DevLan/vispubs huggingface dataset:
Modalities of Regulation in Sustainable HCI: Rethinking the Role of Design in Shaping Sustainable Behaviours and Society
Authors: Chiara Rossitto, Rob Comber, Anton Poikolainen Rosén, Fatemeh Bakhshoudeh, Stanley J. Greenstein
Published at CHI, 2026

There have been repeated calls for more ecological approaches to Sustainable HCI, and for the inclusion of the sociopolitical, structural, historical, and geographical aspects that shape technology-mediated practices of sustainability. Contributing to this discourse, and drawing from scholarship on Law and IT, this paper explores how the law, technology design, market mechanisms, and social norms enable and constrain behaviours — that is, how they regulate them. We use these modalities to discuss two digital technologies: a gig-work platform mediating practices of waste transportation, and an eco-visualisation platform scraping sustainability reports of publicly listed companies. The analysis expands on the set of dynamics that characterise the relations between these practices of sustainability, technology design, and regulation. We conclude by discussing the relevance of this conceptualisation for HCI, specifically in defining the roles of designers as regulators, and regarding design for sustainability as constituted through varying entanglements of these modalities.
More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production
Authors: Amber Kusters, Pooja Prajod, Pablo César, Abdallah El Ali
Published at CHI, 2026

Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article. Through co-design sessions (N=10), we elicited 69 disclosure designs and implemented four prototypes that visually disclose human–AI collaboration in journalism. We then ran a within-subjects lab study (N=32) to examine how disclosure visualizations (Textual, Role-based Timeline, Task-based Timeline, Chatbot) and collaboration ratios (Primarily Human vs. Primarily AI) influenced visualization perceptions, gaze patterns, and post-experience responses. We found that textual disclosures were least effective in communicating human-AI collaboration, whereas Chatbot offered the most in-depth information. Furthermore, while role-based timelines amplified AI contribution in primarily human articles, task-based timeline shifted perceptions toward human involvement in primarily AI articles. We contribute Human-AI collaboration disclosure visualizations and their evaluation, and cautionary considerations on how visualizations can alter perceptions of AI's actual role during news article creation.
Movement as Medium: Personalisation of Instruments for Inclusive Creative Expression in Disability-Led Performance
Authors: Sam Trolland, Melinda Smith, Alon Ilsar, Jon McCormack
Published at CHI, 2026

This paper presents a practice-based case study that explores personalisation as a design methodology for embodied gestural instruments. Through a multi-year collaboration with a professional performer with physical disability, we employed research-through-design and co-design methods to iteratively develop a personalised instrument responsive to the performer's unique movement style and creative vision. The system enabled real-time control of sound, stage lighting, and visualisations, and was ecologically validated in rehearsals and in an award-winning disability-led theatre production. Extending beyond the stage, we refined the instrument through workshops with young people with motor, sensory, and communication disabilities, demonstrating its adaptability to diverse bodies and creative practices. Rather than focusing on generalised solutions, this work advances methods for designing technologies that embrace difference, tailoring interaction to individual capabilities. We contribute to HCI research by articulating personalisation as a methodological approach to inclusive interaction design, expanding opportunities for creative expression among people with physical disability.
Needling Through the Threads: A Visualization Tool for Navigating Threaded Online Discussions
Authors: Yijun Liu, Frederick Choi, Eshwar Chandrasekharan
Published at CHI, 2026

Navigating large-scale online discussions is difficult due to their rapid pace and high volume of content. Platforms like Reddit employ "threads'' to visually organize parallel discussions, but deep nesting obscures conversation flow. For moderators, this fragmentation compounds the difficulty of following evolving conversations and maintaining context across threads, which limits timely and effective moderation. In this paper, we present Needle, an interactive system that applies visual analytics to summarize key conversational metrics: activity, toxicity, and voting trends over time. Needle provides both high-level overviews and detailed breakdowns of threads, enabling moderators to identify priority areas without reading through entire nested conversations. Through a user study with ten Reddit moderators, we find that Needle provides a practical solution to maintain contextual understanding when navigating threaded discussions. Based on these findings, we propose design guidelines for future visualization-based tools that shape how people consume, interpret, and make sense of large-scale online discussions.
NetworkCanvas: Supporting Progressive Network Visualization Exploration via Adaptive Recommendations
Authors: Wenchao Li 0005, Yuewen Gao, Yu He 0024, Cong Zhu, Ke Xu
Published at CHI, 2026

Network visualization has become essential for understanding complex relationships across domains, yet network complexity creates an overwhelming exploration space where users frequently miss critical patterns. Existing tools often require predetermined analysis goals and manual workflow construction, limiting accessibility for non-experts. We present NetworkCanvas, a progressive network visualization system that guides users through personalized exploration via adaptive recommendations. Our approach combines a learning mechanism that adapts to user feedback, an analytic state graph preserving exploration provenance with branching paths, and a context-aware feedback interpreter that suggests analytical continuations based on selection patterns. Controlled studies demonstrate that NetworkCanvas users identified more noteworthy observations, reported higher confidence, and exhibited more systematic exploration compared to a baseline without recommendations. These results demonstrate that recommendation-guided exploration improves outcomes over unguided manual analysis; however, because our baseline lacked recommendation functionality entirely, the specific contribution of adaptive personalization versus static guidance remains an open question. Qualitative findings suggest that recommendations reduce analysis paralysis and support systematic exploration.
PeriphAR: Fast and Accurate Real-World Object Selection with Peripheral Augmented Reality Displays
Authors: Yutong Ren, Arnav Reddy, Michael Nebeling
Published at CHI, 2026

Gaze-based selection in XR requires visual confirmation due to eye-tracking limitations and target ambiguity in 3D contexts. Current designs for wide-FOV displays use world-locked, central overlays, which is not conducive to always-on AR glasses. This paper introduces PeriphAR (/peh-ree-faar/), a visualization technique that leverages peripheral vision for feedback during gaze-based selection on a monocular AR display. In a first user study, we isolated text, color, and shape properties of target objects to compare peripheral selection cues. Peripheral vision was more sensitive to color than shape, but this sensitivity rapidly declined at lower contrast. To preserve preattentive processing of color, we developed two strategies to enhance color in users' peripheral vision. In a second user study, our strategy that maximized contrast of the target to the neighboring object with the most similar color was subjectively preferred. As proof of concept, we implemented PeriphAR in an end-to-end system to test performance with real-world object detection.
Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research Ideation
Authors: Yiren Liu, Viraj Nischal Shah, Sangho Suh, Pao Siangliulue, Tal August, Yun Huang 0003
Published at CHI, 2026

Early-stage interdisciplinary research ideation is often challenged by limited expert access, uncertainty about what to ask, and the cognitive burden of synthesizing unfamiliar domain perspectives. This paper presents Perspectra, a forum-style multi-agent system that structures and visualizes deliberation among LLM-simulated domain experts to support exploration and refinement of emerging research ideas, while encouraging critical thinking and reflections. The interface design combines 1) a threaded canvas for parallel topic exploration with visualization of agent discourse dynamics informed by argumentation theory to aid sensemaking; and 2) feature that enables users to invite multiple self-chosen agents into an ongoing discussion. We conducted a user study with 18 participants, comparing Perspectra against a vanilla chat baseline given a task for the user to develop a short research proposal. Our findings show that Perspectra's design elicits significantly more higher-order critical thinking behaviors during interactions with agents when compared to a traditional chat interface. We also observed more interdisciplinary user replies via forum-styled design, and more frequent and structured proposal revisions (rather than unstructured note-taking). Based on our findings, we further contribute interaction design implications of using multi-agent deliberation for complex ideation and knowledge search, combining flexibility with structured exploration to support user sensemaking and critical thinking.
Practitioners' Perspectives on Designing Data Visualizations for the General Public
Authors: Regina Schuster, Kathleen Gregory, Torsten Möller, Laura Koesten
Published at CHI, 2026

Public-facing data visualizations can play a vital role in making complex information clear and engaging, thereby encouraging informed public discourse and participation. However, existing work offers limited insight into how practitioners make design decisions based on their envisioned target audiences and across different media channels. To investigate this, we conducted semi-structured interviews with 21 professionals from journalistic settings, focusing on how they conceptualize their readers, translate these notions into design choices, and evaluate their work. We found that practitioners often rely on broad audience definitions, despite considering "knowing their readers" essential. Evaluation primarily relies on peer feedback or social metrics rather than user testing. From these accounts, we identify recurring strategies employed to reach general, often undefined publics. We discuss implications for audience-centered authoring tools, proposing features such as persona simulations and content-adaptive multi-format authoring, message-first rhetoric-aware workflows, and lightweight in-tool evaluation to better support the realities of public-facing design.
PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualization
Authors: Erzhen Hu, Frederik Brudy, David Ledo, George W. Fitzmaurice, Fraser Anderson
Published at CHI, 2026

In pre-production, filmmakers and 3D animation experts must rapidly prototype ideas to explore a film's possibilities before full-scale production, yet conventional approaches involve trade-offs in efficiency and expressiveness. Hand-drawn storyboards often lack spatial precision needed for complex cinematography, while 3D previsualization demands expertise and high-quality rigged assets. To address this gap, we present PrevizWhiz, a system that leverages rough 3D scenes in combination with generative image and video models to create stylized video previews. The workflow integrates frame-level image restyling with adjustable resemblance, time-based editing through motion paths or external video inputs, and refinement into high-fidelity video clips. A study with filmmakers demonstrates that our system lowers technical barriers for film-makers, accelerates creative iteration, and effectively bridges the communication gap, while also surfacing challenges of continuity, authorship, and ethical consideration in AI-assisted filmmaking.
PriorWeaver: Prior Elicitation via Iterative Dataset Construction
Authors: Yuwei Xiao, Shuai Ma 0005, Antti Oulasvirta, Eunice Jun
Published at CHI, 2026

In Bayesian analysis, prior elicitation, or the process of facilitating the expression of one's beliefs to inform statistical modeling, is an essential yet challenging step. Analysts often have beliefs about real-world variables and their relationships. However, existing tools require analysts to translate these beliefs and express them indirectly as probability distributions over model parameters. We present PriorWeaver, an interactive visualization system that facilitates prior elicitation through iterative dataset construction and refinement. Analysts visually express their assumptions about individual variables and their relationships. Under the hood, these assumptions create a dataset used to derive statistical priors. Prior predictive checks then help analysts compare the priors to their assumptions. In a lab study with 17 participants new to Bayesian analysis, we compare PriorWeaver to a baseline incorporating existing techniques. Compared to the baseline, PriorWeaver gave participants greater control, clarity, and confidence, leading to priors that were better aligned with their expectations.
Protosampling: Enabling Free-Form Convergence of Sampling and Prototyping through Canvas-Driven Visual AI Generation
Authors: Alicia Guo, David Ledo, George W. Fitzmaurice, Fraser Anderson
Published at CHI, 2026

As an emergent process, creativity relies on explorations via sampling and prototyping for problem construction. These activities compile knowledge, provide a context enveloping the solution, and answer questions. With Generative AI, practitioners can go beyond sampling existing media towards instantly generating and remixing new ones. We refer to this convergence as 'protosampling'. Using existing literature we ground a definition for protosampling and operationalize it through Atelier, a canvas-like system that leverages a variety of generative image and video models for visual creation. Atelier: (1) blends the spaces for thinking and creation, where both references and generated assets co-exist in one space, (2) provides various encapsulated technical workflows that focus on the activity at hand, and (3) enables navigating emergence through interactive visualizations, smart search, and collections. Protosampling as a lens reframes creative work to emphasize the process itself and how seemingly disjointed thoughts can tightly interweave into a final solution.
Redundant is Not Redundant: Automating Efficient Categorical Palettes Design Unifying Color & Shape Encodings with CatPAW
Authors: Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri, Danielle Albers Szafir
Published at CHI, 2026

Colors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combinations are limited. This paper presents four crowdsourced experiments evaluating redundant color–shape encodings and identifying high-performing configurations across different category numbers. Results show that redundancy significantly improves accuracy in assessing class-level correlations, with the strongest benefits for 5–8 categories. We also find pronounced interaction effects between colors and shapes, underscoring the need for careful pairing in designing redundant encodings. Drawing on these findings, we introduce a categorical palette design tool that enables designers to construct empirically grounded palettes for effective categorical visualization. Our work advances understanding of categorical perception in data visualization by systematically identifying effective redundant color–shape combinations and embedding these insights into a practical palette design tool.
Revealing the Gap: Visual Comparison of Large-Scale Datasets via Multi-Scale Density Difference Map
Authors: Xinyuan Guo, Xu Zhu, Yilin Ye, Shixia Liu
Published at CHI, 2026

Visual comparison of high-dimensional machine learning datasets helps practitioners identify gaps in data coverage, diagnose distribution shifts, and understand their potential influence on downstream tasks such as classification and object detection. However, the commonly used density map often blurs details and is computationally expensive. We present DiffGrid, a grid-based tool for comparing differences in large datasets. A regularized, grid-based density difference visualization method is developed to enable multi-level analysis of the differences. Interactive zooming and image labels are provided for efficiently exploring differences from overview to detail. We demonstrate the practical value of DiffGrid with two case studies, comparing coresets with full datasets and comparing synthetic infographics with real ones, and validate its effectiveness and usefulness with a quantitative experiment and a user study.
Revealing the Power Dynamics of Collaborative Sense-Making Supported by Participatory Data Physicalization
Authors: Silvia Cazacu, Georgia Panagiotidou 0001, Andrew Vande Moere
Published at CHI, 2026

While it is proven that the individual construction of a data physicalization aids personal sense-making, little is known about how sense-making is negotiated when it is shared by multiple, co-located participants. Since participatory data physicalization can inadvertently prioritize dominant views, we interpreted data feminism principles to design a collaborative physicalization construction process that empowers stakeholders and participants to co-determine how meanings are represented. This process revealed how the interplay of physical and non-physical actions during construction negotiations supported collaborative sense-making among 14 groups of 55 participants during 4 workshops, enabling us to articulate how explicit power is embodied by the physicalization artifact and negotiated between authoring and collaborating participants, and facilitators; whereas tacit power operates through artifact meanings, participant identity and design decisions. By providing one operationalization of data-feminist critique into the form of design requirements, our contributions support the design of more equitable physicalization and visualization construction methods.
Sci-Fi Spark: A Human-AI Co-Creation System for Science Fiction Ideation
Authors: Zhaojun Jiang, Wengteng Cheang, Xuanpei Xu, Haoyu Zuo, Chunyuan Zheng, Liuqing Chen 0002
Published at CHI, 2026

In science fiction writing, ideation demands both novelty to construct fictional worlds and consistency to maintain internal and temporal logic within those worlds. While large language models (LLMs) are increasingly adopted as co-creators, generated ideas often lack surprise and struggle to maintain consistency. Moreover, current interaction paradigms of human-AI co-creation systems fail to support the fragmented and iterative nature of science fiction ideation. To address these challenges, we introduce Sci-Fi Spark, a human-AI co-creation system to support inspiration and organization in the ideation phase. The system features an Ideation Canvas for visualizing relationships between fragmented ideas, a Novelty Generator that applies computational creativity strategies to produce novel worldbuilding inspirations, and a Consistency Generator to produce context-aware storyline suggestions. A technical evaluation and a user study with writers show that Sci-Fi Spark enhances both novelty and consistency, while enabling iterative co-ideation with LLMs.
SCORE: A Framework for Quantifying Diegesis in Situated Visualization for Augmented Reality
Authors: Tarik Hasan, Khalad Hasan, Barrett Ens
Published at CHI, 2026

A central goal of Augmented Reality (AR)-based Situated Visualization (SV) is to seamlessly integrate digital information into its relevant physical context. While existing frameworks describe numerous design dimensions, the field lacks a rigorous model to evaluate this integration. To address this, we introduce SCORE, a framework for quantifying diegesis - a narratology concept describing the extent to which an element belongs to its narrative world. Grounded in a systematic analysis of 50 contemporary SV works, SCORE defines five dimensions of diegesis in SV: Spatial proximity, Concreteness, cOherence, Referential context, and Environmental context. In addition to qualitative comparison, our framework also provides a quantitative measure of diegesis, enabling SCORE to distinguish AR-based SVs that prior models have treated as theoretically equivalent. We validate the framework by demonstrating a consistent correlation between higher scores and positive usability outcomes. Based on these findings, we offer insights for SV designers.
Scrollytelling as an Alternative Format for Privacy Policies
Authors: Gonzalo Gabriel Méndez, Jose Such
Published at CHI, 2026

Privacy policies are long, complex, and rarely read, which limits their effectiveness in informed consent. We investigate scrollytelling, a scroll-driven narrative approach, as a privacy policy presentation format. We built a prototype that interleaves the full policy text with animated visuals to create a dynamic reading experience. In an online study (N = 454), we compared our tool against text, two nutrition-label variants, and a standalone interactive visualization. Scrollytelling improved user experience over text, yielding higher engagement, lower cognitive load, greater willingness to adopt the format, and increased perceived clarity. It also matched other formats on comprehension accuracy and confidence, with only one nutrition-label variant performing slightly better. Changes in perceived understanding, transparency, and trust were small and statistically inconclusive. These findings suggest that scrollytelling can preserve comprehension while enhancing the experience of policy reading. We discuss design implications for accessible policy communication and identify directions for increasing transparency and user trust.
Selecting Tangible Media for Immersive Exploration of Volumetric Scientific Data
Authors: Zhouhao Wu, Huiting Kong, Mingming Zhou, Qichen Liu, Shuai Chen 0001, Chufan Lai, Richen Liu
Published at CHI, 2026

Immersive scientific data exploration faces challenges in precise and efficient interaction. Tangible media offer a potential solution; but designers lack clear guidance on choosing the appropriate physical dimensionality (1D, 2D, or 3D) for different tasks. To address this problem, we present a design space structuring the relationship between the representative techniques on scientific data visualization and exploration, tangible interactions, and media dimensionality. We further developed a prototype to empirically explore these relationships according to our design space. In a controlled user study, we compared 1D, 2D, and 3D tangible media across seven core techniques. The results demonstrated that the 3D media (e.g., a box) were preferred when tasks required manipulating the entire volumetric data and acted as a proxy. Regarding the tasks requiring 2D operations or interior localization, the 2D media (e.g., a card) offered superior performance. For single-parameter techniques like histogram-based filtering, the 1D media (e.g., a pen) were overwhelmingly preferred for their simplicity and perceived ease of use.