Wearing Many Avatars: How Users Express, Shift and Perceive Identity Across Contexts in Social VRIn Social Virtual Reality (VR), people use avatars to express identity. But how different social contexts influence the weighting of identity aspects people attribute to avatars, and the potential impact on avatar switching to their perception of identity consistency, remains unclear. To address this gap, our study employed a questionnaire-based survey with 100 participants. We found that people place greater emphasis on expressing their age, aesthetics and culture through avatars, relative to other identity aspects. Whereas attributes, such as one's physical disabilities and mental health, are consistently hidden. Education and social status are context-dependent. Beyond adjusting these components, users also employed complete avatar switching as a strategy for meeting social expectations and protecting privacy. Furthermore, although people perceived identity change when switching avatars, their core identity was considered stable. This study advances knowledge of identity practices in digital spaces, and offers insights for designing inclusive Social VR platforms that support multi-context identity expression.2026HYHongxiang Yang et al.University of GlasgowIdentity & Avatars in XRSocial & Collaborative VRCHI
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness AssessmentAssessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 26 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments.2026LLLin Luo et al.University of GlasgowAI Ethics, Fairness & AccountabilityExplainable AI (XAI)Algorithmic Fairness & BiasCHI
CreatureConnect: Exploring Shared Control of Multimodal Displays Between People and LemursWhile zoos deploy technologies for animals' enrichment and visitors' education, little research has investigated how technology can support joint computer use by animals and people working together. To bridge this gap, we developed CreatureConnect, a distributed device with which lemurs and zoo visitors alike can control the intensity of sounds, smells, and visuals on either side of the enclosure boundary. Over 20 days of subsequent observation, we recorded 541 lemur-system interactions, observed 16,139 zoo visitors, and collected 696 sets of questionnaire responses to examine the effects of distributing control on both species across baseline, visitor-control-only, lemur-control-only, shared-control, and no-control conditions. While lemurs used CreatureConnect significantly less when controlling it alone, humans exhibited significantly greater engagement, education, empathy, and overall-experience value under shared-control conditions, which outperformed all other conditions. In light of the results and the fundamental role of interaction and interfaces in animal--computer and human--computer interaction, the paper examines its vital implications for between-species collaboration and control.2026JWJiaqi Wang et al.the University of GlasgowParticipatory DesignTangible User Interface DesignPhysical-Digital Hybrid InteractionCHI
Digital Proxemics as Measures of Social Interaction in Hybrid XRHybrid meetings are the new reality, yet they lack the richness of face-to-face interaction. In shared spaces, virtual or physical, interaction relies on more than words: proximity, non-verbal cues, and subtle movements all shape communication. Proximity captures how close we stand, where we face, and how we move around others. This paper investigates how proxemics in dyad and triad conversations translate across physical and virtual contexts. We conducted a study with 24 participants in four groups, completing social tasks under four conditions: face-to-face, co-located XR, remote XR, and hybrid XR. Our instrumentation of physical and virtual environments enables direct comparison. The work contributes a rich open dataset of 2.3 million rows across 32 columns, supporting comparative and replicable analysis. This is the first study to compare proxemics across face-to-face, co-located XR, remote XR, and hybrid XR, offering a foundation for understanding how social space translates across contexts.2026IMIain William McLean et al.University of GlasgowSocial & Collaborative VRImmersion & Presence ResearchIdentity & Avatars in XRCHI
Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI ExpertiseArtificial intelligence (AI) applications have become ubiquitous in their impact on individuals and society, highlighting a crucial need for their responsible development. Recent research has called for participatory AI auditing, empowering individuals without AI expertise to audit AI applications throughout the entire AI development pipeline. Our work focuses on investigating how to support these kinds of auditors through participatory AI auditing tools and processes. We conducted a series of co-design workshops, using two health-related predictive AI applications as examples. Our results show that participants wanted to be part of AI audits, and were insightful in identifying the potential impacts of applications, but needed to be assisted in conducting audits, especially how to measure impacts. Importantly, participants provided examples of impacts not considered in current risk/harm taxonomies. Our findings provide implications for the design of tools and processes to empower everyone to contribute to responsible AI development in the future.2026PVPatrizia Di Campli San Vito et al.University of GlasgowExplainable AI (XAI)Participatory DesignResearch Ethics & Open ScienceCHI
Gaze and Speech in Multimodal Human-Computer Interaction: A Scoping ReviewMultimodal interaction has long promised to make interfaces more intuitive and effective by combining complementary inputs. Among these, gaze and speech form a compelling pairing: gaze provides rapid spatial grounding, while speech conveys rich semantic information. Together, they offer rich cues for understanding user behaviour and intent. Yet despite decades of exploration, the research remains fragmented, making this synthesis timely as these inputs mature and are integrated into consumer-ready devices. This scoping review examined 103 studies published between 1991 and 2025, organised into \emph{explicit}, where users intentionally provide gaze and speech, and \emph{implicit}, where systems leverage users' natural behaviours to support interaction. Across both, we identified recurring ways for combining gaze and speech to resolve ambiguity, ground references, and support adaptivity. We contribute a synthesis of research on their combined use while highlighting challenges of temporal alignment, fusion and privacy, offering guidance for future research toward richer multimodal human-computer interaction.2026AKAnam Ahmad Khan et al.KAISTEye Tracking & Gaze InteractionVoice User Interface (VUI) DesignAffective Human-Computer DialogueCHI
When the World Opens up: Journeys of People with Intellectual Disabilities in Social Virtual RealityAdults with intellectual disabilities (ID) face systemic social exclusion that narrows autonomy and life opportunities. While social virtual reality (VR) offers a powerful medium for identity expression and community belonging, research often adopts a remedial paradigm, focusing on training functional skills in scripted environments. This paper challenges this deficit-based model by treating social VR as an open world for participation. Following 11 adults with ID across multi-session engagements with VRChat, we employed an adaptive, relational method to scaffold participant leadership. Findings reveal that participants used the platform for interest-driven discovery, sustained through interdependent care webs. Crucially, the study demonstrates how social VR supports transferable confidence and emerging digital citizenship, enabling some users to transition from novices to community leaders. We contribute six Disability Justice-aligned design principles articulating a \textit{world-making paradigm} that reorients Human-Computer Interaction toward supporting personhood and self-determination in mainstream digital publics.2026ACAlexandra Covaci et al.University of KentSocial & Collaborative VRIdentity & Avatars in XRCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
The (Anti-)Affordance Problem: Effects of Physical Context on Collaborator Placement in Augmented Reality MeetingsWhile Augmented Reality (AR) promises to transform remote collaboration, many aspects remain underexplored, particularly where to place remote avatars in messy, everyday environments. Two mixed-methods within-subjects studies examined avatar placement preferences during cooperative (brainstorming) and competitive (negotiation) tasks between participant pairs, focusing on the influence of physical objects (chairs, box, tree) on user preferences. Results showed a strong preference for frontal or slightly off-centre avatar placements, independent of task type. Participants preferred avatar placements that mirrored real-life behaviour, with chairs inviting placements and the tree deterring them. Notably, the large and visually simple box elicited mixed reactions, being viewed alternately as an obstacle to avoid when placing avatars or as an inviting physical anchor for them, despite causing a clear physicality conflict. We term this the "(Anti-)Affordance Problem", highlighting the complexity of avatar placement within physical contexts, and the necessity for AR collaboration platforms to respond to real-world constraints, offering flexibility in avatar placements to accommodate diverse user preferences.2026DDDiego Drago et al.University of GlasgowAR Navigation & Context AwarenessImmersion & Presence ResearchMixed Reality WorkspacesCHI
Running into Traffic: Investigating External Human-Machine Interfaces for Automated Vehicle-Runner InteractionAutomated vehicles (AVs) must communicate their yielding intentions to pedestrians at crossings. External Human-Machine Interfaces (eHMIs, on-vehicle displays) are promising solutions, but were primarily tested with walking pedestrians. Runners are a significant pedestrian group who move faster and face distinct bodily and perceptual demands, raising questions about how pedestrian activity influences eHMI use. We conducted an outdoor study using an augmented reality simulator. Participants navigated a virtual crossing while walking and running; an approaching AV displayed one of three eHMIs: red/green colour-changing lights, animated cyan lights, or no-eHMI. No-eHMI consistently underperformed. Walkers mostly stopped and validated eHMI signals with vehicle behaviour; they processed both eHMI animations and colour changes effectively. Runners experienced greater time pressure to cross, increasing reliance on the eHMI over vehicle behaviour. They preferred colour changes over animation for rapid decisions. These findings are crucial for promoting eHMI inclusivity and physical wellbeing as AVs join our roads.2026AAAmmar Al-Taie et al.University of GlasgowExternal HMI (eHMI) — Communication with Pedestrians & CyclistsCHI
Boundary Switching and Cursor Warping: A Comparative Study of Performance and Comfort in Multi-Display XR EnvironmentsExtended Reality (XR) headsets enable large, reconfigurable multi-display workspaces and support view manipulation, allowing the workspace to reposition itself around the user. Cursor warping similarly reduces traversal distance and pointer search by reinitialising the cursor at defined locations. Yet when both mechanisms operate together, the spatial relationship between user, displays, and cursor becomes dynamic, and it remains unclear how cursor repositioning behaves when the workspace itself moves. In a study (N=20) of five cursor-warping strategies with two view manipulations, we show that the benefits of both do not automatically combine: workspace motion can disrupt spatial consistency and alter both performance and movement costs. We show that continuous cursor movement in world space is limited compared to alternative warping techniques, and cursor behaviour and view control are tightly coupled. Hence, cursor initialisation and view manipulation must be co-designed to support efficient and comfortable interaction in XR multi-display environments.2026YCYuzheng Chen et al.Lancaster UniversityMixed Reality WorkspacesImmersion & Presence ResearchPrototyping & User TestingCHI
From Squishing to Meaning: Exploring Data Physicalization Through Children’s Embodied ExperiencesData physicalization is a promising approach for empowering children to understand and enjoy their own data. However, it relies on embodied metaphors to convey information effectively. This paper explores how to elicit children's embodied experiences using a set of shape-changing objects that can inform the design of dynamic physicalizations. We propose a set of auxetic metamaterials, which can bend, twist, scale and shear. Following principles of tangible interaction, we conducted a study with 59 children who participated in four movement-based games before being introduced to the collection of shape-changing tangibles. Children expressed metaphors based on these activities related to concepts such as containers, rhythm, resistance, and semantic analogies, which we categorised into embodied schemas. Our findings reveal that characteristics of the shape-changing tangibles aid children in connecting their bodily experiences to dynamic transformations. Translating these insights into idea sketches, we outline how to tailor these affordable shape-changing mechanisms into usable prototypes.2026ADAndres Alberto Ramirez Duque et al.University of GlasgowData PhysicalizationHaptic WearablesTangible Interaction in EducationCHI
"Alone and Adrift in Analytics" - Insights from Long-term Involvements with stroke Clinicians when Using Care Quality Monitoring SystemsCare quality improvement systems (CQIS) allow hospitals to monitor and improve their care by analysing performance data. However, many CQIS fail at helping clinicians improve as they see decreased use and stagnating care quality. In this paper, we investigate the use of one CQIS through ten qualitative activities carried out across five years. We synthesize and present our results regarding specific problems various stakeholders face, exact data visualization tasks that clinicians struggle with, and concrete solutions described by clinicians. Our research proposes steps that designers can take towards integrating data analysis interfaces and automating tasks in CQIS to assist with concrete data visualization problems identified by the 74 clinicians who participated in our study.2026HZHamzah Ziadeh et al.Aalborg UniversityInteractive Data VisualizationMedical & Scientific Data VisualizationAI-Assisted Decision-Making & AutomationCHI
The People's Gaze: Co-Designing and Refining Gaze Gestures with Users and ExpertsAs eye-tracking becomes increasingly common in modern mobile devices, the potential for hands-free, gaze-based interaction grows, but current gesture sets are largely expert-designed and often misaligned with how users naturally move their eyes. To address this gap, we introduce a two-phase methodology for developing intuitive gaze gestures. First, four co-design workshops with 20 non-expert participants generated 102 initial concepts. Next, four gaze interaction experts reviewed and refined these into a set of 32 gestures. We found that non-experts, after a brief introduction, intuitively anchor gestures in familiar metaphors and develop a compositional grammar; i.e., activation (dwell) + action (gaze gesture or blink), to ensure intentionality and mitigate the classic Midas Touch problem. Experts prioritized gestures that are ergonomically sound, aligned with natural saccades, and reliably distinguishable. The resulting user-grounded, expert-validated gesture set, along with actionable design principles, provides a foundation for developing intuitive, hands-free interfaces for gaze-enabled devices.2026YLYaxiong Lei et al.University of St AndrewsEye Tracking & Gaze InteractionHand Gesture RecognitionPrototyping & User TestingCHI
A decision-theoretic representation of assistive interfacesAssistive interfaces, such as recommendation engines, adaptive systems, and intelligent assistants, span diverse methods and disciplines but lack a shared conceptual foundation. This paper models assistance as sequential decision-making under uncertainty between two agents: the user and the assistant. The formalism allows casting assistance as an optimization problem and offers a rich but principled vocabulary to understand the dynamics of assistance. Drawing on Partially Observable Stochastic Games (POSGs) and related models, we: (1) motivate multi-agent over single-agent formulations; (2) adapt POSGs to HCI and clarify their tractability through reductions; (3) propose a two-agent sequential model that unambiguously defines concepts such as adaptation, augmentation, and delegation; (4) illustrate applicability through domain problems and examples; and (5) offer a supporting implementation via a library. These results warrant more attention on decision-theory as a principled yet actionable approach to assistive interfaces.2026JGJulien Gori et al.CNRS, Inserm, Sorbonne UniversitéAI-Assisted Decision-Making & AutomationExplainable AI (XAI)Recommender System UXCHI
EmotiV: Exploring Automatic Emotion Sharing through Facial Expression Recognition (FER) for Online Co-WatchingOnline video watching has become prevalent, so are technologies to promote a sense of co-watching across distances. However, most co-watching technologies require active input from users (e.g. through text-based interactions) or rely on special devices. This paper presents EmotiV, a prototype designed to bring the co-watching experience to users without additional effort or devices, by automatically capturing and sharing viewers' emotions through Facial Expression Recognition (FER). A user study with 20 participants using a comedy movie-watching scenario shows that EmotiV helped bring a sense of togetherness, aliveness and fun, and was appreciated to be more timely and authentic although with less control in comparison to traditional text-based interaction. Meanwhile, it also helped promote self-awareness and reflections, with privacy concerns to be addressed. These findings suggest that FER can serve as a lightweight and non-intrusive mechanism for augmenting remote co-watching, offering design insights for affect-aware computing to support everyday media consumption.2026YZYusen Zhang et al.University of GlasgowEmotion Recognition & DetectionAffective Feedback & Emotion Regulation InterfacesAffective Human-Computer DialogueCHI
How to categorize collaboration during a collaborative puzzle-solving task? Validation of collaboration profiles using multimodal data in virtual reality contextIn high-stakes collaborative situations, a decline in collaboration quality can lead to adverse events with significant consequences. Analyses performed by Human factor (HF) specialists, while effective in identifying and addressing collaboration issues, are case-specific and most of the time performed a posteriori. To address these limitations, our research focuses on a real-time assessment of collaboration processes using multimodal signals collected and analyzed during the activity. Existing collaboration profiles taxonomies face limitations such as a posteriori profiles detection and the absence of quantitative behavioral indicators that can be measured during the activity. Leveraging Virtual Reality (VR), we have developed a framework for evaluating collaboration in controlled setting, testing the effectiveness of a subset of multimodal signals to detect collaboration profiles. We test our approach in a study including 11 stereotyped collaborative scenarios applied to a VR puzzle-solving task. This study reveals the effectiveness of our approach in distinguishing between non-collaborative and highly collaborative profiles. However, challenges arise in discriminating between closely related collaborative profiles. This paper also proposes some guidelines on how to improve the collaboration profile detection framework and address other collaborative situations.2025ALAurélien Léchappé et al.Collaborating in Virtual EnvironmentsCSCW
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among StakeholdersNumerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the need to accommodate stakeholders' diverse fairness understandings, efforts are underway to solicit their input. However, conveying AI fairness metrics to stakeholders without AI expertise, capturing their personal preferences, and seeking a collective consensus remain challenging and underexplored. To bridge this gap, we propose a new framework, EARN (Explain, Ask, Review, and Negotiate) Fairness, which facilitates collective metric decisions among stakeholders without requiring AI expertise. The framework features an adaptable interactive system and a stakeholder-centered EARN Fairness process to Explain fairness metrics, Ask stakeholders' personal metric preferences, Review metrics collectively, and Negotiate a consensus on metric selection. To gather empirical results, we applied the framework to a credit rating scenario and conducted a user study involving 18 decision subjects without AI knowledge. We elicited their personal metric preferences and subsequently we studied how they reached metric consensus in team sessions. Our work shows that the EARN Fairness framework supports stakeholders to express and negotiate fairness preferences, and we provide practical guidance for implementing human-centered AI fairness in high-risk contexts. Through this approach, we aim to reach consensus of fairness perspectives, fostering more equitable and inclusive AI fairness.2025LLLin Luo et al.Facilitating Equity and Fairness in TechCSCW
SafeScreen: Evaluating a Screen-Detecting Smartphone Camera App under Benign and Adversarial UseCamera-equipped smartphones pose security risks to organisations by allowing intentional or accidental leaks of confidential on-screen information. We introduce SafeScreen, an Android camera app that detects and obfuscates screen content in real-time using deep-learning recognition for distant screens and Moiré pattern detection for close-up screen captures. Our mixed-methods, ecologically focused study compared "benign" (ordinary photography) and "malign" (circumventing detection) uses. Results show SafeScreen effectively prevents accidental leaks, but that the majority of users were able to exploit it by discovering workarounds such as partial screen occlusion. Our work contributes (1) a novel screen-blocking camera system, and (2) insights from real-world, unguided interactions. We show how evaluating security systems in authentic settings uncovers user-driven vulnerabilities and frustrations that inform future researchers and organisations. We close by discussing future technical features which could offer usability or security improvements, as well as emphasising the benefits of unscripted and adversarial user evaluations.2025SMShaun Macdonald et al.Privacy by Design & User ControlDeepfake & Synthetic Media DetectionIoT Device PrivacyMobileHCI
Reflecting Upon The Unintended Consequences of Personal Informatics Systems: A Systematic Review of Empirical StudiesThe HCI community has been actively developing and studying the use of Personal Informatics (PI) systems. While celebrating the headways, researchers have uncovered many unintended consequences of using PI systems, such as data-induced stress and obsessive tracking, but there has been a lack of systematic analysis of these consequences and their underlying causes. In this work, we reviewed 172 PI research articles, highlighting that tracking and interacting with personal data can adversely affect individuals' cognitive load, emotional well-being, social acts, and behaviors, while also bringing practical challenges. By synthesizing the pathways through which these consequences occur, we recognized that they arose from multiple aspects, including the data-centric design ideology, variations in individuals' tracking needs and literacy, the social dynamics surrounding them, and the intention-behavior gap. Reflecting on the findings, we discuss how to best leverage personal data in our lives and propose a practice-oriented research agenda to mitigate unintended consequences.2025YLYuhan Luo et al.AI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityPrivacy by Design & User ControlDIS
Smiles, Frowns, and Everything In Between: Understanding User Experiences with Emotion Tracking in Daily Life through Facial Expression RecognitionEmotion tracking is commonly practiced for mental wellbeing. In recent years, increasing attention is turned to automatic emotion tracking, including technologies based on Facial Expression Recognition (FER). However, work on FER based tracking so far has mainly focused on accuracy improvement, and little is on how it works in everyday lives. In this paper, we developed an FER-based emotion tracking web application, EmoAction, and used it as a technology probe to explore participants' experiences and perceptions of using FER-based emotion tracking in daily life. A field study with 9 participants trying it over 14 days reveals how such an emotion tracking tool enables people to gain new awareness and new perspectives of their emotions, and identifies several potential usages of such an automatic tracking tool. We then discuss the implications for future design, including providing real-time non-judgmental feedback, prioritizing triggering reflection over seeking maximum precision, and enabling social sharing with respect for privacy.2025ZYZhennan Yi et al.Human Pose & Activity RecognitionCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)DIS