Human-Computer Interaction
MuTable: Composable and Reusable Table Transformations for In-Situ Data Exploration
Fuling Sun, Devamardeep Hayatpur, Jane L. E et al. (+2 more)
Tables are central to data work to support precise lookup and full detail, but they can be limiting for overview and pattern-finding tasks. Visualizations are then created to gain richer perceptual support. In practice, moving between tables and charts often requires maintaining parallel representations, introducing context switching, and extra coordination work. Building on prior hybrid table-visualization systems, we present MuTable, a prototype that reifies transformations as persistent, composable, and reusable modifiers to support in-situ data exploration. Users can reshape the table while retaining and adapting intermediate forms as their questions evolve. An expert interview with eight data workers suggests that MuTable can support coordination between representations, rapid exploration, and greater user agency in constructing visualizations, as a low-commitment exploration space.
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All authors Fuling Sun, Devamardeep Hayatpur, Jane L. E, Nicole Sultanum, Haijun Xia
Human-Computer Interaction
”Bless his heart... he thought all we did was push a button": Understanding Worker Challenges with U.S. Election Technology
Delaney Gomen, Josiah Hester, Naveena Karusala et al. (+1 more)
This paper explores how the technologies U.S. election officials depend on also challenge their work. Every election, misleading narratives stem from errors that occur while using election technology, threatening election official safety and eroding faith in elections. We seek to understand the impact of these challenges directly from worker perspectives. This paper presents a qualitative analysis using combined interview and survey data from a total of 50 election officials representing 20 states. Our findings present design as a substantial factor in mistakes when using election technology, but other obstacles, like lack of funding and technical support, also strain operations. We show how election technology design can lead to harms for election officials and connect those harms to their negative effects on democracy. Our work emphasizes the potential for researchers in human-centered computing to support U.S. election officials-and democracy-by working towards better usability across election technologies.
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All authors Delaney Gomen, Josiah Hester, Naveena Karusala, Michael Specter
Human-Computer Interaction
EarStreAM: A Closed-Loop Earable System for Personalized Stress-Adaptive Meditation
Jonas Hummel, Luisa Faust, Elias Müller et al. (+7 more)
We present EarStreAM, a closed-loop earable system for stress-adaptive meditation that integrates in-ear physiological sensing with personalized, real-time intervention. Leveraging OpenEarable 2.0's
multimodal sensing, EarStreAM continuously monitors physiological signals and detects elevated stress from heart rate and heart rate variability. Upon detection, the system initiates a personalized guided meditation generated by an LLM and adapted in real time to the user's stress state. The demo offers a hands-on experience of stress-adaptive meditation in two modes: a biosignal-adaptive meditation with optional stress induction to illustrate closed-loop adaptation, and a meditation-only mode focusing on EarStreAM's generative personalization capabilities. The demo highlights how in-ear sensing, closed-loop adaptation, and personalized generative meditation can be integrated into an earable system for real-time stress support in demanding office work contexts.
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All authors Jonas Hummel, Luisa Faust, Elias Müller, Eva Bertog, Valeria Zitz, Marius Johannes Prill, Luca L. Bennardo, Luisa Weber, Tobias Röddiger, Michael Beigl
Human-Computer Interaction
Affora: A Design System for Agent-Friendly Interfaces
Jin Gao
Computer-use
agents increasingly operate software designed for people, but interfaces often leave actions or task state unclear to machine readers. We present Affora, a design system that supports both readers while preserving visual freedom and familiar human workflows. Three controlled studies examine component implementations, visual variation, and interaction-design principles. Their findings inform guidance from individual components to complete sites, supported by reusable implementations and executable checks.
Agent performance depends on the interaction meaning available through its interface representation; substantial visual variation remains possible when that meaning is preserved. Evaluation on independently authored interfaces shows gains where Affora addresses existing deficits, but limited effects where those deficits are absent or outside its coverage. A workflow case provides preliminary evidence of reduced interaction cost. Affora connects user experience and
agent experience through a shared interface rather than a separate
agent-only surface.
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Human-Computer Interaction · Cross-listed from cs.RO
Examining the Difference in Human Behavior Between Virtual and Real-World Human-Robot Teaming
Sean Dallas, Absalat Getachew, Motaz AbuHijleh et al. (+4 more)
Prototyping and evaluating human-
robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an alternative to conducting user studies in the real-world to investigate user perceptions, behaviors, and performance during human-
robot interactions. The consistency of human behavior between the real and virtual-worlds is integral to the validity of utilizing such virtual experimentation. This paper presents a user study examining the difference in human behavior between an HRT scenario conducted in a virtual vs real environment. We employed mixed-methods to examine team performance and human factors during the HRT. Our quantitative results showed a significant difference in workload between the two modalities. Our qualitative analysis expanded on the quantitative results and found differences in participants' strategy, their mental model of
robots, and the type of trust they had for
robots between modalities.
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All authors Sean Dallas, Absalat Getachew, Motaz AbuHijleh, Andrea Macklem-Zabel, Douglas Zytko, Mark Brudnak, Wing-Yue Geoffrey Louie
Human-Computer Interaction
Calmables: Demonstrating Closed-Loop Infrared Earables for Thermal Biofeedback and Relaxation Support
Valeria Zitz, Michael Küttner, Jonas Hummel et al. (+3 more)
We present Calmables, a walk-up demo of a closed-loop infrared earable that uses smart-ring heart rate to create subtle, ear-localized warming cues. Building on work on thermal comfort and in-ear infrared stimulation, Calmables explores warmth at the ear as a biosignal-adaptive cue for brief recovery moments following acute activation. A smartphone first establishes an individual resting baseline and derives a personalized heart-rate threshold, while the earable controller independently enforces an over-temperature cut-off and communication fail-safes. During the guided demo flow, attendees complete a brief rapid-breathing activation phase until their heart rate reaches the personalized threshold. This triggers an ear-localized warming cue followed by a short relaxation phase, while physiological changes are displayed on a live dashboard. Attendees can also manually explore different infrared stimulation intensities. To contextualize the demo, we report preliminary placebo-controlled UX ratings from 18 participants: participants rated the active prototype higher on perceived relaxation and perceived recovery support than an identical-looking placebo. Together, the demo illustrates how infrared earables can make physiological feedback tangible through subtle, biosignal-adaptive thermal cues.
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All authors Valeria Zitz, Michael Küttner, Jonas Hummel, Taku Inoue, Tobias Rödiger, Michael Beigl
Human-Computer Interaction
"Okay, I've Actually Softened My Take on This": How People in Decentralized Social Media Reason about the Appropriateness of Generative AI
Romina Mahinpei, Manoel Horta Ribeiro, Andrés Monroy-Hernández et al. (+1 more)
Generative AI (GenAI) is increasingly integrated into social media, raising questions about whether, where, and how it belongs. In decentralized social media (DSM), these decisions are distributed across users, developers, moderators, and administrators, making GenAI a collective governance challenge. At the same time, public discourse often flattens arguments to broad pro- or anti-AI positions that offer little insight into what people actually find (in)appropriate and why. Through 20 semi-structured interviews with people from Mastodon and Bluesky, structured around seven GenAI scenarios, we examine how people reason about GenAI's appropriateness in DSM. We find that participants drew conditional boundaries around particular GenAI configurations through distinct, salient, and weighted considerations spanning technology, integration, and use. We conceptualize this as boundary drawing and show how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.
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All authors Romina Mahinpei, Manoel Horta Ribeiro, Andrés Monroy-Hernández, Sohyeon Hwang
Human-Computer Interaction · Cross-listed from cs.CV
RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection
Liyang Fan, Xinping Bi, Yitai Li et al. (+3 more)
Graphical User Interface (GUI) grounding is a fundamental perception task for
multimodal agents, enabling them to interpret natural
language instructions and interact with digital interfaces. Existing methods face a fundamental trade-off between accuracy and efficiency: direct full-image inference often fails to capture small or visually similar UI elements, while multi-crop strategies improve localization at the cost of multiple expensive
Vision-
Language Model (VLM) calls per query. To address this challenge, we propose RankGround, a two-stage framework that achieves accurate GUI grounding with a single VLM call per query. Central to our approach is GroundRanker, a lightweight
multimodal reranker that identifies the most promising crop from a dense candidate set. Because no off-the-shelf ranking
dataset is available, we construct ranking supervision data from existing grounding
datasets. A strict containment criterion and boundary-aware positive augmentation improve
alignment and spatial coverage in cluttered layouts. GroundRanker is then trained with a two-stage curriculum: a pointwise objective first learns coarse containment, and a listwise objective refines subtle semantic and spatial distinctions among visually similar crops. Experimental results show that RankGround consistently outperforms strong baselines while reducing computational cost. It achieves 1.4 times faster inference and improves localization accuracy by 5.5% on average over the second-best method across all backbones and screen scales, establishing a new state of the art in both efficiency and precision for GUI grounding.
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All authors Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang
Human-Computer Interaction
Integrating Flipped Learning and Generative AI for Practice-Based Design Education: Evidence from a Knit Yarn Design Course
Hong Qu, Zichao Ling and Yadie Yang
In practice-based design courses such as knit yarn design, students must turn visual ideas into feasible material outcomes. This is difficult because creative decisions are tied to yarn properties, stitch structures, machine operation, and limited opportunities for physical sampling. This study presents an integrated pedagogical framework that combines flipped learning, exemplar-based reference, GenAI-assisted visual prototyping, and studio feedback in an undergraduate knit yarn design course. The framework was implemented through a cross-device platform with pre-class micro-
videos, formative checks, a curated gallery, and a GenAI-supported ideation module. An exploratory course-based evaluation compared a historical control cohort (N = 12) and an intervention cohort (N = 16), supplemented by questionnaire responses and brief interviews. The findings are interpreted as context-specific indicators rather than confirmatory causal evidence. Exploratory comparisons showed higher scores in creativity thinking, design skills, problem solving, and total course score in the intervention cohort. Student and instructor responses suggested that flipped learning supported studio readiness, while GenAI mainly supported early-stage visual exploration rather than precise technical guidance. Overall, the study offers a practice-based instructional framework for integrating flipped preparation, GenAI-assisted visual prototyping, and studio feedback in design education.
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Human-Computer Interaction
EasyFashion: A Human-AI Co-Creation System for Personalized Fashion Design and Sewing Pattern Generation
Hong Qu, Zhaoxiang Xu, Jinbo Luo et al. (+3 more)
People often want garments that reflect their aesthetic preferences, fit their bodies, and meet their sizing needs, yet turning these requirements into physical garments remains difficult. Ready-to-wear options provide limited personalization, while custom tailoring is costly and time-consuming. Recent generative artificial intelligence (AI) systems can visualize garment ideas but often stop short of supporting downstream production. To address this gap, we present EasyFashion, a human-AI co-creation system that enables users to iteratively refine design intent for personalized garment style and size, evaluate designs through virtual try-on on reconstructed personal avatars, and generate sewing patterns for garment production. Using reference images, text descriptions, and body photos as input, EasyFashion translates user intent into structured garment specifications and try-on results. Technical experiments, user studies, and a real-world production case demonstrate the value of EasyFashion for
multimodal design expression, body-specific evaluation, and production-oriented outputs in personalized garment design. Our project page is available at https://zoequ.github.io/easyfashion.github.io/.
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All authors Hong Qu, Zhaoxiang Xu, Jinbo Luo, Yujie Zhao, Jie Zhang, Yadie Yang
Human-Computer Interaction
Verify, Offload, Extend & Recommend: Selective Complementarity in AI Support for Physical Activity Planning with Longitudinal Patient Data
Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco et al. (+5 more)
Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.
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All authors Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco, Gil Batista Rosa, Rita Pinto, Albrecht Schmidt, Tiago Guerreiro, Jan David Smeddinck
Human-Computer Interaction · Cross-listed from cs.RO
Hardware-Free Robotics Laboratories in Mixed Reality
Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev et al. (+2 more)
Teaching robotics relies on screen-based simulation, showing
robot motion in an abstract coordinate frame rather than at real scale in the learner's own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays MATLAB-generated
robot trajectories at real scale within the learner's physical environment. A browser-based service validates a MATLAB workspace file (.mat), normalizes units, and publishes a versioned JSON trajectory; a Unity application on a Meta Quest 3 then reproduces the authored joint configurations under position control and replays them at the declared frame rate within a physics-enabled scene that supports collision detection and end-effector grasping. A formative single-group evaluation with engineering students found that participants reported low setup effort (M = 4.67 on a 5-point scale) and perceived support for workspace understanding from multi-viewpoint inspection (M = 4.56), and 83% of participants affirmed their willingness to use the platform in an introductory robotics course. The evaluation instrument records only perceived outcomes, without counterbalancing or a learning measure, so no comparative advantage over desktop simulation is claimed. The contribution is a reusable simulation-to-MR trajectory pathway and design guidance for hardware-free
robot visualization in engineering education.
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All authors Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev, Vanesa Metaj, Stefan Wagner
Human-Computer Interaction
Building a Cultural Perspective on Doctor-Patient Conversations
Krithi Shailya, Siddharth D Jaiswal, Ashish Makani et al. (+3 more)
AI-powered medical scribes are increasingly used to transcribe doctor-patient conversations and automate clinical documentation. However, large-scale real-world consultation
datasets are scarce due to the sensitivity of clinical conversations, leading developers to rely on simulated and LLM-generated synthetic consultations. While scalable, these alternatives may fail to capture culturally situated patterns of clinical interaction. We introduce interactional cultural markers, measurable patterns of doctor-patient interaction grounded in cross-cultural clinical communication, and use them to compare real, simulated, and synthetic consultations from Indian and US clinical contexts. We find distinct patterns of participation and control: Indian consultations involve greater patient participation but stronger doctor control, while US consultations exhibit balanced participation and open-ended discussion. Synthetic Indian consultations often fail to reproduce these patterns, instead converging toward US-like interaction. We identify additional synthetic signatures, including excessive doctor explanation and formulaic patient responses. We conclude by discussing implications for generating culturally grounded synthetic clinical conversations.
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All authors Krithi Shailya, Siddharth D Jaiswal, Ashish Makani, Suvrankar Datta, Sunayana Sitaram, Mohit Jain
Human-Computer Interaction
GYROval: A Robust Benchmark for Cultural Value Orientation in Large Language Models
Alexander Didenko, Anna Shabanova, Vladislav Zapylikhin et al. (+2 more)
We present a robust
benchmark for measuring cultural value orientation in large
language models on the two Inglehart-Welzel axes over several domains and roles (hence GYROval - Gridded Yielding of Robust value Orientation), together with the results of administering it to twenty models. Items are binary contrastive scenarios in the sense introduced by CDEval: both options are legitimate courses of action, neither is correct, there is no answer key, and a model's score on an axis is the proportion of its responses falling on the counted pole. Eleven of the twenty models were additionally administered a paired Russian translation of the identical items and a second sampling temperature. The instrument is publicly released in both
languages. Stability was assessed by treating the vignette as the unit of analysis, ranking the models within the levels of each perturbation factor, and summarising the agreement between levels by tie-corrected Kendall's W against an empirical permutation null.
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All authors Alexander Didenko, Anna Shabanova, Vladislav Zapylikhin, Alexander Antipov, Ruslana Raemgulova
Human-Computer Interaction
SmartFlex: An Adaptive Lumbar Support System Based on Posture Recognition and Air Bag Array
Ben Xiaolu Huang
Low back pain (LBP) is a leading cause of disability worldwide and affects populations ranging from working adults to students with prolonged sitting habits. Conventional lumbar support belts are generally static and non-adaptive, which limits their ability to accommodate dynamic postural changes and individualized comfort requirements. This paper presents SmartFlex, an intelligent wearable lumbar support system that integrates real-time posture recognition with an adaptive air bag array. The system uses a JY901S gyroscope sensor to detect user posture and a lightweight TinyML neural network deployed on an Arduino R4 UNO to process posture data at the edge. Based on the recognized posture state, a closed-loop pneumatic control system dynamically inflates or deflates 14 distributed air bags through four independent micro air pumps to provide targeted biomechanical support. Evaluation results show that SmartFlex achieves over 94% posture recognition accuracy and generates corresponding pressure-control commands with a sensing-to-command delay of less than 120 ms. The pneumatic system operates within a calibrated pressure range of 15-85 kPa. A user study with 20 participants produced a 4.5/5 rating for support effectiveness, suggesting that adaptive wearable support may improve daily sitting comfort and reduce lumbar fatigue.
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Human-Computer Interaction · Cross-listed from cs.CL
Beyond Accuracy: How Procedural Traces Shift the Decision Criterion of LLM Overseers
Zihan Chen, Di Zhu, Lei Zheng et al. (+1 more)
Organizations increasingly use oversight loops where one large
language model (LLM) audits another's outputs alongside procedural traces of claimed steps. A common concern about such LLM-as-a-judge pipelines is that detailed traces make overseers gullible. Using signal detection theory, we audit five LLM overseers on 19 compliance tasks (4,551 analyzed judgments), varying only trace detail and evidence labeling. With disconfirming evidence always visible, error detection remains near ceiling. Instead, elaborate traces shift the decision criterion toward rejection, increasing false alarms in susceptible overseers. Without option labels, human-validated reason coding shows about 60% of false alarms cite an inability to tie evidence to its option. Labels eliminate this stated reason, yet residual rejection of correct work persists in those overseers and rises with trace detail. Procedural traces thus act as governance artifacts that shape oversight decisions. AI auditors should be evaluated by their decision criterion and false-alarm behavior, alongside accuracy.
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All authors Zihan Chen, Di Zhu, Lei Zheng, Weiling Li
Human-Computer Interaction
Misgendering as Breakdown in Human-Machine Communication: How AI Companion Chatbot Users Experience and Repair Misgendering
Julia Liu, Qing Xiao, Leona Yinglang Pang et al. (+3 more)
In recent years, large
language model-based AI companion and role play chatbots have grown increasingly popular. People turn to these chatbots for emotional support and to engage in romantic and erotic role play. Although prior research suggests that digital role play can help people explore their gender and sexuality, LLM based technologies are also replete with gender and sexuality biases. In this study, we examine one way that AI chatbots can harm users: misgendering. In order to study chatbot misgendering we qualitatively analyzed 326 posts mentioning misgendering that were shared in AI companion or role play subreddits. We document how chatbot misgendering takes place and how, in response, users engage in ongoing work to curate their gender presentation to prevent and repair misgendering. We discuss how researchers and designers can mitigate chatbot misgendering and consider the implications of using AI chatbots for identity exploration.
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All authors Julia Liu, Qing Xiao, Leona Yinglang Pang, Haiyi Zhu, Hong Shen, Jordan Taylor
Human-Computer Interaction
Replication Studies: Not Just a Copy
Yiheng Liang, Kim Marriott and Helen C. Purchase
Replication studies revisit previous experimental findings for multiple purposes, including assessing reliability, exploring generalisability, and evaluating research methods. Such studies entail design decisions that a single replication label cannot fully capture, making their designs difficult to describe and compare. We present REPVIS2, a validated design space that describes how a replication study relates to a reference study across eight practical dimensions, each coded as identical, similar, or different. We refined our initial design space, REPVIS1, by applying it to a corpus of replication studies and validated REPVIS2 against a separate corpus. We then characterised 86 replication studies from 51 papers in visualisation through paired reading of the replication and reference reports. Studies often retained the task while changing the procedure, interface, environment, participant population, evidence, or analysis. Additions beyond the replication core were also common. REPVIS2 makes replication design explicit for characterisation, reporting, and planning. An interactive visualisation and supplementary materials are available at https://replication-study.github.io/.
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Human-Computer Interaction
Needs Your Help: Understanding Platform-Directed Rating Participation in Community Notes on X
Shuning Zhang, Changxi Wen, Jiuchang Wang et al. (+4 more)
Community-based fact-checking is promising in countering misinformation, yet its scalability is constrained by slow rating accumulation. To address this challenge, platforms such as X implement platform-directed rating mechanisms, specifically through “Needs Your Help” algorithmic
prompts, to target unresolved notes. Using a
dataset of over 220 million rating contributions – including 1.9 million platform-directed ratings – on X, we examine contributors' response to note
prompts, notes' resolution, and raters' spillovers. We found (i) at note level, population-sampled ratings concentrate on recent notes with certain helpfulness and high disagreement. Once sampled, population-sampled rating was associated with faster and more transitions to resolved statuses. (ii) At rater level, following raters' first observed population-sampled rating, raters exhibit significant yet modest increases in daily ratings, rating pace and tag usage, while other behaviors show no change. These highlight the promise of algorithmic nudges to guide volunteer
attention toward contested content, accelerating consensus while sustaining rater engagement.
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All authors Shuning Zhang, Changxi Wen, Jiuchang Wang, Dai Shi, Gabriele Lenzini, Yuwei Chuai, Xin Yi
Human-Computer Interaction
A Design Space for Visual Interfaces for Generative Image Models
Susie S.Y. Li, Mingwei S.G. Li and Remco Chang
Interactive visual interfaces have become an important means of controlling generative image models, enabling users to manipulate generation through
prompts, direct manipulation, and a range of interactions. However, existing techniques are typically presented as independent systems, making it difficult to understand how they relate, compare their interaction mechanisms, or identify opportunities for new interface designs. We introduce a design space for interactive visual interfaces for generative image models derived from 51 research systems and practitioner tools. The framework decomposes each system into three complementary components: the user interface (U), the controllable model objects (Z), and the mapping function ($\phi$) that translates user interaction into model operations. This decomposition provides a common representation for analyzing heterogeneous interaction techniques across model families, revealing recurring design patterns and underexplored regions of the design space. We further present an interactive corpus explorer that support comparative analysis, and discuss usage scenarios for both educational settings and HCI/AI practitioners identifying research and design opportunities.
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Human-Computer Interaction
Encypher: Shared Agency and Social Presence in Collaborative Music Generation for Dance Cyphers
Zhixing Chen and Cheng-Zhi Anna Huang
Music and dance are social practices of expression and connection, yet most HCI work in human-AI co-creation centers the solo performer. As generative music matures, we ask not only what AI can compose but what social encounters it can organize around sound. We present Encypher, a collaborative generative music system that translates collective movement qualities into text
prompts conditioning real-time music generation for dance cyphers. Through five weeks of co-design with local dancers, a user study with unacquainted participants, a public museum event, and a live performance, we found that users developed shared agency, perceiving the music as a response to the room's energy. While newcomers felt uncertain, the system fostered social presence by prompting them to look to each other for cues. By treating sociality as a design concern rather than a downstream effect, we offer a framework and design implications for AI systems for collaborative, embodied expression.
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Human-Computer Interaction
AI Peers Exert Social Influence on Human Dishonesty in Groups
Shuning Zhang, Xinyuan Zhou, Yuanyang Qiu et al. (+4 more)
Human dishonesty in group settings is highly susceptible to peer influence, particularly when incentivized. Although artificial intelligence (AI) evolves from passive tools into active collaborators, its impact on human moral behavior within groups remains underexplored. We addressed this gap through a two-phase randomized behavioral study (N=280 and N=360). We found AI
agents exert substantial social influence comparable in magnitude to that of human peers. Specifically, participants reported more dishonestly when exposed to dishonest rather than honest normative cues. This effect is evident across injunctive, subjective, and descriptive social norms. Interestingly, the only significant adjacent behavioral change occurred when dishonest peer behavior first appeared, whereas further increases from one to four dishonest peers produced weaker and non-monotonic changes. Furthermore, participants rapidly converge on decision-making, showing modest increases in dishonest reporting through repeated exposure. These findings highlight the importance of managing the behaviors and normative signals communicated by AI group members.
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All authors Shuning Zhang, Xinyuan Zhou, Yuanyang Qiu, Tianqi Song, Yuting Yang, Yiwen Ren, Xin Yi
Human-Computer Interaction · Cross-listed from cs.CL
Gaze as Evidence for Common Grounding: A Cross-Corpus Analysis of MapTask and MUNDEX
Nan Li, Albert Gatt and Massimo Poesio
In collaborative tasks with asymmetric information, participants coordinate their understanding through interaction. We ask whether gaze provides evidence about grounding across two such tasks. Working from discrete behavioral annotations, we map HCRC MapTask (Anderson et al., 1991) and MUNDEX (Türk et al., 2023) into a shared partner/task/away vocabulary and compute gaze features around task-relevant dialogue units. In both corpora, aligned reference interpretations (MapTask) and UND (understood) judgments (MUNDEX) are associated with more task-directed gaze and with less partner-directed gaze, lower gaze
entropy, and fewer gaze transitions. The associations are clearest for the participant leading the task: in giver-produced references, and in explainer judgments, which also co-vary with the explainee's gaze. In same-speaker MapTask reference chains, the speaker's gaze
entropy is lower at the mention where a previously non-aligned referent becomes aligned. The best gaze feature groups improve modestly over controls under grouped cross-validation: temporal features in MapTask and raw proportions in MUNDEX. Because effects are small and several weaken when recurring participants rather than dialogues are the unit of inference, we treat gaze as one contributing cue to grounding, to be interpreted alongside task and dialogue context.
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Human-Computer Interaction · Cross-listed from cs.AI
Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations
Mustafa Akben and Leslie Coyne
AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to study leadership in AI-enabled work. We develop the AI Leadership Battery, which organizes 36 behaviorally specific subdimensions into 11 theory-specified content families. Following established scale-development procedures, the research used deductive item generation; content validation of definitional correspondence and definitional distinctiveness; exploratory factor analysis and item reduction; confirmatory factor analysis in independent samples; and tests of internal consistency reliability, convergent validity, discriminant validity, and criterion-related and incremental validity. Across the development and validation studies, the analyses provided evidence for the Battery's content, multidimensional structure, reliability, and distinction from selected orbiting constructs. The Battery also contributed additional information beyond orbiting constructs across organizational growth, decision speed, customer/stakeholder response capability, AI-enabled team performance, AI-enabled work experience, AI security and risk management, and AI adoption and integration. The resulting measure provides researchers with a behavioral framework for examining how leaders in AI-enabled work regulate judgment, learning, adaptation, transparency, and accountability.
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Human-Computer Interaction
A11yLTLNav: Automatic Detection of Accessibility Navigation Failures
Chenming Ge, Kewen Peng, Chengyang Shi et al. (+2 more)
For blind and low-
vision (BLV) screen-reader users, a website that appears accessible in a static snapshot can become difficult or impossible to navigate once interaction begins. Yet, most automated accessibility checkers miss failures involving focus, interface state, and accessible feedback across interactions. We present A11yLTLNav, a property-based approach for automatically detecting accessibility navigation failures. Through a structured review of prior research, we organize accessibility navigation failures into a failure taxonomy and formalize a browser-observable subset as executable Linear Temporal Logic properties over action-state traces. A11yLTLNav combines random keyboard exploration with runtime property monitoring to detect these failures during interactions. We evaluate A11yLTLNav on 31 generated websites based on real-world websites and tasks. It reported 309 accessibility failures, of which 274 were confirmed, achieving 88.7% precision and identifying more confirmed failures than the comparison checkers. Our results show that A11yLTLNav transforms accessibility knowledge into reusable checks of interface behavior over time.
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All authors Chenming Ge, Kewen Peng, Chengyang Shi, Ben Greenman, Yue Jiang
Human-Computer Interaction
Apply-<x>Mag: One Tool to Support Many Inclusive Design Methods
Sadia Afroz, Rudrajit Choudhuri, Fatima A. Moussaoui et al. (+3 more)
Doing inclusive design in HCI practice can be labor-intensive, a costly barrier that some companies and HCI practitioners may be unwilling or unable to overcome. Yet, not doing inclusive design is costly too, in the form of UX barriers that disproportionately disadvantage under-served user populations. To address this problem, we introduce Apply-<x>Mag, an LLM-powered tool to support HCI practitioners' work to design their products inclusively to wide ranges of users. Apply-<x>Mag is general, supporting any inclusive design method that can be expressed as <x>Mags (i.e., using attribute ranges and heuristics). It is also effective: Empirical results with researcher and practitioner teams using various combinations of two <x>Mags on 7 products showed Apply-<x>Mag precision averaging 90-99% and recall averaging 82-89%. Further, its environmental costs were reasonable, costing about the same resources as 2-4 ordinary Google searches.
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All authors Sadia Afroz, Rudrajit Choudhuri, Fatima A. Moussaoui, Amreeta Chatterjee, Margaret Burnett, Anita Sarma