Modelling Visuo-Haptic Perception Change in Size Estimation TasksTangible interactions involve multiple sensory cues, enabling the accurate perception of object properties, such as size. Research has shown, however, that if we decouple these cues (for example, by altering the visual cue), then the resulting discrepancies present new opportunities for interactions. Perception over time though, not only relies on momentary sensory cues, but also on a priori beliefs about the object, implying a continuing update cycle. This cycle is poorly understood and its impact on interaction remains unknown. We study (N=80) visuo-haptic perception of size over time and (a) reveal how perception drifts, (b) examine the effects of visual priming and dead-reckoning, and (c) present a model of visuo-haptic perception as a cyclical, self-adjusting system. Our work has a direct impact on illusory perception in VR, but also sheds light on how our visual and haptic systems cooperate and diverge.2026JZJian Zhang et al.University of MelbourneMid-Air Haptics (Ultrasonic)Immersion & Presence ResearchEmotion Visualization & Affective DataCHI
Explainable Moderation in Multiplayer Games: Player Responses to Explanations of an Automated Temporary BanThe opaqueness of moderation systems can leave offenders of toxic behaviour disaffected and without recourse for change. We examined whether explainability, the means by which an automated system explains its decisions, can improve player responses to automated moderation decisions within the context of multiplayer games. Through a mixed methods experiment we evaluated players' perceptions of six explanations of an automated temporary ban decision. Despite finding only minor benefits to explainable AI methods over the best current explanation used in practice, we found that justification, i.e. providing evidence within an explanation, is fundamental for improving players' perceived fairness and emotional response to moderation. We contextualise these results through a reflexive thematic analysis in which we identify four themes that reflect players' competing understandings of both explainability and moderation. We conclude by proposing four design implications for researchers and developers to consider when designing explainability for future community management systems.2026THTimothy Holland et al.The University of MelbourneAgent Personality & AnthropomorphismGame UX & Player BehaviorGame AccessibilityCHI
CADModelScope: Revealing the Dependency Structure Behind Parametric Computer-Aided Design ModelsParametric computer-aided design (CAD) models are constructed by a sequence of operations, where each operation may reference geometries created by earlier operations. This network of dependencies enables efficient modelling of complex geometry but also results in fragile models, where small modifications can trigger cascading errors. These interdependencies are obscured in commercial CAD systems, leaving users to rely on trial and error when navigating, modularizing, and debugging unfamiliar and complex models. In this paper, we motivate, present, and pilot CADModelScope, a multi-level graph-based visualization of operation dependencies integrated into a commercial CAD platform. In a qualitative lab study, we observed how participants locate and interpret operations, and how CADModelScope enhances awareness of hidden interdependencies and supports more structured navigation. Our findings highlight the potential of using the network of operation dependency as an effective representation for understanding and interacting with parametric CAD models, and we discuss implications for future tool design.2026YDYuanzhe Deng et al.University of TorontoInteractive Data VisualizationCircuit Making & Hardware PrototypingCHI
Influencers vs. Legacy Media on Instagram: Effects on Perceived Credibility and Following IntentionSocial media has blurred the line between professional journalism and personality-driven commentary, yet we know little about how users evaluate credibility and engage with news from influencers and legacy media when they appear in the same feed. This short paper investigates how political ideology and news source type shape perceived credibility and follow intentions on Instagram. We conducted a mixed-methods experiment where U.S.-based participants (N=120) viewed a set of real news posts and rated the credibility of four accounts (two legacy media–based, two influencer-based), balanced by ideology (two eft-leaning, two right-leaning), and indicated whether they would follow each account. Our findings suggest that perceived credibility on Instagram is multi-dimensional, rooted in ideological alignment, yet moderated by institutional signals and perceived authenticity. These insights highlight how platform design and source dynamics can reinforce selective exposure, with implications for both mitigating polarisation and strengthening trust in online news ecosystems.2026CSCherie Sew et al.University of MelbourneSocial Platform Design & User BehaviorContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
Timing Matters: Designing Effective Corrections for Short-Form Video MisinformationShort-form video platforms have become major channels for misinformation, with their rich multimodal features making false claims highly believable. HCI research shows that providing corrections in the same modality as the misinformation can be an effective solution. However, since corrections and misinformation convey contradicting information, the order in which one is exposed to them can impact what one believes. We conducted a between-subjects mixed-methods experiment where participants (N=120) rated the credibility of misinformation statements before and after viewing misinformation videos paired with correction videos. Corrections were shown either before, during, or after misinformation. Across all three timings, corrections reduced belief in misinformation, but post-exposure corrections proved most effective and mid-exposure corrections least effective. These findings suggest that correction mechanisms should appear after misinformation exposure, while avoiding mid-exposure interruptions that reduce impact. We outline design recommendations for integrating correction videos into short-form video platforms to improve resilience against misinformation.2026SGSuwani Gunasekara et al.University of MelbourneMisinformation & Fact-CheckingSocial Platform Design & User BehaviorCHI
Narratives and Perspectives: How AI Summaries Steer Users' Opinions and Engagement on Social MediaAI summaries on social media are reshaping how users form opinions about political topics, yet their influence remains largely unexamined despite their widespread deployment. This paper investigates how two types of AI summaries affect user opinions and engagement: textual summaries of discussion narratives and percentage breakdowns of agreement/disagreement. Through a 144-participant experiment on simulated online discussion threads, we found that displaying commenter agreement percentages amplified social conformity towards the majority views beyond reading comments alone. Conversely, AI narrative summaries created misperceptions of balance in polarised threads, reducing opinion change. While these summaries did not influence participants’ willingness to engage, toxic discussions deterred participation even when participants held majority views. Based on our findings, we provide critical design interventions for industry and researchers to mitigate these tools' polarising effects, paving the way for responsible AI deployment on social media platforms.2026JGJarod Govers et al.University of MelbourneConversational ChatbotsMisinformation & Fact-CheckingAI Ethics, Fairness & AccountabilityCHI
AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical PrioritiesAI technologies that sense student attention and emotions to enable more personalised teaching interventions are increasingly promoted, but raise pressing questions about student learning, wellbeing, and ethics. In particular, students’ perspectives about AI sensing-intervention in learning are often overlooked. We conducted an online mixed-method experiment with Australian university students (N=132), presenting video scenarios varying by whether sensing was used (in-use vs. not-in-use), sensing modality (gaze-based attention detection vs. facial-based emotion detection), and intervention (by digital device vs. teacher). Participants also completed pairwise ranking tasks to prioritise six core ethical concerns. Findings revealed that students valued targeted intervention but responded negatively to AI monitoring, regardless of sensing methods. Students preferred system-generated hints over teacher-initiated assistance, citing learning agency and social embarrassment concerns. Students’ ethical considerations prioritised autonomy and privacy, followed by transparency, accuracy, fairness, and learning beneficence. We advocate designing customisable, social-sensitive, non-intrusive systems that preserve student control, agency, and well-being.2026BHBingyi Han et al.University of MelbourneBrain-Computer Interface (BCI) & NeurofeedbackExplainable AI (XAI)Mental Health Apps & Online Support CommunitiesCHI
Building Resilience in Human–Robot Collaboration: Affective and Cognitive Feedback from Robot for Human-Initiated Failure HandlingHuman–robot collaboration increasingly frames robots as teammates rather than tools, yet there is limited guidance on how robots should respond when failures are attributed to the human collaborator. We investigate how robot collaborators should respond to support collaboration experience after a human-attributed failure. In a 4 × 2 mixed factorial design (N = 60), participants completed a collaborative block-stacking task with either a humanoid robot (NAO) or a human collaborator under four scenarios: success, affective feedback, cognitive feedback, and no feedback. We measured collaboration experience in terms of teamwork quality, perceived copresence, and intimacy. Both affective and cognitive feedback improved these outcomes compared with no feedback: affective cues yielded the strongest socio-relational gains (copresence, intimacy), whereas cognitive cues more strongly enhanced perceived teamwork quality. These patterns were consistent across human–robot and human–human collaboration, indicating shared team-level expectations that extend beyond the individual actor. The results provide empirical evidence for socially adaptive robots that pair brief emotional reassurance with concrete guidance to support collaboration after human-attributed failures.2026JKJihwan Kim et al.Hanyang UniversityHuman-Robot Collaboration (HRC)Affective Feedback & Emotion Regulation InterfacesAffective Human-Computer DialogueCHI
"I'm here to see nature, not spend more time on my phone!": A Study of Technological Obtrusiveness and How it is Managed in an Urban Nature SettingAlthough people often visit places of nature to disconnect from technology, increasingly digital tools are shaping these experiences. To better understand how technologies might become obtrusive in nature settings, and how people manage such obtrusiveness, we conducted a field study of 30 adults visiting an urban nature park and using digital tools that ranged in interactive intensity from simple photography, through photo sharing and plant identification, to a more immersive site-specific augmented reality app. Distinctive facets of the way they experienced technology as obtrusive were observed: deprivation, prescriptiveness, engrossment, diminishment, neediness, unreliability, and awkwardness. We identify six tactics used to manage these experiences: abstaining, limiting, minimising, deferring, delegating, and conforming. Our findings indicate that technological obtrusiveness is a complex experience affected not only by the intensity of interactive demands, but also incongruities between the way people desire to experience nature and technology-prescribed activities, and people’s agency to deploy mitigating tactics.2026PPPiumi Perera et al.The University of MelbourneHuman-Nature Relationships (More-than-Human Design)Participatory DesignField StudiesCHI
Oops, I Did It Again (But I Know It): Robot Failure Consistency and Awareness in Human-Robot CollaborationIn human–robot collaboration, repeated failures are inevitable and can undermine trust and perceptions of robot intelligence. While some failures severely disrupt tasks and others are relatively benign, their cumulative impact on trust is not clearly understood. We investigated whether users perceive repeated failures of the same type differently from varied failures, and how robot awareness of its own failures affects these perceptions. In a collaborative physical task with 54 participants, we manipulated failure sequence (homogeneous vs. heterogeneous) and awareness (none, partial, full). Results show that trust and perceived intelligence were influenced by both current and prior failures, with homogeneous sequences leading to smaller reductions in these evaluations compared to heterogeneous ones. Robots displaying awareness, whether partial or full, were consistently rated higher than unaware robots, particularly for grasping and planning failures. Our findings provide a deeper understanding of how failure type, sequence, and robot awareness shape users' perceptions of collaborative robots.2026RTRamtin Tabatabaei et al.University of MelbourneHuman-Robot Collaboration (HRC)Teleoperation & TelepresenceCHI
Getting Hybridity Just Right: The Goldilocks Factor in Hybrid Digital BoardgamesDespite increasing interest in Hybrid Digital Boardgames (HDBs) that necessarily combine smart technology with a physical board- game, little is known about how the design of hybrid functions impacts player experience (PX). Additionally, it is unclear whether existing, videogame-centric, PX measures apply to tabletop set- tings. We designed a mixed-methods study to examine the PX of a boardgame in both its published form and as a custom HDB. We learned that players’ expectations of the “core” gameplay influence their perceptions of technology, highlighting a balance between the values and drawbacks of hybridity in relation to its impact on their sense of autonomy, fairness, and challenge in play. Our results also highlight methodological considerations for future tabletop PX studies, and suggest the existence of a Goldilocks Factor where the game offers “just right” hybridity that satisfies players without impacting core gameplay.2026SSMelissa J. Rogerson et al.University of MelbourneDigitalization of Board & Tabletop GamesSerious & Functional GamesCHI
From Quarters Per Minute to Daily Quests and Seasons: Developer Perspectives on Temporal Design in Video GamesTime is central to how video games are played, monetised, and maintained – yet how developers understand and design for time is often overlooked. This study addresses that gap through twenty semi-structured interviews with international game professionals from AAA, Indie, Mobile, and Live-service studios. Using constructivist grounded theory, we develop a practitioner-informed grounded account of temporal game design as operating between organisational constraints, data infrastructures, and player engagement. We develop three core understandings informing this account: temporal design across studio contexts – how temporal priorities range across industry; tools of temporal design – the systems and metrics that collect and target player time; and data-centered temporal design – how temporal data informs game design. From this, we contribute four temporal design heuristics to help organise design decisions about how player time is structured and communicated in games and related attention-economy systems.2026TBThomas Byers et al.The University of MelbourneGame UX & Player BehaviorGamification DesignRecommender System UXCHI
Sensemaking in Multi-Agent LLM Interfaces: How Users Interpret Transparency and Trustworthiness CuesAs multi-agent Large Language Models (LLMs) gain traction, designers must consider how to surface their internal reasoning in ways that foster appropriate trust. We present a design-led, qualitative, comparative structured observation study, exploring how users interpret and evaluate transparency in multi-agent LLMs. Participants interacted with five interface variants, each instantiating different combinations of transparency-related design dimensions, across two task types: information-seeking and logical reasoning. We surface participants’ mental models, the cues they interpret as signals of transparency and trustworthiness, and how they weigh the costs and benefits of increasing process visibility. Transparency needs were dynamic and context-sensitive, with the ideal "Goldilocks" (i.e., "just right" transparency) level shaped jointly by task demands, interface affordances, and user characteristics such as task expertise and dispositional AI trust. We highlight tensions between process visibility, information sufficiency, and cognitive effort, and synthesise these insights into design considerations for aligning transparency with user needs in future multi-agent LLM interfaces.2026SPSaumya Pareek et al.University of MelbourneHuman-LLM CollaborationExplainable AI (XAI)Privacy by Design & User ControlCHI
Exploring Meaningful Hybridity in Hybrid Digital BoardgamesAs modern tabletop play becomes more hybrid through the integration of digital tools, hybrid digital boardgames (HDBs) – games which mix physical and digital components – can be seen as ''gimmicky''. Previous work has explored the use of technology in hybrid play settings, but relatively little work exists on what makes hybridity meaningful in HDBs. In this paper, we present a model for understanding how meaningful hybridity is constructed through the relationship between the technology, game, and player. Over twelve months, we convened a monthly Critical Play Reference Group of 21 local players to play and discuss a curated selection of HDBs. We analysed 37 semi-structured group interviews for qualities of meaningful hybridity across 25 unique published HDBs. This model identifies what players assess in their HDB experience and how that maps to their overall perception of hybridity, informing the design and evaluation of meaningful hybrid game experiences.2026SSSasha Soraine et al.University of MelbourneDigitalization of Board & Tabletop GamesMakerspace CultureCHI
Exploring the Paradox of Using Digital Technologies in NaturePeople often spend time in nature to escape an over-technologised life, yet they increasingly rely on technology to do so. Understanding how technology use shapes nature experiences is crucial, given their implications for wellbeing and environmental concern. Through in-depth interviews with 30 people, we examine this technology–nature paradox, focusing on commonplace digital tools used around visits to nature. Our findings chart how people assemble and manage a carefully chosen array of tools for each outing. We show how these technology choices depend on people’s adopted mode(s) of nature engagement, identifying six modes and their associated technologies: adventure, aesthetic, ambient, enquiry, escape, and novelty. We further demonstrate how these modes shape the temporal deployment of tools across phases of a visit: before, during, and after. We offer an interpretation of how people seek to manage the technology–nature paradox and consider broader implications for designing technologies for beneficial nature experiences.2026PPPiumi Perera et al.The University of MelbourneHuman-Nature Relationships (More-than-Human Design)Sustainable HCIContext-Aware ComputingCHI
A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI PerspectivePrivacy Policies are a cornerstone of informed consent, yet a persistent gap exists between their legal intent and practical efficacy. Despite decades of research proposing various visualizations, user comprehension remains low, and designs rarely see widespread adoption. To understand this landscape and chart a path forward, we synthesized 65 top-tier papers using a framework adapted from user-centered design lifecycles. Our analysis presented four findings of the field's evolution: (1) trade-off between information load and decision efficacy, which shows a shift from augmenting disclosures to cognitive load management, (2) co-evolutionary dynamic of design and automation, revealing that designs such as context-awareness drove automation needs, while LLM breakthroughs enable the semantic interpretation required to realize those designs, (3) tension between generality and specificity, highlighting the divergence between standardized solutions and the increasing necessity for specialized interaction in IoT and immersive environments, and (4) balancing stakeholder opinions, where visualization efficacy is constrained by the interplay of regulatory mandates, developer capabilities and provider incentives.2026SZShuning Zhang et al.Tsinghua UniversityPrivacy Perception & Decision-MakingPrivacy by Design & User ControlExplainable AI (XAI)CHI
Safeguarding Crowdsourcing Surveys from ChatGPT through Prompt InjectionChatGPT and other large language models (LLMs) have proven useful in crowdsourcing tasks, where they can effectively annotate machine learning training data. However, this means that they also have the potential for misuse, specifically to automatically answer surveys. LLMs can potentially circumvent quality assurance measures, thereby threatening the integrity of methodologies that rely on crowdsourcing surveys. In this paper, we propose a mechanism to detect LLM-generated responses to surveys. The mechanism uses "prompt injection," such as directions that can mislead LLMs into giving predictable responses. We evaluate our technique against a range of question scenarios, types, and positions, and find that it can reliably detect LLM-generated responses with more than 98% effectiveness. We also provide an open-source software to help survey designers use our technique to detect LLM responses. Our work is a step in ensuring that survey methodologies remain rigorous vis-a-vis LLMs.2025CWChaofan Wang et al.Working with AICSCW
LuxAct: Enhance Everyday Objects for Visual Sensing with Interaction-Powered IlluminationImbuing sensing and interactivity into everyday objects has long been sought after within the HCI community to facilitate richer and more immersive user experiences. However, conventional methods rely on costly hardware, such as embedded sensor tags, or passive visual markers that lack digital capabilities to sense user context. We present LuxAct, an interaction-powered visual communication system that enables everyday objects to encode their information and user interaction data into sequences of RGB color light. These sequences are decoded by Point of View (POV) cameras on AR headsets or smart glasses to derive meaningful information from interactions. LuxAct are self-powered and ultra-low-cost, leveraging striking and plucking on piezoelectric generators to harvest energy from user interactions. Through strategic pattern design, our system transforms visual channels into carriers of both object identification and sensory data, supporting applications with rich sensing needs. We demonstrated a wide range of use cases, including interactive controls, sensate storage, smart water hose, medicine reminder, fingertip probes and beyond, offering a practical alternative for digitalizing passive objects to enable ubiquitous sensing in AR-enhanced environments.2025XYXiaoying Yang et al.Haptic WearablesContext-Aware ComputingUIST
A Balancing Act: Navigating Effort, Sustainability, Explainability, and Disconnection in Personal Informatics Ecologies for Physical ActivityExperiential accounts of personal informatics are important as they inform us about users' lived experiences with tracking technologies. However, these accounts describe experience at a meta-level and are not specifically related to how personal informatics is often composed of a collection of interconnected artefacts---what we call an ecology. To design personal informatics artefacts, we need a thorough understanding of how they are experienced as ecologies in practice. To this end, we interviewed 12 users of these ecologies using an interpretative phenomenological analysis approach. Our results show that users experience these ecologies through four aspects: effort minimising, sustainable tracking, performance explainability, and disconnection. We conceptualised these aspects as an automation experience---where technologies work collectively to minimise user effort while maximising insight. This conceptualisation provides a novel lens for analysis that can inform the design of more integrated and user-centered personal informatics ecologies.2025AAAhed AladwanFitness Tracking & Physical Activity MonitoringContext-Aware ComputingMobileHCI
"I can feel the risks by looking at the robot face": Communicating Risk through a Physical AgentRisk communication is essential for shaping public understanding and encouraging action in response to hazards. We investigate the potential of physical humanlike agents as a novel visualisation interface for risk communication, given their ability to communicate emotion and visually convey information. We first conducted a design workshop with 9 HCI experts to identify challenges, opportunities, and design strategies for using an agent's face as a visualisation canvas. We then conducted a lab study with 28 participants to assess the effectiveness of this interface to visualise the consequences of health risks. Our findings reveal that it facilitates data comprehension, heightens risk perception, elicits empathy, and motivates behavioural change by making the risk relatable and emotionally resonant. We discuss the potential of using these interfaces for risk communication in public spaces, health campaigns, education, and beyond. We provide design considerations, takeaways and future directions for an important pathway of human-centered risk communication.2025SSSarah Schömbs et al.Social Robot InteractionCommunity Engagement & Civic TechnologyDIS