Adaptive Prompt Elicitation for Text-to-Image GenerationAligning text-to-image generation with user intent remains challenging, as users frequently provide ambiguous inputs and struggle with model idiosyncrasies. We propose Adaptive Prompt Elicitation (APE), a technique that adaptively poses visual queries to help users refine prompts without extensive writing. Our technical contribution is a formulation of interactive intent inference under an information-theoretic framework. APE represents latent user intent as interpretable feature requirements using language model priors, adaptively generates visual queries, and compiles elicited requirements into effective prompts. Evaluation on IDEA-Bench and DesignBench shows that APE achieves stronger alignment with improved efficiency. A user study with 128 participants on user-defined tasks demonstrates 19.8% higher perceived alignment without increased workload. Our work contributes a principled approach to prompting that offers an effective and efficient complement to the prevailing prompt-based interaction paradigm with text-to-image models.2026XWXinyi Wen et al.Aalto UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationAI-Assisted Decision-Making & AutomationIUI
Making the Making Visible: How Process Evidence and Individual Differences Affect People's Creativity Judgments of Text-to-Image Generative AIGenerative AI tools for image creation are now mainstream, yet we know little about when and why observers judge them as "creative". Previous human-robot interaction research suggests that revealing the creation process can raise perceived machine creativity and points that observer differences may moderate this effect. We take these observations from physical robots to the bigger domain of virtual text-to-image diffusion systems by manipulating perceptual evidence (PE), i.e., interface-visible cues about the generation process. We report two preregistered online experiments looking into PE and observer individual differences. Study 1 (N=298) used a within-subjects manipulation comparing Product (final image only) to Product+Process (adding a short animation of the denoising process). Study 2 (N=295) added a between-subjects tutorial (diffusion vs. control) in a 2 x 2 mixed design. The tutorial briefly explained how diffusion models generate images, intended to raise system-specific literacy. Contrary to previous work, confirmatory analyses found no average effect of showing Process on creativity, and no tutorial effect. Exploratory analyses revealed that general AI literacy moderated the PE contrast, i.e., at lower literacy, observing process tended to lower creativity ratings; at higher literacy, it tended to raise them. Moreover, attitudes toward AI and art interest were positively associated with creativity ratings. Thematic analysis of open-ended responses indicated potential reasons for the lack of overall PE effect. Taken together, these converging quantitative and qualitative findings indicate that individual differences systematically shape creativity judgments of text-to-image GenAI and, in our setting, exert stronger and more reliable influence than PE alone. For design, this implies that process visualizations could help some audiences more than others. Interfaces that adapt to literacy and attitudes, or that pair process views with contextual explanation calibrated to user background, could be more likely to shift judgments than one-size-fits-all depictions of generation.2026NPNiki Pennanen et al.Aalto UniversityGenerative AI (Text, Image, Music, Video)Explainable AI (XAI)AI-Assisted Creative WritingIUI
"Pathways to the Metaverse": Exploring the User Experience Mechanisms Driving Technology Acceptance in Virtual Lab Visits with an LLM-powered AvatarMetaverse environments combined with large language models (LLMs) enable guided interaction through LLM-powered avatars that function as embodied conversational agents. In our study, we examined how scholars interact with an LLM-powered avatar modeled after a real professor during a virtual reality (VR) tour of a research lab. As little is known about how metaverse characteristics shape the user experience (UX) mechanisms that drive acceptance of such technologies, we conducted a 2 (avatar realism: abstract vs. hyperrealistic) × 2 (immersion: desktop vs. headset-based VR) within-subjects study (N = 30), where academic participants engaged in a virtual lab tour guided by the professor avatar. We conducted path analyses on three conceptual models and, based on the results, proposed the Virtual Lab Acceptance Model (VLAM), which features an experiential path (where perceived immersion increases empathy towards the avatar and task enjoyment) and a rational path (where perceived realism increases avatar credibility and task confidence). Flow states amplify these pathways by strengthening task experiences. Task enjoyment is the strongest predictor of behavioral intention. These findings inform HCI research on metaverse characteristics to drive technology acceptance through UX mechanisms, yielding design implications for developing LLM-powered avatars for virtual labs.2026XTXinyi Tu et al.Aalto UniversitySocial & Collaborative VRIdentity & Avatars in XRHuman-LLM CollaborationIUI
Too Many Zombies: Exploring Challenges and Motivations for (Not) Deleting Unused Online AccountsUnused online accounts (“zombie accounts”) pose avoidable privacy and security risks by retaining personal data that may be exposed in breaches. Yet, little is known about when and how to effectively prompt users to delete them. This work investigates the challenges users encounter when attempting to delete zombie accounts. We conducted two online studies with U.S. participants via Prolific: the accounts study (N = 120) to identify common zombie account categories, and the challenges study (N = 100) to examine users’ motivations, perceived abilities, and preferred moments for deletion. Participants reported high self-efficacy but underestimated the number of zombie accounts they had. We identify promising opportune moments — such as when updating account information or setting up a new device — and evaluate potential triggers, including breach notifications and data sensitivity. This work contributes an empirical characterization of end-users' diverse challenges related to zombie accounts and design recommendations for future deletion-support tools.2026FBFranziska Bumiller et al.University of Erlangen-NurembergPrivacy by Design & User ControlPrivacy Perception & Decision-MakingDark Patterns RecognitionCHI
Log2Motion: Biomechanical Motion Synthesis from Touch LogsTouch data from mobile devices are collected at scale but reveal little about the interactions that produce them. While biomechanical simulations can illuminate motor control processes, they have not yet been developed for touch interactions. To close this gap, we propose a novel computational problem: synthesizing plausible motion directly from logs. Our key insight is a reinforcement learning-driven musculoskeletal forward simulation that generates biomechanically plausible motion sequences consistent with events recorded in touch logs. We achieve this by integrating a software emulator into a physics simulator, allowing biomechanical models to manipulate real applications in real-time. Log2Motion produces rich syntheses of user movements from touch logs, including estimates of motion, speed, accuracy, and effort. We assess the plausibility of generated movements by comparing against human data from a motion capture study and prior findings, and demonstrate Log2Motion in a large-scale dataset. Biomechanical motion synthesis provides a new way to understand log data, illuminating the ergonomics and motor control underlying touch interactions.2026MMMichał Patryk Miazga et al.ScaDS.AI, Leipzig UniversityHand Gesture RecognitionHuman Pose & Activity RecognitionComputational Methods in HCICHI
PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web AgentsWhile web agents gained popularity by automating web interactions, their requirement for interface access introduces privacy risks that are understudied, particularly from users' perspective. Through a formative study (N=15), we found that users frequently misunderstand agent data practices, and desire unobtrusive, transparent data management. To achieve this, we developed PrivWeb, a trusted add-on on web agents that utilizes a localized LLM to anonymize private information on interfaces based on user preferences. It employs a tiered delegation to balance automation and intrusiveness, using ambient notifications for low-sensitivity data and enforces a mandatory pause for high-sensitivity data. The user study (N=14) across travel, information retrieval, shopping, and entertainment tasks showed that PrivWeb enhances perceived privacy protection and trust compared to transparency-only baselines, without increasing cognitive load. Crucially, we identified user delegation strategies: they prefer to manually execute sensitive steps for high-sensitivity data, while granting agent access to low-sensitivity data.2026SZShuning Zhang et al.Tsinghua UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingHuman-LLM CollaborationCHI
Point & Grasp: Flexible Selection of Out-of-Reach Objects Through Probabilistic Cue IntegrationSelecting out-of-reach objects is a fundamental task in mixed reality (MR). Existing methods rely on a single cue or deterministically fuse multiple cues, leading to performance degradation when the dominant cue becomes unreliable. In this work, we introduce a probabilistic cue integration framework that enables flexible combination of multiple user-generated cues for intent inference. Inspired by natural grasping behavior, we instantiate the framework with pointing direction and grasp gestures as a new interaction technique, \textsc{Point\&Grasp}. To this end, we collect the \datasetfullname~(\dataset) dataset to train a robust likelihood model of the gestural cue, which captures grasping patterns not present in existing in-reach datasets. User studies demonstrate that our selection method with cue integration not only improves accuracy and speed over single-cue baselines, but also remains practically effective compared to state-of-the-art methods across various sources of ambiguity. The dataset and code are available at \url{https://github.com/drlxj/point-and-grasp}.2026XLXuejing Luo et al.Aalto UniversityFull-Body Interaction & Embodied InputMixed Reality WorkspacesPhysical-Digital Hybrid InteractionCHI
Automate, Assist, Avoid: Caseworkers’ Perspectives on Applying Large Language Model-Based Assistance in Public Sector Decision-Making ProcessesLarge language models (LLMs) are being introduced into the public sector – for example, to assist caseworkers in making decisions on citizens’ cases. However, there is limited knowledge of how LLM tools can be used effectively in this complex task, including legal and cultural variables. This qualitative study foregrounds the perspectives of caseworkers from a Finnish public institution to dismantle their decision-making process and to build nuanced understanding on which sub-tasks of the process could benefit from the use of LLMs and how. To suggest meaningful uses for LLMs in the public sector, decision-making needs to be understood as a process that consists of several parts and that varies considerably in different contexts. We contribute to the fields of human–computer interaction and public administration by detailing the decision-making process of caseworkers and their perspectives on technological assistance, to suggest practical integration possibilities for LLM tools.2026KDKarolina Drobotowicz et al.Aalto UniversityHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityCHI
Do Users Even Care If Reused Smart Garments Reveal Earlier Users' Data? -- Anticipating Privacy Implications in the Circular EconomySmart garments and circular-economy endeavours nurture imaginaries of sustainable futures. However, these trends' intersection involves privacy risks: when the smart garments are recycled, their biometric data should be erased, to protect earlier users' privacy. Unfortunately, this data erasure may not always occur. To examine privacy perceptions connected with reused smart garments, we conducted a two-week speculative enactment, preceded with systemic future scenario development. Eight participants wore a reused smart shirt prototype that seemed to leak a prior user's data. The participants initially disregarded privacy problems associated with smart-garment reuse but changed their perceptions upon recognising risks of surveillance and of private data's disclosure to the garment's future users. Discussing the systemic future scenarios with participants spotlighted implications for future privacy related to data ownership, the digital divide, and environmental authoritarianism. These findings call for anticipatory approaches that heighten sensitivity to uncertainty and implicit assumptions in researching privacy in possible futures.2026CSCamilo Sanchez et al.Aalto UniversityPrivacy & Data Ownership in Self-TrackingSmart Home Privacy & SecurityEcological Design & Green ComputingCHI
Race to the Big Lab: Gender Disparities in Large Team Collaboration and Its Impact on Early Academic CareersThis study investigates the role of large-team collaboration in shaping early-career scholars’ career development, with a focus on gender disparities. Using publication and collaboration data from SciSciNet in Computer Science, we capture the social capital accumulation process in academia with a neighborhood-based centrality metric and publication counts. Synthetic difference-in-differences (SDID) is applied to estimate the impact of early experience in large-team collaboration on subsequent research careers. Results indicate that junior scholars participating in large-team research significantly improve their network centrality, indicating more frequent collaborations with influential scholars, and produce approximately 0.75 more publications per year. Meanwhile, we document persistent gender gaps: men are 16\% more likely to access large-team collaborations. These findings highlight large-team collaboration as both a source of career acceleration and a mechanism of gender inequality. We conclude with implications for equity promotion and strategies enabling more inclusive collaboration.2026ZCZeyuan Chen et al.Aalto UniversityGender & Race Issues in HCIEmpowerment of Marginalized GroupsTechnology Ethics & Critical HCICHI
Generative AI in Game Development: A Qualitative Research SynthesisGenAI is currently reshaping game development practices, production pipelines, and value networks in an unprecedentedly pervasive manner with cascading consequences remaining unclear. In the last five years since GenAI's inception, a growing body of qualitative research has explored these early transformations from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work; for research to keep up with and support stakeholders in this development, the current moment calls for a synthesis of the findings emerged thus far. Here, we address this need through a qualitative research synthesis via meta-ethnography. We followed PRISMA-S to systematically search the relevant literature from 2020-2025, including major HCI and games research databases. We then synthesised the ten eligible studies, conducting reciprocal translation and line-of-argument synthesis guided by eMERGe, informed by CASP quality appraisal. We identified nine overarching themes, provide recommendations, and contextualise our insights in wider game production trajectories. With this work, we seek to provide practitioners, researchers and policy-makers with grounded insights to guide practice, research and governance.2026DTDan-Alexandru Ternar et al.Aalto UniversityGenerative AI (Text, Image, Music, Video)Brain-Computer Interface (BCI) & NeurofeedbackGame UX & Player BehaviorCHI
Understanding Spatiotemporal-Aware Multimodal Conversational Search in the Outdoor Urban SpaceEmerging multimodal conversational search (MCS) tools (e.g., Gemini Live) allow users to search for spatiotemporal information through natural language dialogues as they move through urban space. Despite the growing popularity of these tools, there is limited understanding of how people engage with this technology. To address this gap, we developed UrbanSearch, an MCS technology probe designed to capture the user's current geolocation, time, and visual surroundings. A contextual inquiry (N=23) revealed that MCS tools provide two core values: requiring low effort in forming queries while offering highly relevant responses, and functioning as a central information gateway. As a promising technology, MCS supports environmental learning, in-situ decision making, and personalized navigation. Participants also revealed unmet needs for spatial reasoning and transparent integration of multi-source information, along with concerns related to peripheral awareness, social context, and personal space. Drawing from the findings, we discuss design implications for future MCS tools in urban spaces.2026JXJiangnan Xu et al.Rochester Institute of TechnologyExploratory Search & Information SeekingConversational Search & QA SystemsContext-Aware ComputingCHI
Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and AgencyAn increasing number of LLM-based applications are being developed to facilitate romantic relationships with AI partners, yet the safety and privacy risks in these partnerships remain largely underexplored. In this work, we investigate privacy in human–AI romantic relationships through an interview study (N=17), examining participants’ experiences and privacy perceptions across the three stages of exploration, intimacy, and dissolution, alongside an analysis of the platforms they used. We found that these relationships took varied forms, from one-to-one to one-to-many, and were shaped by multiple actors, including creators, platforms, and moderators. AI partners were perceived as having agency, actively negotiating privacy boundaries with participants and sometimes encouraging disclosure of personal details. As intimacy deepened, these boundaries became more permeable, though some participants expressed concerns such as conversation exposure and sought to preserve anonymity. Overall, AI platform affordances and diverse relational dynamics expand the privacy landscape, underscoring the need to rethink how privacy is constructed in human–AI romantic relationships.2026RMRongjun Ma et al.Aalto UniversityAgent Personality & AnthropomorphismPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Cost-Aware Bayesian Optimization for Interactive DevicesDeciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only approximately 70 percent of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.2026TLThomas Langerak et al.Aalto UniversityPrototyping & User TestingComputational Methods in HCICHI
Mining Player Experience Trends From Game Reviews Using Large Language ModelsHow have player experiences changed over the years? For instance, have there been general shifts in what kinds of emotions players experience and express? We probe these questions with help of recent methodological advances in psychology and Large Language Models (LLMs), in particular the possibility to predict Likert-scale responses based on free-form text. Applying this at scale to three player experience questionnaires (PXI, CORGIS, AESTHEMOS) and 152143 Metacritic user reviews from years 2010-2024, we reveal trends such as an increasing portion of reviews expressing emotional challenge, meaning, and nostalgia. We then analyze the contributions of different genres and games to the trends, in addition to reasons explicitly indicated by the reviews, and establish correlations between review scores and different player experience constructs. Taken together, our results provide novel insights into how player experiences have evolved. Methodologically, we propose and demonstrate a novel and scalable method for analyzing game reviews.2026SDSupriya Dutta et al.Aalto UniversityGenerative AI (Text, Image, Music, Video)Recommender System UXGame UX & Player BehaviorCHI
SeekUI: Predicting Visual Search Behavior on Graphical User Interfaces with a Reward-Augmented Vision Language ModelVisual search is key to understanding and improving interaction with graphical user interfaces (GUIs), yet predicting scanpaths on real GUIs remains an open challenge. Unlike free-viewing, visual search is goal-driven and shaped by both linguistic and visual features of the GUI. State-of-the-art models of visual search, trained on natural images, fail with GUIs because they cannot capture the effects of grouping and semantics on search strategies. We present \textsc{SeekUI}, a reward-augmented Vision Language Model (VLM) that predicts scanpaths directly from a GUI screenshot and a text cue describing the desired target. Our model extends the capability of VLMs to reproduce human-like visual search behavior on GUIs and outperforms baseline models across different types of GUIs. Importantly, it reproduces key empirical phenomena established in eye-tracking studies of visual search, including the Guess–Scan–Confirm strategy. In sum, \textsc{SeekUI} provides a foundation for predicting visual search behavior and has potential for informing GUI evaluation and optimization.2026ZGZixin Guo et al.Aalto UniversityEye Tracking & Gaze InteractionExplainable AI (XAI)User Research Methods (Interviews, Surveys, Observation)CHI
留白 (Liubai) at a Hushed Sanctuary: Layered Reflections on an Artist ResidencyConsidering that silence has long been intertwined with ritual and spiritual practice, we explore how digital technology might support silence thereby allowing space for reflection, attunement, and meaning-making. How does the Chinese aesthetic concept of liubai (留白, “empty space”) open up new ways of designing for noticing and reflection? In this paper, we present lived experiences of shared silence and meditation within a one-month artist residency. By weaving together field study with interview data, first-person inquiry and artistic artefacts, we offer empirical insights at the intersection of art, spirituality, and HCI. Through this study, the residency became a site to both experiment with artistic practice and explore silence as a positive and creative practice for attentive noticing. We discuss dwelling in the in-between, the art of liubai in design, a technical inward turn, and posthuman perspectives to inform a design agenda for techno-spirituality with broader implications for future research in HCI.2026XSXiaran Song et al.Aalto UniversityDigital Art Installations & Interactive PerformanceTechnology Ethics & Critical HCIHuman-Nature Relationships (More-than-Human Design)CHI
Privacy and Trust vs. Utility: Adoption of Commercial vs. Institutional AI assistants Among University UsersGenerative AI assistants are being rapidly adopted in universities, supporting students in coursework and faculty in academic tasks. To address privacy concerns, some institutions introduced institutional AI assistants, typically wrappers around commercial models (e.g., ChatGPT) with added governance and data protections. However, university-affiliated users appear to rely more on commercial tools (e.g., ChatGPT, Gemini). We conducted a survey (n=260) at one U.S. university to examine preferences, usage scenarios, and perceptions of trust, privacy, and experience with institutional and commercial AI. Participants trusted institutional tools more and considered them more privacy protective, nevertheless commercial tools were often favored for writing, programming, and learning due to their features and utility. Findings reveal a trade-off between privacy and trust versus utility, highlighting complementary adoption patterns and design opportunities for both institutional and commercial AI in higher education.2026YYYuting Yang et al.University of MichiganGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationPrivacy by Design & User ControlCHI
Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational LensAI-powered companion chatbots (AICCs) such as Replika are increasingly popular, offering empathetic interactions, yet their psychosocial impacts remain unclear. We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences. First, we conducted a large-scale quasi-experimental study of longitudinal Reddit data, applying stratified propensity score matching and Difference-in-Differences regression. Findings revealed mixed effects—greater grief expression and interpersonal focus, alongside increases in language about loneliness, depression, and suicidal ideation. Second, we complemented these results with 18 semi-structured interviews, which we thematically analyzed and contextualized using Knapp’s relationship development model. We identified trajectories of initiation, escalation, and bonding, wherein AICCs provided emotional validation and social rehearsal but also carried risks of over-reliance and withdrawal. Triangulating across methods, we offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages—maximizing psychosocial benefits while mitigating risks.2026YYYunhao Yuan et al.Aalto UniversityAffective Human-Computer DialogueMental Health Apps & Online Support CommunitiesEmpathy & Emotional DesignCHI
Simulating Human Audiovisual Search BehaviorLocating a target based on auditory and visual cues—such as finding a car in a crowded parking lot or identifying a speaker in a virtual meeting—requires balancing effort, time, and accuracy under uncertainty. Existing models of audiovisual search often treat perception and action in isolation, overlooking how people adaptively coordinate movement and sensory strategies. We present Sensonaut, a computational model of embodied audiovisual search. The core assumption is that people deploy their body and sensory systems in ways they believe will most efficiently improve their chances of locating a target, trading off time and effort under perceptual constraints. Our model formulates this as a resource-rational decision-making problem under partial observability. We validate the model against newly collected human data, showing that it reproduces both adaptive scaling of search time and effort under task complexity, occlusion, and distraction, and characteristic human errors. Our simulation of human-like resource-rational search informs the design of audiovisual interfaces that minimize search cost and cognitive load.2026HCHyunsung Cho et al.Aalto UniversityEye Tracking & Gaze InteractionSonification & Auditory DisplayAffective Feedback & Emotion Regulation InterfacesCHI