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Computer Science Human-Computer Interaction

cs.HC Watch Human-Computer Interaction

Thursday 20 August

Human-Computer Interaction

Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness

Nini Kurashvili, Yana Ivanchenko, Greta Schiavo et al. (+2 more)

Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support. However, in some contexts, AI-generated information may not be suitable for direct delivery to the final recipient. Instead, it may need to be interpreted, adapted, and communicated by a human who understands the recipient's needs, emotional state, and situational context. Human-AI interaction research has given less attention to situations in which a more knowledgeable human acts as an intermediary between an AI system and a less experienced or less informed recipient. We introduce the human-mediated AI guidance framework and explore it through Ready Together, an AI-supported family emergency preparedness system in which parents mediate AI-generated content for their children. The system is designed to provide personalized guidance and support parents in making emergency preparedness more interactive and understandable through guided activities and family-centered learning. The system design was informed by a qualitative, design-oriented research process involving semi-structured interviews and co-design activities. Findings identified challenges in family emergency preparedness, including difficulty discussing emergencies with children, uncertainty about providing appropriate explanations, and a preference for interactive learning activities. These findings informed the design of an interactive prototype, subsequently evaluated through a pilot study and a heuristic evaluation. Participants responded positively to the personalized recommendations and practical activities. Preliminary findings suggest that human-mediated AI guidance may support context-sensitive family preparedness while preserving parents' responsibility for interpreting, adapting, and communicating AI-generated information.
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All authors Nini Kurashvili, Yana Ivanchenko, Greta Schiavo, Cansu Koyuturk, Dimitri Ognibene

Human-Computer Interaction

Evaluating Smart Home Device User Responses to their (Un)Confirmed Privacy Expectations

Tania Khatun, Mahdieh Sheikh Rezaei, Danny Yuxing Huang et al. (+2 more)

Users of smart home devices are often unaware of how their devices handle personal data. We examine how revealing these data practices influences user trust, satisfaction, and coping behaviors, including decisions to block device communications. Using Expectation-Confirmation Theory, we conducted two complementary studies to balance ecological validity with experimental control. An in-situ field study used network monitoring to reveal actual device traffic, and an online experiment presented simulated reports with manipulated levels of advertising-related communications. Across both studies, when data practices aligned with user expectations, satisfaction increased, strengthening intentions to continue using the device. Defensive responses, however, followed different pathways: satisfaction predicted willingness to block in the in-situ field study, whereas collection concerns were the primary predictor of blocking in the experiment. Together, these findings show how transparency reshapes attitudes and behaviors among existing smart-home users, underscoring the role of expectation confirmation in real-world, continued-use contexts.
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All authors Tania Khatun, Mahdieh Sheikh Rezaei, Danny Yuxing Huang, Oded Nov, Reza Ghaiumy Anaraky

Human-Computer Interaction

Dancing Through Soundscapes: Designing a Low-Cost, Sound-Based Device for Sensing and Interpreting Movement and Dance

Swen E. Gaudl and Silvia Carderelli-Gronau

When we move through space, we often rely on multiple senses beyond vision to perceive and act in that environment: we “feel” the presence of others; we build internal representations and models and recall them to navigate the environment. We also leave traces and impressions that others pick up on. The traces include echoes, heat, the displacement of objects such as furniture or footprints, air movement close to the face of another, smells such as perfume, but also the immediate sounds we make when we move and breathe. Movement is a spatial and temporal activity, and dance as a form of movement practice requires coordination of oneself in relation to others, the space and a potential score. When rehearsing dance, dancers have to relate to others often not just by looking but more often by feeling and imagining or remembering where others are based on experience and shared practice. So how can we approach technology-mediated movement and dance? Why should we explore it? How can we support spatial and temporal practice meaningfully and joyfully? In this work, we focus on sound traces; we present the design and rationale for a sound-based artefact that translates movement-based sound into layered, explorable, generative soundscapes. The work contributes a novel artefact for exploring movement-based activities with a audio-first approach, with a focus on the spatial performative experience. The paper further reflects on observations from workshops and public sharings, including how participants used repetition, stillness, environmental sound, and call-and-response to understand and improvise with the soundscape.
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Human-Computer Interaction

Calming Robot Pitches? Exploring the Influence of Robot Voice Pitch on Children's Stress Levels

Nina G. M. van Roij, Emilia I. Barakova, Briana Isaila et al. (+1 more)

This study examined whether variations in robot speech pitch influence children's stress levels during a robot-guided game. Although lower-pitched voices have been shown to facilitate stress regulation in human communication, it remains unclear whether this effect generalizes to synthetic voices in child-robot interactions. Twenty-seven Dutch children aged 8-12 years were randomly assigned to interact with a Zenbo Junior II robot using either a lower-pitched or a higher-pitched voice. The interaction consisted of an introduction followed by a timed LEGO-building game. Stress levels, measured with an adapted version of CAM-S, increased during the game, confirming the stress-inducing nature of the task. No differences emerged between pitch conditions. These findings suggest that the benefits of lower pitch in reducing stress may not directly translate to child-robot interactions. Possible explanations include children's developing sensitivity to emotional tone, mismatches between the robot's voice and appearance, or the use of fixed pitch changes that sound unnatural, since real speech varies dynamically across multiple dimensions. Future research examining combinations of prosodic cues (beyond pitch alone) could provide further insights and help inform robot voice design for effective stress regulation support for children.
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All authors Nina G. M. van Roij, Emilia I. Barakova, Briana Isaila, Aoju Chen

Human-Computer Interaction

Grounding Mindfulness in Embodied Tangibles: A Scoping Review & Theoretical Framework for HCI Design

Tharaka Sachintha Ratnayake, Samangi Wadinambiarachchi, Sarah Schömbs et al. (+3 more)

Embodied and tangible devices are increasingly used to support mindfulness practices across meditation, yoga, and everyday routines. However, existing HCI research lacks a coherent theoretical foundation for explaining how such systems support distinct mindfulness processes and outcomes. First, we report findings from a scoping review of tangible devices (n=65) for mindfulness in HCI based on the mechanisms of action of mindfulness. The review found that most systems primarily target attentional regulation and body awareness, while emotion regulation and change in perspective on the self remain comparatively underexplored. Also, the evaluation methods used to assess the effectiveness of tangible systems for mindfulness-related outcomes were found to be fragmented and weakly grounded in theory. Building on these findings, a theoretical framework grounded in the Self-Awareness, Self-Regulation, and Self-Transcendence (S-ART) framework is proposed to explain how tangible systems support different forms of mindfulness practice. Within the framework, we present two complementary models: (1) Embodied Sensory Expansion (ESE) for focused-attention meditation (FAM) and (2) Embodied Sensory Anchoring (ESA) for open-monitoring meditation (OMM). Together, we show how this framework can provide a principled basis for explaining designs, generating hypotheses, and evaluating tangible mindfulness technologies in HCI, while highlighting key gaps for future research.
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All authors Tharaka Sachintha Ratnayake, Samangi Wadinambiarachchi, Sarah Schömbs, Jonathan Eden, Denny Oetomo, Wafa Johal

Human-Computer Interaction

IRIS: Navigating and Reflecting on Writing Traces Using Intelligent Document Histories

David Zhou, Andrew Chen, John Joon Young Chung et al. (+1 more)

Much of the text produced throughout the lifetime of a document is impermanent. In this paper, we explore how writing activity traces can be made visible and interactive to help writers navigate their document histories and understand their writing processes. Using the Flower and Hayes cognitive process model of writing, IRIS infers writing process states from keystroke logs and presents them using an AI-enhanced version history. IRIS provides three primary interactions: revision highlighting that shows local process histories in-situ, conceptual filters that constrain the version history by process type or topic, and natural language inquiry that lets writers pose reflective questions about their writing and process. Following a formative and a longitudinal study, we find that writers use the interfaces to locate specific revisions and understand the progression of their writing. They use system outputs as interpretive material, relating them to pre-existing beliefs and confirming, challenging, and deepening their understanding of their writing.
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All authors David Zhou, Andrew Chen, John Joon Young Chung, Sarah Sterman

Human-Computer Interaction

Localized Ecological Momentary Assessment for Mental Health Research in China: An Implementation-Oriented Framework and Preliminary Case Application

Xinying Zhao, Yue Li, Jiafeng Wang et al. (+5 more)

Background: Ecological momentary assessment (EMA) is increasingly used in mental health research, but research-grade deployment requires platforms supporting protocol configuration, automated delivery, participant management, and data export. In China, these requirements are not consistently supported. Objective: We aimed to identify workflow gaps affecting localized EMA deployment, develop an implementation-oriented framework for platform assessment, and assess Huixin EMAI. Methods: We reviewed EMA platforms reported in Chinese mental health studies in CNKI and Wanfang. A multidisciplinary panel of 6 experts developed the Multi-dimensional EMA Platform Evaluation Framework (MEPEF) and benchmarked 7 platforms across 43 indicators in 6 domains. MEPEF was then applied to Huixin EMAI using deployment logs from 48 participants, questionnaires from 44 participants, and semistructured interviews with 6 researchers. Results: We identified 66 empirical studies. Most relied on instant-messaging-based workflows (36/66, 54.5%), whereas specialized EMA platforms were less common (14/66, 21.2%). MEPEF provided a 6-domain framework for cross-platform benchmarking and highlighted a trade-off between localized deployability and advanced research functions. In a Huixin EMAI deployment, 1893 of 2472 expected prompts were completed (76.6%), with a median response latency of 4.0 minutes (Q1-Q3 0.0-13.0). Participant feedback indicated favorable acceptability; researchers reported support for core workflows but gaps in control, delivery monitoring, and data readiness. Conclusions: The main challenge for EMA in Chinese mental health research appears to lie less in feasibility than in recurring workflow gaps affecting localized deployment. This study translates these gaps into structured evaluation and design targets, providing an implementation-oriented pathway for advancing localized EMA platforms.
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All authors Xinying Zhao, Yue Li, Jiafeng Wang, Yunfan Fu, Ruilin Guo, Chen Yang, Cheng Yao, Wei Deng

Human-Computer Interaction

Delegating or Doing? Understanding User Behavior in Hybrid Human-Agent Interfaces

Gavin Dizon, Tyrone Justin Sta Maria, Jordan Aiko Deja et al. (+1 more)

Large Language Models (LLMs) are increasingly embedded into applications, allowing users to complete tasks either through direct manipulation or by delegating actions to conversational agents. However, little is known about how users balance these modalities when both are available. We present a web-based content management system augmented with an LLM agent through the Model Context Protocol (MCP), enabling users to perform CRUD tasks through a graphical interface, a conversational agent, or both. We conducted a between-subjects study (N=73) comparing three interaction modes: Traditional-Only, AI-First, and Hybrid. Across sixteen scenarios, we analyzed task completion time, interaction logs, and delegation behavior. AI-assisted interaction significantly reduced clicks, page navigations, and scrolling indicating lower interaction effort. Surprisingly, these reductions did not translate into faster task completion, as task duration did not differ significantly across conditions. We also found no significant relationship between CRUD operation type and delegation, suggesting that users did not systematically avoid delegating higher-risk actions. Instead, delegation varied far more between participants than between tasks, with individual differences accounting for roughly half the variance in assistant use (ICC = .50). Our findings suggest that the primary benefit of human–agent interfaces may be reducing interaction effort rather than improving speed, and that delegation reflects who the user is more than what the task demands.
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All authors Gavin Dizon, Tyrone Justin Sta Maria, Jordan Aiko Deja, Yasuyuki Sumi

Human-Computer Interaction · Cross-listed from cs.CL

Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems

Mikio Nakano, Kazunori Komatani and Hironori Takeuchi

This paper addresses the issue of the significant labor required to test interview dialogue systems. While interview dialogue systems are expected to be useful in various scenarios, like other dialogue systems, testing them with human users requires significant effort and cost. Therefore, testing with user simulators can be beneficial. Since most conventional user simulators have been primarily designed for training task-oriented dialogue systems, little attention has been paid to the personas of the simulated users. During development, testing interview dialogue systems requires simulating a wide range of user behaviors, but manually creating a large number of personas is labor-intensive. We propose a method that automatically generates personas for user simulators using a large language model. Furthermore, by assigning personality traits related to communication styles when generating personas, we aim to increase the diversity of communication styles in the user simulator. Experimental results show that the proposed method enables the user simulator to generate utterances with greater variation.
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Human-Computer Interaction

Does Listening Matter? Backchanneling and Nodding in AI Clone

Koji Inoue, Kazushi Kato, Tatsuya Kawahara et al. (+1 more)

AI clones that imitate a specific person typically reproduce what the person says and how they sound, but not how they listen. We investigate whether adding multimodal listening behaviors gives such a clone more presence and authenticity. We integrated verbal backchannels and head nodding, driven by real-time prediction models, into an AI clone equipped with voice cloning and LLM-based responses. In a within-subjects study (N=35), adding these behaviors significantly improved the perceived attentiveness of the avatar, the sense of talking with the real person, and the feeling of co-presence. These results indicate that AI clone fidelity should extend beyond voice and response content to include interactive listening behavior.
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All authors Koji Inoue, Kazushi Kato, Tatsuya Kawahara, Shunichi Kasahara

Wednesday 19 August

Human-Computer Interaction · Cross-listed from cs.AI

Agentic AI for Safety-critical Multi-drone Systems: Challenges and Opportunities

Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al. (+2 more)

Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue (SAR) and critical infrastructure monitoring. Yet, real-world adoption remains constrained not only by autonomy performance, but by the difficulty of integrating agentic behavior into professional work: operators must understand, trust, and govern automation under uncertainty, time pressure, and accountability. This position paper synthesizes the ambitions and lessons from two ongoing efforts: NAMUR, which explores LLM-supported robot control in SAR and firefighting contexts, and PERSIST, which explores persistent drone operations for monitoring and security at critical infrastructure sites. We argue that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms. We outline a human-centered, participatory, and iterative research approach aimed at uncovering stakeholder needs, shaping agent capabilities through successive prototypes, and producing transferable proof-of-concept systems and evaluation strategies for other safety-critical contexts.
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All authors Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal, Maria-Theresa Bahodi, Anders Lyhne Christensen

Human-Computer Interaction · Cross-listed from cs.SI

Social.Wiki: A Web Held in Common

Theia Henderson, Carmel Schare, Ana Dodik et al. (+4 more)

Many of the websites people depend on have owners whose interests are not fully aligned with their users. We address the root of this problem by presenting a reimagining of the web where sites are not owned at all but are instead collaboratively produced like Wikipedia articles. We call the system Social$.$Wiki because it supports the co-creation of interactive social sites, such as those for microblogging, messaging, dating, gaming, ride sharing, and so on. With off-the-shelf AI tools, people with little or no programming experience can edit these sites to better reflect the needs and preferences of their communities. Social$.$Wiki builds on ideas from collaborative malleable software systems such as Webstrates, but is designed for public participation rather than use only within small, trusted groups. To this end, Social$.$Wiki includes governance to mitigate conflict. To accommodate diverse governance preferences, our model of "plural governance" lets people independently choose the policies that determine which edits to a site they see. Social$.$Wiki also implements a granular security model to protect personal data in a malleable environment. Complementing the decentralized design and governance of Social$.$Wiki sites, both site edits and within-site data are stored on Graffiti, a decentralized infrastructure, decoupling the ownership of underlying servers from the ownership of sites. We evaluate Social$.$Wiki through case studies that demonstrate the range of sociotechnical structures it supports, as well as through deployments at a hackathon and in the wild.
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All authors Theia Henderson, Carmel Schare, Ana Dodik, Clemens N. Klokmose, Ziv Epstein, David D. Clark, David R. Karger

Human-Computer Interaction

Cyber-Physical Systems for Accessibility and Ability Augmentation: Bridging Diverse Communities

Shuchang Xu, Riku Arakawa, Mina Huh et al. (+11 more)

The powerful convergence of wearables, robotics, extended reality, and smart environments is expanding the design space for cyber-physical systems (CPS) that support and augment human abilities in daily life. By sensing real-world contexts, modeling user needs, and providing situated assistance, these systems can improve accessibility for people with disabilities while enhancing broader human abilities such as perception, memory, learning, and mobility. However, realizing this potential requires addressing key challenges in context sensing, user modeling, adaptive interaction, privacy, and evaluation to ensure that CPS are reliable and effective in real-world contexts. This workshop will bring together researchers and practitioners across HCI, AI, wearables, robotics, XR, smart environments, accessibility, and ability augmentation to examine shared strategies and challenges for designing accessibility- and ability-centered CPS. Through panel discussions, interactive demos, and mixed-group design activities, participants will identify recurring design principles, technical challenges, and future directions for CPS that support and augment human abilities in real-world settings. For details, please visit: https://cps4all.github.io.
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All authors Shuchang Xu, Riku Arakawa, Mina Huh, Nandi Zhang, Tianyu Zhang, Wazeer Zulfikar, Ruei-Che Chang, Yotam Sechayk, Huamin Qu, Amy Pavel, Franklin Mingzhe Li, Yukang Yan, Brian A. Smith, Pattie Maes

Human-Computer Interaction

Scientific Visualization as a Collaborative Data Infrastructure

Jasmine Tan Otto, Alexandra Diehl, Max Kreminski et al. (+1 more)

Scientific visualization is an active site of infrastructuring with many layers of data and evidential claims. This paper reflects on collaborative Mars geoscience research conducted at the NASA Jet Propulsion Lab, which produced the PIXLISE spectroscopic analysis platform, through a retrospective analysis of a scientific discovery made by the team using PIXLISE. The history of infrastructuring in scientific visualization is exceedingly rich, which presents an opportunity for archival research. Simultaneously, the field of visualization aspires to become a science of communication, opening the door to future collaborations. Finally, with regard to visualization practitioners, we believe that opportunities for infrastructuring in both science and science communication are actively emerging.
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All authors Jasmine Tan Otto, Alexandra Diehl, Max Kreminski, Scott Davidoff

Human-Computer Interaction · Cross-listed from cs.CY

Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates

Kelly Yuan, Ronald Liu, Daniel Crawford et al. (+1 more)

K-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure for effective K-12 and university collaborations remains underexplored. This paper introduces a multi-tiered mentorship framework enabling high school students to engage in authentic engineering through AI-assisted development using large language models and AI agents, while undergraduate mentors provide architectural oversight. We test this framework through LuckyTag, a privacy-preserving NFC-based lost-and-found system. The model positions high schoolers as product leads, undergraduates as technical architects, and faculty as minimal-intervention advisors. A pilot with four high school students, three undergraduates and two faculty yielded survey data showing high perceived barrier removal and gains in system architecture understanding. Thematic analysis reveals that AI amplifies rather than supplants mentoring demands, requiring human oversight for logic and security. These findings suggest a hybrid model for equitable K-12 and university collaboration on computing integration that emphasizes "AI micromanagement" and architectural reasoning over traditional syntax.
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All authors Kelly Yuan, Ronald Liu, Daniel Crawford, Weihao Qu

Human-Computer Interaction

MultiVerse: A Creator-Centered Approach to Steering Context-Adaptive Lyrics

Alexander Wang, Chris Donahue and David Lindlbauer

Generative AI may enable new forms of context-aware creative expression by dynamically tailoring media content to its consumption context. For instance, AI systems could adapt song lyrics to the listener and their current activity. However, existing media adaptation systems primarily optimize for audience experience, often neglecting artists' intent, style, and preference. We address this challenge by introducing a novel creator-centered approach to adaptive media authoring and present MultiVerse, a system that instantiates this approach for steering adaptive lyrics. Our approach allows creators to explicitly author controls based on their intent, lyric structure, and audience context, and uses rule-based validations to ensure controls are followed. We conducted a study with 10 songwriters, comparing MultiVerse with a prompting-based workflow for composing adaptive lyrics. The comparison revealed that creators preferred to author how lyrics adapt by explicitly specifying relevant context and adaptation constraints, while recognizing trade-offs in flexibility and iteration speed. Interviews further revealed that creators viewed adaptive media as enabling new forms of audience connection, introducing a distinct creative process, favoring new compositional strategies, and reshaping notions of authorship.
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Human-Computer Interaction · Cross-listed from cs.RO

APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs

Bijan Kavousian, Miray Özakkas, Josefine Monnet et al. (+2 more)

Programming robots remains challenging for non-experts, as traditional methods require expert knowledge and even block-based interfaces often lack flexibility. Recent work has explored Large Language Models (LLMs) to automatically generate robot programs from natural language, but these systems remain limited by a lack of transparency, missing mechanisms to ensure alignment with user intent, and little support for reuse. We present APPROVE (AI-Powered Programming for Robots with Visual End-User Feedback), an LLM-based multi-modal end-user programming framework that integrates natural language input with a block-based interface and an explicit user confirmation step. Generated programs are visualized using a block-based interface in Blockly, allowing users to confirm, modify, or reject them before execution. Confirmed functions are stored in a library for reuse, gradually building a set of reliable program components. Our approach contributes a human-centered design for LLM-based robot programming that emphasizes user trust, intent alignment, and reusability.
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All authors Bijan Kavousian, Miray Özakkas, Josefine Monnet, Oliver Petrovic, Christian Brecher

Human-Computer Interaction · Cross-listed from cs.CY

LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University

Weihao Qu, Ling Zheng, Chris Buzaid et al. (+1 more)

As generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience. This experience report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university. The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines. The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection. Over two semesters, 35 clients co-created 36 portfolio websites and over 20 deployed web applications. Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction. A small paired pre/post AI readiness dataset (N = 7) provides preliminary descriptive context, and tutor accounts document how the pedagogical script was enacted and adapted across client types. We report on boundary cases including clients who felt overwhelmed and respondents who deliberately rejected AI use. This paper contributes a practical, adoptable framework with initial evidence from a single institution.
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All authors Weihao Qu, Ling Zheng, Chris Buzaid, Daniel Crawford

Human-Computer Interaction

Trade-offs in Data Color Palette Design Tools

Shiyi He and Andrew M McNutt

Designing a color palette for data requires designers to balance multiple constraints, including accessibility and aesthetics. Color palette tools support this process through features including direct manipulation, automated palette generation and evaluation, previews, and so on. Despite their prominence, relatively little is known about how these different mechanisms shape design across contexts. We conducted an exploratory think-aloud crowd work study with 40 self-identified designers. Each participant used one of four palette tools selected to span different interaction modalities to complete a series of accessibility- and aesthetics-oriented design tasks. We observed two preliminary patterns. First, tool differences were more pronounced in accessibility-constrained tasks. Second, even when accessibility was not explicitly required, some tools produced more accessibility-friendly palettes and prompted more accessibility-oriented thinking. In this tool genre, then, system design shapes outcomes both via built-in functionality, as well as by directing designers' attention toward particular constraints and design considerations.
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Human-Computer Interaction

When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation

Chenchen Mao, Hanjing Shi, Haiyan Jia et al. (+2 more)

Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves. We investigated how source-text condition and output rendering relate to perceived translation quality, and how output and system appraisals relate to trust and stated disclosure willingness in a plain-text interface. A focal 2 * 2 comparison (N=306) using TransLingo examined simple generated narratives and complex literary-philosophical prose alongside LLM-generated readability-oriented outputs and researcher-revised fidelity-oriented outputs. A descriptive stimulus audit indicated greater source retention in fidelity-oriented outputs in both source-text conditions. Factorial analyses showed a significant rendering-by-source-text-condition interaction in perceived quality. Participants rated fidelity-oriented outputs higher than readability-oriented outputs for the simple narratives, whereas no reliable rendering difference emerged for the complex prose. A corresponding source-condition-dependent pattern was observed for perceived intelligence, agency-oriented anthropomorphic attribution, and task-performance trust. A separate theory-ordered appraisal-structure SEM characterized concurrent associations among perceived quality, perceived intelligence, agency-oriented anthropomorphic attribution, task-performance trust, and stated disclosure willingness across six domains, with task-performance trust as the proximal correlate of stated willingness. The observed rating pattern distinguishes source access from source evaluability: for the complex stimuli, displaying the source did not ensure that one overall-quality rating reflected differences in retained content. It also separates support for evaluating translation output from data-handling support for decisions about what personal text to entrust to a system.
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All authors Chenchen Mao, Hanjing Shi, Haiyan Jia, Emily Wegrzyn, Dominic DiFranzo

Human-Computer Interaction · Cross-listed from eess.SP

Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform

Emily Yang, Liyuan Guo, Seyed Mohammad Ali Zeinolabedin et al. (+5 more)

Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55-60% to 70-75% on difficult high-noise datasets and from 90-95% to 95-99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.
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All authors Emily Yang, Liyuan Guo, Seyed Mohammad Ali Zeinolabedin, Meng Zhang, Ke Yang, Matthieu Couriol, Christian Mayr, Pierre-Emmanuel Gaillardon

Human-Computer Interaction

TractorBeam: Personalized AI Sensemaking Support via Collaborative Machine Annotation

Sireesh Gururaja, Jordan Taylor and Emma Strubell

Language model-based systems which allow asking questions of documents have become popular tools for sensemaking. Despite their implied capability, these systems still suffer from issues of factuality and provenance, while encouraging confirmatory, rather than exploratory, research. We present TractorBeam, a browser extension-based mixed-initiative system that uses collaborative annotation as an interface metaphor for sensemaking, re-framing language model (LM) outputs as suggested highlights in a process that we call collaborative machine annotation. This metaphor allows us to present LM results in-context on PDF documents, directly addressing concerns of provenance and factuality, while allowing users to iteratively construct mental schemas and queries for language models directly in the context of a document. In a preliminary user study, all of our participants felt that TractorBeam enabled them evaluate and iteratively improve the model's reflection of their intended highlighting, and several found suggestions that made them reconsider their original schema. TractorBeam suggests that systems that facilitate exploratory research on individual documents may lead to verifiable sensemaking for users and complement tools that work across broader corpora.
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Human-Computer Interaction

A revised framework for the assessment of psychological safety in autonomous vehicles

Yandika Sirgabsou, Benjamin Hardin, François Leblanc et al. (+5 more)

Despite recent technological progress in the development of autonomous vehicles (AVs), their societal acceptability remains a subject of debate as recent research findings point to psychological roadblocks. Concerns arise not only for physical safety but also for potential psychological risks resulting from human interaction with AVs. Psychological concepts such as trust, and perceived safety are well-studied in this context and are found to be determinant factors for the intention to use AVs. Unfortunately, there has been no formalization of the mechanism by which human interaction with AVs may lead to psychological hazards, threatening trust, perceived safety, and acceptability. Furthermore, there has been little prior research that conceptualizes the severity of psychological risk in AVs, and there are no clear guidelines for a systems designer on how to assess and address psychological risk in the AV development context. To address these limitations, this paper extends a theoretical framework for AV psychological safety risk assessment based on an early proposal. The proposed framework consists of an extended risk model for psychological safety including all the key concepts related to psychological safety in AVs, and an assessment method based on the Systems-Theoretic Accident Model and Processes (STAMP). We demonstrate the usefulness of the theoretical framework through a highly automated AV use case scenario, uncovering factors which may lead to psychological risk for an occupant. The use cases provide examples of how to use the framework to extensively evaluate psychological risk and determine vehicle behaviour that could lead to this risk. By developing a theoretical framework for AV psychological safety risk assessment, we provide a foundation and a method that were previously lacking to better enable responsible AV development regarding psychological safety.
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All authors Yandika Sirgabsou, Benjamin Hardin, François Leblanc, Efi Raili, David Jackson, Pericle Salvini, Lars Kunze, Marina Jirotka

Human-Computer Interaction

Scoring and Gamification to Encourage Sustainable Use of Compute Clusters

Maximilian MacDonald, Chris McCaig, Sean MacAvaney et al. (+2 more)

The environmental cost of computing continues to grow, yet behaviour change remains limited. We present a composite sustainability score integrating average carbon intensity, resource utilisation, and embodied emissions into a single 0-100 metric designed for gamified feedback. Each component rewards a different dimension of sustainable behaviour: carbon-aware workload shifting, high resource utilisation, and selecting hardware that is commonly underutilised. This scoring system is built into an existing cluster management interface and underpins three dashboard conditions: raw metrics, composite score, and a gamified tree visualisation, which we are planning to evaluate in a 12-week within-subjects study with approximately 35 researchers. Furthermore, we open the discussion on the challenge of defining computational work `goodness' in the context of sustainability scores.
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All authors Maximilian MacDonald, Chris McCaig, Sean MacAvaney, Matthew Barr, Lauritz Thamsen

Human-Computer Interaction

Engineering Psychological Safety in Autonomous Vehicles: A Systems-Theoretic Framework for Psychological Safety in Autonomous Vehicles and its Validation in Real-World Scenarios

Yandika Sirgabsou, Benjamin Hardin, François Leblanc et al. (+5 more)

Despite rapid technological advances, the societal acceptability of autonomous vehicles (AVs) remains limited by psychological barriers that extend beyond traditional concerns of physical safety. While factors such as trust and perceived safety are known to influence user acceptance, there is a lack of formalized mechanisms and engineering methods to systematically identify, assess, and mitigate psychological risks arising from human-AV interactions. To address this gap, this work proposes and validates a systems-theoretic framework for the assessment of psychological safety in autonomous vehicles. First, a comprehensive psychological safety risk model is defined, extending the Systems-Theoretic Accident Model and Processes (STAMP) to incorporate key psychological constructs such as trust, perceived control, predictability, and perceived support. Based on this model, a hazard analysis method (AV-PsySafe) is developed to systematically identify psychological hazards, unsafe control actions, and loss scenarios, while introducing a Psychological Safety Integrity Level (PsySIL) to support risk prioritization. Second, the applicability and relevance of the framework are evaluated through its deployment in realistic autonomous vehicle scenarios. A structured validation approach is implemented, including a methodological guide, standardized analysis templates, and the collection of analyst feedback. The results demonstrate that the framework can be consistently applied by practitioners, producing meaningful insights into psychological risks. Overall, this work establishes both the theoretical foundations and practical feasibility of a unified approach to co-assessing psychological and physical safety in autonomous systems, contributing to more human-centred and trustworthy AV development.
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All authors Yandika Sirgabsou, Benjamin Hardin, François Leblanc, Efi Raili, David Jackson, Pericle Salvini, Lars Kunze, Marina Jirotka

Human-Computer Interaction

Model Literacy: An Extra Summative Evaluation Factor for Visual Analytics

Lei Xia, Siyu Wu, Haodian Li et al. (+3 more)

Understanding and enhancing visual analytics (VA) performance is important for maximizing their impact. Existing studies have successfully applied well-established summative evaluation methods from information visualization to the VA context, yet the recent emphasis on an extra data analysis/modeling stage in the VA pipeline poses an additional challenge. Inspired by the modern concept of visualization literacy, this paper examines model literacy, namely users' knowledge of the analysis model used in a VA technique, as an additional factor for VA performance. Results from a controlled study on the visual analysis of multidimensional data with two dimensionality-reduction models indicate a positive correlation between model-task accuracy and VA-task accuracy. The study involves two common dimensionality-reduction models, PCA and t-SNE. The correlation is stronger for PCA than for t-SNE in the current task design, a pattern consistent with the possibility that VA effectiveness is more closely associated with model literacy when model outputs are less directly readable from the visualization. Completion-time evidence does not show a stable efficiency gain, suggesting that differences in model intuitiveness may help explain when model knowledge shortens task completion and when it involves additional interpretive effort. The findings of this study suggest ways to further enrich VA evaluation methods and provide directions for developing more rigorous model-literacy assessment instruments.
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All authors Lei Xia, Siyu Wu, Haodian Li, Ye Sun, Liang Zhou, Lei Shi

Human-Computer Interaction

Sounds Uncertain: Exploring the Affective Aspects of Sonification for Uncertainty Visualization

Marcel-Simon Dutt, Sita A. Vriend, Elias Elmquist et al. (+1 more)

Affective visualization can influence how users perceive, interpret, and engage with data by embedding and conveying emotion through visual design. While sound is widely used in media to evoke emotions, little is known about how sonification can support affective visualization. In this work, we investigate how sonification can communicate emotion in uncertainty visualizations through a co-design study. Participants created two sonifications to accompany a visualization: one conveying the affective component of uncertainty and one conveying neutrality. Our findings show that uncertainty was commonly associated with wavy auditory qualities related to an ominous sentiment. On the other hand, neutrality was associated with clear and relaxing auditory qualities. These results provide insights for the design of visualizations that integrate sonification to communicate the affective component of uncertainty.
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All authors Marcel-Simon Dutt, Sita A. Vriend, Elias Elmquist, Daniel Weiskopf

Human-Computer Interaction

Report on The 1st Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access at CHIIR 2026

Kirandeep Kaur, Vinayak Gupta, Tanya Roosta et al. (+3 more)

Interactive information access is increasingly moving beyond reactive query-response paradigms toward agentic systems that can personalize interaction, retain context, infer latent needs, recommend next steps, and initiate support. This shift creates new opportunities for adaptive and context-aware assistance, while also raising important questions about autonomy, privacy, trust, transparency, user welfare, and evaluation. The First Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access provided an interdisciplinary forum for examining these questions across information retrieval, human-computer interaction, dialogue systems, AI ethics, cognitive science, learning technologies, and human-centered AI. Through invited talks, paper presentations, and open discussion, the workshop engaged with topics including calibrated initiative, knowledge-gap navigation, long-term memory, value-sensitive design, implicit personalization, AI-mediated care, proactive dialogue, and evaluation beyond task accuracy. A central theme across the workshop was that proactivity should not be understood only as earlier action or improved prediction, but as a form of initiative that must be appropriately timed, transparent, contestable, and aligned with user goals. This report summarizes the workshop and synthesizes the research challenges it surfaced for designing proactive and personalized agents in interactive information access.
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All authors Kirandeep Kaur, Vinayak Gupta, Tanya Roosta, Madhura Raju, Grace Hui Yang, Chirag Shah

Human-Computer Interaction

SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects

Ru Wang, Rahul Jain, Koichiro Niinuma et al. (+1 more)

Fine-grained control over continuous semantic attributes of 3D objects is essential for 3D content creation, but is not well supported by conventional 3D modeling workflows or prompt-based interaction with existing generative AI tools. While slider-based methods have proven effective for fine-grained semantic control in 2D image generation, no equivalent approach exists for 3D. Extending these 2D methods to 3D is non-trivial due to challenges unique to 3D, including geometric integrity and cross-view coherence. We present SemanticSlider3D, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training. Given a user-specified attribute, our pipeline constructs a semantic editing direction in the latent space of a state-of-the-art 3D generation model, presenting a diverse and coherent spectrum of 3D variations. A technical validation on a dataset of 50 3D object-attribute pairs shows our method was preferred by all five human assessors across variation range, consistency, 3D object quality, and attribute disentanglement, over a baseline combining a 2D slider with an image-to-3D model. An exploratory study with six participants demonstrates that SemanticSlider3D supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
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All authors Ru Wang, Rahul Jain, Koichiro Niinuma, Aakar Gupta

Human-Computer Interaction

Measuring Proof Burden in Public Bounty Listings: A RentAHuman Case Study

Iman YeckehZaare

Online bounty markets let requesters advertise paid tasks. Workers may be asked not just to complete a task but to prove it, and proof can mean exposure: revealing identity or location, using a personal account, posting publicly, acting in the physical world, or repeated evidence at later checks, none disclosed by the posted price. We call these advertised requirements proof burden and measure them on RentAHuman, a 2026 market publicized as a place for AI agents to hire humans. We study what listings request, not what workers submit or experience. We manually audited a nonrandom May 31, 2026 snapshot: every listing our searches returned from RentAHuman and Human Pages, another such market (981 listings, all but one from RentAHuman). Two independent coders recorded 13 features (11 kinds of evidence, recurring monitoring, physical-world action) and our 0-5 Proof Burden Score; a blinded third resolved all disagreements. A planned content screen leaves 779 bounty/task listings as the primary population; 438 (56.2%) score 4 or 5, spanning 154 distinct feature combinations: a checklist, not a single score, tells workers what a listing entails. Platform metadata labels some requester accounts as agents or bots. Exploratory comparisons show physical-world action, location proof, or recurring monitoring in 75.0% of agent-or-bot-labeled versus 55.3% of human-labeled listings, though score-4-or-5 shares did not clearly differ. The labels are self-reported or platform-assigned, the agent-or-bot-labeled listings come from only 20 displayed names, and the comparison was chosen post hoc, after seeing the data: a hypothesis, not a confirmed difference. We contribute the 13-requirement vocabulary, the adjudicated manual audit, and this descriptive case study; the score is a secondary screening summary. The study offers no worker-validated measure or automated detector yet.
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