatch caused by vehicle motion. The first study evaluated five locomotion methods, identifying joystick-based navigation as the most suitable for in-car use due to its low physical demand and stability. The second study focused on designing and testing contextual cues that translate physical sensations of vehicle motion into virtual effects without limiting the user’s freedom of movement, with results demonstrating their effectiveness in reducing motion sickness and enhancing presence. We conclude with initial insights and design considerations for expanding upon our findings in regards to enabling active locomotion in in-car VR.2025BGBocheon Gim et al.Gwangju Institute of Science and Technology, Human-Centered Intelligent Systems LabMotion Sickness & Passenger ExperienceSocial & Collaborative VRImmersion & Presence ResearchCHI
Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social MediaInfinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.2025LMLuca-Maxim Meinhardt et al.Institute of Media Informatics, Ulm UniversityNotification & Interruption ManagementCHI
Bumpy Ride? Understanding the Effects of External Forces on Spatial Interactions in Moving VehiclesAs the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze\&Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method $\times$ Road Type $\times$ Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.2025MSMarkus Sasalovici et al.Mercedes-Benz Tech Motion GmbH; Ulm University, Institute of Media InformaticsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Motion Sickness & Passenger ExperienceCHI
Lessons from Real-World Settings: What Makes It Uniquely Difficult to Design Cognitive Training Programs for Children with Autism Spectrum Disorder and Other Developmental DisabilitiesDespite the prevalence of autism spectrum disorder (ASD) and other developmental disabilities (DD) worldwide, children with ASD and DD face tremendous difficulties receiving support due to physical, financial, and psychological barriers to onsite health and education clinics. As a result, researchers and practitioners have designed software solutions aimed at providing accessible support to meet users’ needs. However, we have limited knowledge of whether these solutions indeed work in real-world settings. To address this gap, we conducted a case study on a cognitive training program called Dubupang, designed by Dubu Inc. From in-depth interviews with multiple stakeholders and field observations of children with ASD and DD, we identify Dubu Inc.’s internal development processes, the critical design issues that emerged through a series of field trials (e.g., instructional design and feedback), and the key implications (e.g., importance of caregivers’ strategic human interventions) for design that better supports both children with ASD and DD and their caregivers.2025HPHyanghee Park et al.University of Illinois Urbana-Champaign, School of Information SciencesCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Special Education TechnologyCHI
Promoting Prosociality via Micro-acts of Joy: A Large-Scale Well-Being Intervention StudyProsociality has been well-documented to positively impact mental, social, and physical well-being. However, existing studies of interventions for promoting prosociality have limitations such as small sample sizes or unclear benchmarks. To address this gap, we conducted a global-scale well-being intervention deployment study, BIGJOY, with more than 18,000 participants from 172 countries and regions. The week-long BIGJOY intervention consists of seven daily micro-acts (i.e., brief actions that require minimal effort), each adapted from validated positive psychology interventions. The analyses of large-scale intervention data reveal unique insights into the impact of well-being micro-acts across diverse populations, patterns of responses, effectiveness of specific micro-acts and their nuanced impacts across different populations, linkages between improvements in prosociality and in well-being, as well as the potential for machine learning to predict changes in prosociality. This study offers valuable insights into a set of design guidelines for future well-being and prosociality interventions. We envision our work as a stepping stone towards future large-scale prosociality interventions that foster a more unified and compassionate world.2025HGHitesh Goel et al.International Institute of Information Technology HyderabadMental Health Apps & Online Support CommunitiesEmpowerment of Marginalized GroupsCHI