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Computer Science Human-Computer Interaction

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Thursday 8 October

Human-Computer Interaction

STcubeOperator: A Framework for Analyzing Spatiotemporal Event Data

Julius Rauscher, Lucas Joos, Mark Rohonyi et al. (+2 more)

The analysis of spatiotemporal event data is essential for informed decision-making in domains such as disaster response, conflict analysis, or intelligence investigations. However, the complexity and interdependence of spatial, temporal, and multiple thematic attributes pose significant challenges for both analysis and visualization. While space-time cubes (STCs) present a powerful integrated visualization technique to analyze this kind of data, existing approaches often lack support for complex exploratory workflows, thus limiting the ability to derive meaningful insights. We address this gap by introducing STcubeOperator, a novel framework that models analysis tasks through space-time cube operations, considering them in context of visualizations, interactions, and computational choices, and implement them in an interactive visual analytics environment. By expressing analysis tasks as a sequence of multiple elementary operations–such as filtering, chopping, and flattening–our approach enables analysts to dynamically explore data from different perspectives. We further provide an open-source prototype implementing the operations in a 3D interactive environment to facilitate task-based exploratory analysis of spatiotemporal event data. We demonstrate the applicability of our framework with a case study based on real-world data on strategic and military operations in the Russia-Ukrainian War, showing its capabilities to reveal spatiotemporal patterns. An expert user study (n=8) shows how specific tasks can be solved with our framework, highlights the versatility of our approach, and provides valuable insights on which operations experienced analysts utilize in practice.
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All authors Julius Rauscher, Lucas Joos, Mark Rohonyi, Daniel A. Keim, Maximilian T. Fischer

Human-Computer Interaction

NeuroDivSim: An Interactive Tool for Model-Based Reflection on Cognitive Diversity in Interface Design

Eske Beckefeld and Henrik H.J. Detjen

Recent approaches to simulated and synthetic users offer new ways to support design, but raise questions about how computational representations of users should contribute to design practice. We present NeuroDivSim, an interactive tool that explores simulation as an inspectable mechanism for reflecting on cognitive diversity during design and prototyping. Rather than using an LLM to act as a simulated user, NeuroDivSim uses generative AI to construct inspectable task, interface, and environment models from a usage scenario. After human review, these models are combined with explicit cognitive reference configurations and processed through deterministic simulation. This enables designers to hold a modeled usage situation constant while varying cognitive assumptions and tracing their consequences to interaction steps and rule-based design recommendations. We further report an exploratory pilot evaluation (N=10) that provided formative insights into how participants engaged with the workflow and informed subsequent refinements to the presentation of models, simulation results, and recommendations.
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Human-Computer Interaction

Design Creativity Bench: Measuring creativity in LLM-Generated UI

Aman Rusia, Abhijit Bhole, Prashank Gupta et al. (+1 more)

As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same prompt (originality), how much a model's designs change between two prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model in our test set, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue. A live leaderboard with newer models is maintained at https://kombai.com/benchmark/.
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All authors Aman Rusia, Abhijit Bhole, Prashank Gupta, Dipanjan Dey

Human-Computer Interaction

SpheriColor: Colormaps for Spherical Geospatial Input Topographies

Julius Rauscher, Johannes Fuchs, Daniel A. Keim et al. (+1 more)

Multivariate geospatial data visualization often relies on multiple coordinated views, where color can be used to either link views or encode data attributes. Encoding spatial locations through color can reveal patterns in non-spatial visualizations, yet most applications of colormaps focus on high-dimensional attribute encodings instead. While 2D colormaps have been studied extensively, color encodings designed for spherical geospatial data have received much less attention, even though all locations lie on a sphere. To address this, we propose SpheriColor, a colormap generation approach for mapping geospatial references guided by the design goals of distance preservation and colorspace exploitation. We project a geospatial distance function into a perceptually linear colorspace, followed by two different gamut-constrained optimization strategies. We perform a quantitative evaluation using both real-world and synthetic datasets, demonstrating superior performance over 2D colormaps and HSLuv double cone encodings. The simplex-based optimization excels at distance preservation, whereas the ray-based approach provides better colorspace exploitation.
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All authors Julius Rauscher, Johannes Fuchs, Daniel A. Keim, Frederik L. Dennig

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

It's Always 10:10: Reference Images Break a Bias That Prompts Only Dent

Luca Cazzaniga

Text-to-image models appear to reproduce the habits of the photographs they learned from. Analog clocks are an extreme case: in advertising, watches almost always show 10:10, and generated clocks return to 10:10 even when another time is requested. We measure this bias and test three ways of overcoming it on 52 models available on the Magnific platform, with a replication on Higgsfield. Every image shows three identical clocks that must show 2:35, 6:50 and 11:20. The description of the object is fixed and only the request about the time changes: no time (A), the time in digits (B), the hand positions described by construction relative to the dial numerals (C), or the same description plus a drawn reference dial (D). Two AI readers read all 1,799 images blind from coded copies, with a third reader and the author settling disagreements (dial-level agreement 96.0% and 97.3%). With no time requested, 67% of the images have all three clocks at 10:10. On the 20 current models, all three clocks are correct in 34% of the images with digits, 30% with the hands described in words and 75% with the reference dial (D-B: +37 points, 95% CI +28 to +45); we found no evidence that describing the hands in words beats the digits (C-B: -4 points, CI -10 to +1). The replication on the 12 models shared by both platforms gives the same picture (B 54%, C 50%, D 81%). Writing the time reduces the bias but leaves two thirds of the images of the 20 current models with at least one wrong clock; adding a drawn reference raises full accuracy to three quarters and almost eliminates images entirely at 10:10. We release all images, prompts, raw readings and a script that recomputes every result.
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Human-Computer Interaction

Magic Pen: Automatic Pen Mode Switching for Document Annotation

Kevin Desousa, Adam Bradley, Nathalie Henry Riche et al. (+2 more)

Traditional digital pen interfaces use menu buttons to change the pen mode, which results in time and cognitive load spent on round-trip interactions and mode errors from tapping small mode selection buttons. This work presents the Magic Pen, a technique which uses machine learning to automatically switch between digital pen modes without requiring explicit mode changes. Magic Pen is driven by an LSTM model trained on pen data collected from 27 participants across two studies and uses transfer learning to iteratively tune the model towards how a specific user annotates. Error mitigation techniques using a flick gesture or on-screen tap are incorporated to correct mode errors or remove a stroke quickly. We evaluated Magic Pen in a comparative study with 18 participants, followed by iterative improvements and a deployment study with 8 participants. Magic Pen was preferred compared to a conventional menu-based approach, and transfer learning allowed for greater model predictability and stability.
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All authors Kevin Desousa, Adam Bradley, Nathalie Henry Riche, Ken Hinckley, Christopher Collins

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

TAP3D: Thermal-Assisted 3D Human Point Clouds

Xie Zhang, Chengxiao Li, Xuan Liu et al. (+1 more)

Human body point clouds are a versatile representation for AI-enabled human sensing. However, existing methods using LiDAR, radar, and depth cameras suffer from inherent drawbacks in high cost, sparse reconstruction, and privacy concerns, etc. In this paper, we exploit low-cost thermal arrays and present TAP3D, the first system to reconstruct 3D human point clouds from body heat signatures, offering significant advantages in cost, density, human sensitivity, and privacy. To overcome major challenges in depth estimation, thermal interference, and multi-person separation, we propose a novel physics-informed design, which integrates a forward thermal physics model with two distinct modules: multi-primitive estimation for self-supervised joint recovery of depth and other thermal properties, and geometric perspective fusion for suppressing interference and disentangling multiple people. We implement TAP3D using a single commodity thermal array sensor and build a large-scale dataset (160K samples, 8 environments, 11 users) for evaluation. TAP3D achieves remarkable accuracy for dense point cloud generation, enabling downstream tasks like fall detection (91.46%), indoor tracking (21.86 cm MAE), and human mesh recovery (4.87 cm error). By transforming body heat into point clouds for the first time, TAP3D pioneers a new paradigm for privacy-first, fully passive human sensing for many applications. TAP3D is open-sourced at https://github.com/aiot-lab/TAP3D.
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All authors Xie Zhang, Chengxiao Li, Xuan Liu, Chenshu Wu

Human-Computer Interaction

Intent Graph: Navigating the Analytical Reasoning Space for Exploratory Data Analysis

Junran Yang, Shruti Badrish, Teanna Barrett et al. (+1 more)

Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can answer them, prioritizing directions with domain knowledge and prior hypotheses. Large language models (LLMs) can supply such knowledge, but their responses are unstructured, leaving analysts no way to see what has been explored, what is missing, or why one direction was chosen over another. We present DAG-EDA, a system that lets analysts and an LLM co-navigate the space of possible analyses through two linked structures. An intent graph, governed by a grammar of analytical intent, decomposes an ambiguous natural-language question into progressively concrete analysis tasks, keeping alternative framings open and letting analysts branch, backtrack, and compare paths. A multi-layered knowledge graph externalizes the LLM's domain knowledge, linking domain concepts to the dataset variables that can measure them, so analysts can inspect and contest how their question is grounded in the data. Both graphs are constructed from only the dataset and the analyst's question, and the analyses the analyst reaches are rendered as interactive dashboards. We illustrate the system through a usage scenario and describe a user study design for examining whether the system scaffold analysts' reasoning and navigation.
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All authors Junran Yang, Shruti Badrish, Teanna Barrett, Leilani Battle

Wednesday 7 October

Human-Computer Interaction · Cross-listed from physics.soc-ph

An ecology of participation for fusion energy development

Aditi Verma, Katie Snyder, Andrea Morales Coto et al. (+10 more)

Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and introduce a framework that reinterprets opposition as a divergence between communities and technology developers in values, norms, or design characteristics. We present six case studies from our work: participatory technology assessment focus groups, participatory design workshops, immersive virtual-reality models of a fusion facility, the Global Fusion Forum platform, the Imaginary Energies speculative design platform, and Heartbeat, an arts-based sound installation. Mapping these onto the values-norms-design framework shows how each interrogates or closes a different part of the expert-public gap, and exposes two limitations of fusion public engagement efforts writ large: developers are seldom asked to articulate their own values and norms, and the environment is represented only indirectly. We close with five guiding questions for fusion researchers building their own participatory engagements.
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All authors Aditi Verma, Katie Snyder, Andrea Morales Coto, Nathan Kawamoto, Daniel Hoover, Ana Kova, Sara Eskandari, Stephanie O'Malley, Jared Owens, Mahmud Farooque, Gabrielle Hoelzle, Stephanie Diem, Kevin Daley

Human-Computer Interaction

Feeling Wistful: Reflecting on Scholarly Sensibilities with Creative Reading Traces

Sophia W. Liu, Kate Chier, Shm Garanganao Almeda et al. (+2 more)

Researchers often read before they can articulate what they are looking for. As AI increasingly mediates scholarly search and synthesis, understanding and preserving the idiosyncratic judgments guiding early exploration become important. We call these evolving orientations scholarly sensibilities. To understand curiosity-driven reading, we first examined Wikipedia rabbitholing, a self-directed browsing practice, then designed Wistful, a research probe for open-ended scholarly exploration that captures reading paths as creative reading traces. We studied Wistful with 16 HCI researchers—eight junior and eight senior—in comparison with their usual workflows. Researchers approached the same scholarly landscape differently, with familiarity and personal interests shaping what they pursued and semantic proximity and scholarly links shaping their paths. Their traces made these differences visible and let readers revisit and compare their paths. We position creative reading traces as artifacts for reflection and exchange and as a means of studying how scholarly sensibilities are expressed through reading.
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All authors Sophia W. Liu, Kate Chier, Shm Garanganao Almeda, Max Kreminski, Bjoern Hartmann

Human-Computer Interaction

Guided Reflection for Personal Sleep Insight in Everyday Sleep Tracking

Bokyung Kim, Amama Mahmood, Honghao Zhao et al. (+4 more)

Digital sleep technologies make tracking accessible, yet users often struggle to interpret what changes in their sleep mean. Behavioral sleep medicine addresses this through guided discovery, helping patients develop personal interpretations rather than simply receiving explanations. To bring this to everyday tracking, we present DREAM, an LLM-powered voice assistant that monitors conversational sleep diaries, selectively invites users to interpret meaningful changes, and uses their interpretation to tailor subsequent education. We co-designed DREAM with sleep specialists iteratively and evaluated it in a six-week field study (N=14) against a generic-education control. DREAM participants reported greater personal sleep insight, motivation, and willingness to use the system, and described a clearer rationale for trying strategies. Our findings suggest that guided reflection made participants more active interpreters of their own sleep. Furthermore, we argue that expert involvement is not a single transfer of knowledge but an ongoing process of making tacit judgment explicit and testable.
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All authors Bokyung Kim, Amama Mahmood, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Ziang Xiao, Chien-Ming Huang

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

Improving social media for democratic discourse

Fan Cheng, Amirhossein Farzmahdi, Pinyuan Feng et al. (+8 more)

Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenue, contributing to concerns about polarization, misinformation, social isolation, and loss of civility. We explore how social media might instead be deliberately designed to support democratic discourse, collective deliberation, and collective intelligence. Drawing on literature across computer science, psychology, political science, and related fields, we present a modular collection of mechanisms that could be implemented individually or in combination. These include user-controlled and open recommender systems, tools for exposure to diverse perspectives, new forms of cognitive and epistemic feedback, collaborative and AI-assisted fact-checking, privacy and visibility controls, mechanisms for improving civility and evidentiary integrity, and reputation systems that reward high-quality participation. The proposals are intended both as a practical menu of design possibilities and as a starting point for broader interdisciplinary discussion about digital public spaces designed around democratic values rather than engagement alone.
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All authors Fan Cheng, Amirhossein Farzmahdi, Pinyuan Feng, Kedar Garzón Gupta, Trenton Jerde, Nikolaus Kriegeskorte, Zi Qi Liow, Akihito Maruya, Savannah Smith, Patrick Stinson, JohnMark Taylor

Human-Computer Interaction

AI-Mediated Self: How HCI Defines and Relates to the Self

Jenny Xiyu Fu, Qian Yang and Malte Jung

How might AI alter how we understand and experience the self? This scoping review analyzes 102 papers to examine how the self is defined in the field of human-computer interaction (HCI), how AI-self relationships are conceptualized, and what risks emerge when AI becomes entangled with selfhood. Our synthesis makes three contributions. First, we define AI-mediated self as a conceptual umbrella that connects dispersed work across education, workplace, health, and creative practices. Second, we consolidate six framings of the self with four domains of ethical risk-agency/autonomy, identity/authorship, relational capacity, and meaning-making-into a conceptual map that provides a reusable vocabulary across contexts. Third, we introduce the Inclusion of AI-Self framework, which situates AI-self relationships along a spectrum of proximity. Together, these contributions position selfhood as a central design space in HCI.
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Human-Computer Interaction

What does it mean to use AI critically? Unpacking critical AI literacy through students' evaluation of AI-generated content

Hyejeong Lee and Wonjin Yu

Although critical AI literacy has emerged as an important educational goal, the construct remains conceptually broad and insufficiently specified for guiding students' day-to-day interactions with AI. This study examines how students critically evaluated AI-generated content. Drawing on an analysis of students' chatbot interactions and written reflections, we first identified seven stages of AI-supported academic task completion and five functional roles assumed by AI. More importantly, we identified eight evaluative lenses that students used to assess AI-generated responses (accuracy, completeness, task alignment/relevance, personalization, practicality, creativity, ethics, and bias). The findings suggest that critical AI literacy extends beyond verifying the factual accuracy of AI-generated information. Rather, critically engaging with AI involves situated and multidimensional judgment about the epistemic quality, contextual fit, practical feasibility, creative value, and ethical implications of AI-generated content. The framework offers both a conceptual contribution to the emerging literature on critical AI literacy and a practical tool for educators seeking to help learners move from passive acceptance of AI toward deliberate, context-sensitive, and responsible human judgment.
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Human-Computer Interaction

How assigned AI use before class shapes active student engagement in class

Dan J. Wang, Neelam Modi Jain, Vanessa Burbano et al. (+5 more)

AI learning tools are rapidly entering classrooms, but evidence about whether they help students learn is mixed and rests mostly on test scores. Comparatively less research addresses whether the use of AI changes students' live learning behaviors in class. Here, we report the results of a preregistered field experiment with 759 MBA students enrolled in ten sections of a course, in which each student was randomly assigned two of ten class sessions to prepare for with a purpose-built voice-based AI discussion partner. After two uses of the AI discussion partner, students made about 31% more voluntary contributions in each later class session. Students who used the AI discussion partner more also reported greater comfort speaking up and greater perceived learning, but not greater focus or motivation. These findings suggest that repeated practice with a voice-based AI partner can meaningfully increase students' engagement in class discussion, enhancing a critical intermediate learning outcome.
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All authors Dan J. Wang, Neelam Modi Jain, Vanessa Burbano, Jorge Guzman, Daniel Keum, Soomi Kim, Bruce Kogut, Nataliya Wright

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

MemoCare: An Interactive Multimodal Mobile System for Automated Cognitive Screening

Duy-Cat Can, Mau Minh Phuc Le, Tuan-Khoa Hoang et al. (+10 more)

MemoCare is an interactive mobile system for automated multimodal cognitive screening. A React Native application combines spoken responses, temporal and spatial orientation, touchscreen actions, and visuoconstruction in complete English and Vietnamese workflows. Speech is transcribed by Google Speech-to-Text and scored locally with deterministic task-specific natural language processing rules; GPS coordinates are resolved by the MemoCare spatial module before answer matching; touch tasks are scored from interaction events; and the drawing task uses a three-model convolutional neural network consensus with separate visual interpretation. Software tests pass 151/151 predefined cases across speech/language, spatial-answer, and touch-interaction scoring, while spatial regression passes 48/48 four-country coordinate-resolution cases. For the drawing module, validation-selected ShuffleNetV2 x1.5 achieved 91.33% mean balanced accuracy and 78.87% exact three-criterion accuracy on a locked 71-image test set. Four clinician co-authors additionally inspected the end-to-end workflow, yielding a pooled median rating of 4/5 across eight criteria, with item-level medians ranging from 3 to 4.5. At MMM, attendees can directly try a shortened multimodal screening workflow and inspect automatic item-level and total scoring.
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All authors Duy-Cat Can, Mau Minh Phuc Le, Tuan-Khoa Hoang, Hai-Dang Nguyen, Trung-Hieu Do, Dang Minh Ly, Minh-Duc Nguyen, Nghia TT Hoang, Linh-Trung Nguyen, Huy-Hieu Pham, Huong Ha, Binh T. Nguyen, Oliver Y. Chén

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

CrossWeave: Bridging Perspectives Across Online Communities with a Dual-Pane Design

Fei Fang, Reva Hirave, William Jurayj et al. (+19 more)

Social media systems typically display conversations among already familiar contributors, which can be predictable and one-sided. In civic discourse, this design narrows discussion, reinforces divides, and distorts the perception of public opinion. To encourage cross-community engagement, we present CrossWeave, an AI-powered bridging system that augments the standard social media feed. As the user reads a post, CrossWeave surfaces diverse relevant posts from other threads in a side pane and highlights the connections. Users are invited to venture out of their echo chamber, explore a broader range of views and arguments, and “click across” to engage with their authors. When they do, CrossWeave facilitates constructive posting, not only by showcasing relevant past content but also by simulating possible reactions as the user drafts a post.
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All authors Fei Fang, Reva Hirave, William Jurayj, Yuqi Li, Brian Lu, Tarik Metin, Tsugunobu Miyake, Kateryna Morhun, Yash Permalla, Kenan Rustamov, Allen Shen, Haojun Shi, Prabhav Singh, Xiheng Tom Wang, Kevin Xu, Qingcheng Zeng, Jiayi Zhang, Daniel Khashabi, Andrew Perrin, Tiziano Piccardi, Ziang Xiao, Jason Eisner

Human-Computer Interaction

PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling

Chentao Li, Mingze Gao, Runze Sun et al. (+3 more)

As smart glasses and lightweight MR devices become increasingly practical, input remains a key challenge. The bare palm is an always-available, tactile, and proprioceptively accessible surface, but it has neither an explicit coordinate system nor embedded touch sensing. Prior on-palm systems typically expose isolated touch events, discrete regions, continuous trajectories, or task-specific gestures, limiting the palm's ability to support precise selection and gesture manipulation through a common input representation. We present PalmSpace, a wrist-worn infrared system that exposes mode-aware, body-referenced absolute input on the bare palm without per-user sensing calibration. At the interaction level, PalmSpace jointly represents contact occurrence, interaction mode, and palm-referenced absolute location; at the model level, it learns these coupled outputs through a shared real-time representation. In leave-one-participant-out evaluation with 17 participants, PalmSpace achieved 6.7 mm mean localization error, 98.9% contact detection accuracy, and 96.7% F1 for four-class interaction-state recognition. User studies further demonstrated absolute pointing and dragging, eyes-free digit input, and representative multi-finger controls including scrolling and pinch-based map manipulation. These results show that a morphologically variable bare palm can function as a transferable, mode-aware interaction surface.
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All authors Chentao Li, Mingze Gao, Runze Sun, Zhaoguo Wang, Jianjiang Feng, Jie Zhou

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

The Handover Problem: Governing Autonomy Transitions in Human-AI Collaboration

Vicente Pelechano, Antoni Mestre, Manoli Albert et al. (+1 more)

Human-machine systems rarely operate at a fixed level of AI autonomy. As operators and AI systems collaborate over time, control must shift: the AI can take on more responsibility when collaboration is stable, maintain its current role when evidence is ambiguous, or return control to the human when conditions deteriorate. Existing work on adaptive automation, supervisory control, trust in automation, and deskilling explains parts of this problem, but provides no auditable, multi-signal criterion for governing when autonomy should change across multi-cycle workflows. We formalise this challenge as the Handover Problem: deciding, at each operational cycle, whether to escalate, maintain, or revert AI autonomy while keeping the process reversible, recoverable, and auditable. We introduce the Handover Readiness Score (HRS), a transparent composite measure that integrates four signal dimensions: operator readiness, human-AI trust, learning stability, and operational performance. It is combined with a hysteresis-based transition policy that requires sustained positive evidence before increasing autonomy but reverts promptly when conditions worsen. Across software engineering and manufacturing domains, the HRS and hard safety guards address complementary failure regimes: guards enforce immediate corrective action when a single indicator breaches a critical threshold, while the HRS detects the slow, multi-signal erosion of operator readiness that no individual guard can observe. The framework establishes autonomy handover as a governance problem requiring explicit, composite, and auditable criteria. This provides a conceptual and formal foundation that adaptive automation research has not previously provided.
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All authors Vicente Pelechano, Antoni Mestre, Manoli Albert, Miriam Gil

Human-Computer Interaction

Active Inference for Interaction-Mediated Control of a High-Dimensional Robotic Arm

Fraser C. Paterson, Sebastian Stein, Markus Klar et al. (+2 more)

We propose interaction-mediated control via Active Inference as a general architectural approach to high-dimensional control problems in Human–Computer Interaction (HCI). This architecture recasts user interaction as the provision of evidence about a latent task objective, rather than the direct specification of plant-control inputs. The mediating function is distributed between an interaction broker, which selects informative user queries and performs Bayesian inference over user preferences, and an Active Inference controller, which autonomously plans and acts under the resulting preference information to control the plant. This division of labour decouples the semantics of user interaction from those of low-level plant control. We instantiate the architecture in a simulated, multi-link robotic arm to perform a simultaneous whole-arm target-coverage task. A simulated user communicates exclusively through a clutch-style binary evaluative channel, without specifying joint-torque commands. Across increasing arm dimensionalities, the architecture achieves successful interaction-mediated control, although task success is lower than when the controller receives the true target preferences directly. Exact target-subset identification also remains imperfect, highlighting the distinction between preference inference and successful task completion. The experimental findings provide an initial computational demonstration of the proposed architecture under controlled, matched-model assumptions and motivate its further investigation in broader HCI applications.
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All authors Fraser C. Paterson, Sebastian Stein, Markus Klar, John H. Williamson, Roderick Murray-Smith

Human-Computer Interaction

DuoSketch: How Pairs Navigate Challenges in AI-Supported Collaborative Design Ideation

Weiyan Shi, Darryl Lim, Geraldine Quek et al. (+1 more)

Generative AI offers new resources for collaborative design ideation, yet how designers work with it as a shared concept develops remains underexplored. We developed DuoSketch, integrating a shared canvas and live transcripts with a separate AI for each member of a pair. An exploratory qualitative study with 12 pairs identified three challenges: unresolved fit between AI proposals and shared design purposes, shared meanings not expressed in AI responses, and agreed changes missing from later outputs. We found that pairs navigated these challenges through bridging work in two directions: AI -> Shared Design (reworking features, developing uses, and assessing fit with shared requirements) and Shared Design -> AI (supplying materials, explaining meanings, and communicating decisions). We discuss how future AI systems can support the joint exploration and articulation of design ideas.
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All authors Weiyan Shi, Darryl Lim, Geraldine Quek, Kenny Tsu Wei Choo

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

MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

Marius Bock, Yuwei Zhang, Juergen Gall et al. (+3 more)

Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings into foundational motion models remains an open challenge. Self-supervised learning (SSL) has alleviated the need for costly annotations, yet existing approaches leave the global structure of large-scale motion data largely untapped, relying on randomly sampled batches and local comparisons that become particularly problematic for in-the-wild inertial data dominated by stationary, low-variance behaviors. Here we introduce Morphological Contrastive Learning (MorphCL), a self-supervised pretraining framework that uses structure-aware grouping to inject explicit modeling of global structure into inertial-based SSL approaches. Building on two well-established pillars of motion analysis, the discovery of motion primitives, or motifs, and domain-specific feature descriptors, we show that MorphCL substantially improves linear probing and finetuning results of learned encoders by up to 15 percentage points in F1-score. In a comparison with existing foundation models, we demonstrate that MorphCL-pretrained encoders match or surpass them models in linear probing performance while trained on $4600\times$ less data. Qualitative analysis of the resulting embedding spaces further reveals morphologically meaningful cluster structure, with improved separation of kinematically similar activity classes.
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All authors Marius Bock, Yuwei Zhang, Juergen Gall, Michael Moeller, Kristof Van Laerhoven, Cecilia Mascolo

Human-Computer Interaction

EEG and Eye-Tracking Evidence That AI Disclosure Shapes Face Evaluation

Teodora Mitrevska, Luise Donat, Andreas Butz et al. (+3 more)

AI-generated faces can be difficult to distinguish from real ones, leaving viewers to rely on source labels when judging an image. Yet prior work has made it difficult to separate the effects of what an image actually is from what viewers are told it is. We validated faces as AI-generated or human in an online study (N=169), then crossed actual source (AI, human) with label (none, Made with AI, Made by a human) in a lab study $N=30), recording event-related potentials (ERPs) and gaze. ERP responses were equivalent for AI-generated and real faces, but varied with the label: labels drew early attention (N2), while labels that conflicted with the face's actual source prompted re-evaluation of the face (P3). Affective processing and initial gaze orienting were unchanged, but labels altered visual exploration. We provide a validated stimulus set and evidence that attributed origin shapes face processing, with implications for disclosure design.
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All authors Teodora Mitrevska, Luise Donat, Andreas Butz, Thomas Kosch, Abdallah El Ali, Francesco Chiossi

Human-Computer Interaction

A Scale For Value Alignment In Human-AI Interaction

Lena Hegemann, Steeven Villa, Hyemin Bang et al. (+4 more)

Value alignment is a central objective in AI and HCI research, yet no validated instrument measures how users perceive it. This gap hampers the comparison and accumulation of findings and limits the effectiveness of applications, where understanding users' viewpoints is critical. We construct and evaluate a 13-item psychometric scale that measures perceived value alignment across two components: value understanding and value manifestation. It is based on a large item pool drawn from prior empirical studies, filtered by experts, and finally assessed by users (N=607) across diverse AI scenarios. Confirmatory factor analysis on an independent sample (N=259) confirmed the two-factor structure and high internal consistency for both subscales. Using optimization, we also derived a 6-item short form for quick administration. The scale is a reliable measure, providing HCI researchers with a common evaluation metric across contexts.
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All authors Lena Hegemann, Steeven Villa, Hyemin Bang, Mitchell L. Gordon, Antti Oulasvirta, Robin Welsch, Patrick Ebel

Human-Computer Interaction

Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance

Sebastian Cochinescu

We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about attachment, trust, perceived mind, or machine interiority. Partiality has two components: caring, defined as allocation of a finite interaction surplus above a guaranteed per-user service floor, and particularity, defined as a per-user state estimate accumulated from relationship history on fixed inference machinery. We implement both as a persistence layer over one open-weight base model and evaluate them on constructed multi-session relationships with known hidden-state schedules. A four-channel divergence compares known-user and stranger conditions on identical probes with paired generation seeds; the stranger prompt is not length-matched, and one channel (initiative) is an allocator output rather than generated behavior. The full mechanism reproduces the ordinal shape, but not the magnitude, of an analytically specified curve and is the only arm satisfying the protocol-defined joint signature on the calibrated battery. The initial mechanism fails probe-quality equivalence because relationship content reduces topical relevance. A guarded revision satisfies equivalence on the calibrated battery but not on a second battery not used in calibration, where it scores higher than the unmodified baseline. On that second battery, the specificity margin is 0.0198, below the 0.02 protocol threshold. The paired mean right-user–wrong-user estimation-accuracy contrast is 0.375 but is not positive in every run. The mechanism is therefore a candidate for a later perception study, not evidence that users perceive love. The versioned protocol record is not independently time-stamped and is not described as a preregistration. Ethical constraints include a stranger-treatment floor, a bounded surplus, disclosure, and de-intensification.
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Human-Computer Interaction

A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration

Alexandra Coroiu, Andrea Vogt, Viktor Werbilo et al. (+3 more)

Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based robots. Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-robot adaptation. We conducted a scoping review of RLHF in HRC systems, mapping methods that address these challenges. Following PRISMA guidelines, we screened 199 records and included 20 peer-reviewed publications (2020-2025) spanning multiple HRC domains. To our knowledge, this is the first review focused on the bidirectional, closed-loop design of RLHF. Our review found multiple feedback modalities enabling data collection in various feedback formats. Collected data can be integrated at different stages of AI training, resulting in a multi-step development process. Pilot experiments are commonly used to evaluate HRC systems based on both human and robot metrics. To empirically test a key gap identified in the review, we conducted a between-subjects VR experiment comparing system- and user-initiated feedback on robot proxemic behaviour for safe navigation. Using Bayesian models, we analysed the relation between the collected feedback and safety metrics: psychological safety (post-experiment questionnaire) and physical safety (inverse time-to-collision). Results show that user-initiated feedback captures perceived safety better than system-initiated feedback, indicating that feedback timing directly affects feedback quality. Our review and experiment findings show that RLHF relies on appropriate feedback methods to ensure AI safety in HRC, and future RLHF research should prioritise realistic HRC experiments evaluating the effects of feedback collection methods on relevant human and robot metrics.
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All authors Alexandra Coroiu, Andrea Vogt, Viktor Werbilo, Andreas Poppele, Johann Christensen, Sven Hallerbach

Human-Computer Interaction

Empowering Users in Graph Rule Mining via Large Language Models

Francesco Cambria, Francesco Invernici, Andrea Colombo et al. (+1 more)

In the era of interconnected data, graphs have emerged as an effective abstraction for modeling complex systems in an intuitive format, especially with the rise of Property Graphs, which offer an intuitive and scalable way of navigating non-intuitive structures. In this context, graph mining techniques have been developed for testing complex graph-based rules, as the MINE GRAPH RULE operator, which, however, require users to have prior expertise both in graph theory and formal query language. In this work, we propose to bridge the gap between users and the graph-association rule-mining process by showing how Large Language Models (LLMs) can be easily prompted to formulate, refine, and interpret complex relational rules, directly producing MINE GRAPH RULE queries.
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All authors Francesco Cambria, Francesco Invernici, Andrea Colombo, Anna Bernasconi

Human-Computer Interaction

AI-Driven Urge Regulation Assistant (AURA): Designing Preemptive Smart Wearables for Smoking Cessation

Antoni Timothy, Lam Fu Yuan Kevin, Hou Junyi et al. (+1 more)

Smoking cessation remains a complex self-regulatory challenge, often undermined by cue-triggered cravings arising from habitual, emotional, and social contexts. Existing wearable cessation systems are largely reactive, detecting smoking events only after lapses occur and thus missing the critical window for timely intervention. Guided by cue-reactivity theory and Self-Determination Theory (SDT), we argue that future digital health systems should shift from retrospective feedback toward proactive craving management that supports user autonomy and competence. To inform the design of such systems, we present a formative pilot qualitative study involving semi-structured interviews with 10 smokers and ex-smokers from a multi-ethnic Asian population, a demographic underrepresented in current wearable AI and smoking cessation research. Through an iterative co-design interview process, we examine (1) the barriers and facilitators to smoking cessation and (2) the desired design features of wearable systems targeting cravings before lapses occur. Our findings identify key craving predictors across habitual, emotional, and social dimensions, alongside user-preferred intervention strategies such as social accountability, personalized support, rewards, and just-in-time distraction techniques. Participants also emphasized the importance of trust, privacy, personalization, and non-judgmental interaction styles. These insights directly inform the design of AURA, a novel smartwatch-smartphone ecosystem for proactive craving detection and intervention using commercially available wearable devices.
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All authors Antoni Timothy, Lam Fu Yuan Kevin, Hou Junyi, Cheong Wei Soon

Human-Computer Interaction

Healthy skepticism in AI: a data visualization research agenda

G. Elisabeta Marai, Marc Baaden, Michael Behrisch et al. (+2 more)

Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI. The trustworthiness line of work was built at least partially on an assumption that humans were critical users unlikely to adopt AI technology. It is increasingly clear that human trust levels in AI span, in fact, a wide range from critical to over-reliant. There is an urgent need to support both trust and healthy skepticism in AI solutions. We argue that it is healthy for humans to adopt a skeptical view both on the results of AI models and on the use of such AI models. We share our thoughts on the rising phenomenon of over-reliance on AI models, the risks and opportunities in using AI models, and the role of data visualization in over-reliance situations where humans are not motivated to engage in critical thinking.
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All authors G. Elisabeta Marai, Marc Baaden, Michael Behrisch, Michael Krone, Pere-Pau Vázquez

Human-Computer Interaction

When My Skill Becomes Agent Skill: How Knowledge Workers Share Their Expertise with AI Systems

Tianqi Song, Zicheng Zhu, Hancheng Cao et al. (+1 more)

Organizations have long sought to make workers' expertise reusable by others. Agentic AI changes the nature of such reuse by enabling AI systems to act on workers' knowledge with limited human involvement. This raises the question of how this shift shapes workers' willingness to share their expertise. We conducted an experiment with knowledge workers who created materials incorporating domain knowledge and decided whether to authorize human or AI reuse. We find that AI and human reuse differ primarily in whether workers choose to share their knowledge, rather than in what they choose to share. Participants' reasoning shifts from prosocial considerations when sharing with humans toward concerns about loss of control, replacement, and downstream governance when sharing with AI. These findings suggest that AI reuse may intensify tensions between organizational knowledge reuse and contributors' interests. We discuss implications for workplace AI and knowledge management policies that preserve workers' rights and agency.
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All authors Tianqi Song, Zicheng Zhu, Hancheng Cao, Yi-Chieh Lee