DevLan/vispubs huggingface dataset:
From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model
Authors: Yongsu Ahn, Lejun R. Liao, Benjamin Bach, Nam Wook Kim
Published at CHI, 2026

Design feedback helps practitioners improve their artifacts while also fostering reflection and design reasoning. Large Language Models (LLMs) such as ChatGPT can support design work, but often provide generic, one-off suggestions that limit reflective engagement. We investigate how to guide LLMs to act as design mentors by applying the Cognitive Apprenticeship Model, which emphasizes demonstrating reasoning through six methods: modeling, coaching, scaffolding, articulation, reflection, and exploration. We operationalize these instructional methods through structured prompting and evaluate them in a within-subjects study with data visualization practitioners. Participants interacted with both a baseline LLM and an instructional LLM designed with cognitive apprenticeship prompts. Surveys, interviews, and conversational log analyses compared experiences across conditions. Our findings show that cognitively informed prompts elicit deeper design reasoning and more reflective feedback exchanges, though the baseline is sometimes preferred depending on task types or experience levels. We distill design considerations for AI-assisted feedback systems that foster reflective practice.
From Invisible to Actionable: Augmented Reality Interactions with Indoor CO2
Authors: Prasenjit Karmakar, Manjeet Yadav, Swayanshu Rout, Swadhin Pradhan, Sandip Chakraborty 0001
Published at CHI, 2026

Indoor carbon dioxide (CO2) can rapidly accumulate to form invisible pollution hotspots, posing significant health risks due to its odorless and colorless nature. Despite growing interest in wearable or stationary sensors for pollutant detection, effectively visualizing CO2 levels and engaging individuals remains an ongoing challenge. In this paper, we develop a portable wrist-sized pollution sensor that detects CO2 in real time at any indoor location and reveals CO2 bubbles by highlighting sudden spikes. In order to promote better ventilation habits and user awareness, we also develop a smartphone-based augmented reality (AR) game for users to locate and disperse these high-CO2 zones. A user study with 35 participants demonstrated increased engagement and heightened understanding of CO2's health impacts. Our system's usability evaluations yielded a median score of 1.88, indicating its strong practicality.
From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization
Authors: Mehul Parmar, Chaklam Silpasuwanchai
Published at CHI, 2026

As AI systems increasingly mediate negotiations, understanding how the number of negotiated issues impacts human performance is crucial for maintaining human agency. We designed a human–AI negotiation case study in a realistic property rental scenario, varying the number of negotiated issues; empirical findings show that without support, performance stays stable up to three issues but declines as additional issues increase cognitive load. To address this, we introduce a novel uncertainty-based visualization driven by Bayesian estimation of agreement probability. It shows how the space of mutually acceptable agreements narrows as negotiation progresses, helping users identify promising options. In a within-subjects experiment (N=32), it improved human outcomes and efficiency, preserved human control, and avoided redistributing value. Our findings surface practical limits on the complexity people can manage in human–AI negotiation, advance theory on human performance in complex negotiations, and offer validated design guidance for interactive systems.
From Squishing to Meaning: Exploring Data Physicalization Through Children's Embodied Experiences
Authors: Andrés Alberto Ramírez-Duque, Dushani Perera, Dorsey B. Kaufmann, Ayça Atabey, Uta Hinrichs, Andrew Manches, Stephen Anthony Brewster
Published at CHI, 2026

Data physicalization is a promising approach for empowering children to understand and enjoy their own data. However, it relies on embodied metaphors to convey information effectively. This paper explores how to elicit children's embodied experiences using a set of shape-changing objects that can inform the design of dynamic physicalizations. We propose a set of auxetic metamaterials, which can bend, twist, scale and shear. Following principles of tangible interaction, we conducted a study with 59 children who participated in four movement-based games before being introduced to the collection of shape-changing tangibles. Children expressed metaphors based on these activities related to concepts such as containers, rhythm, resistance, and semantic analogies, which we categorised into embodied schemas. Our findings reveal that characteristics of the shape-changing tangibles aid children in connecting their bodily experiences to dynamic transformations. Translating these insights into idea sketches, we outline how to tailor these affordable shape-changing mechanisms into usable prototypes.
From Touch to Change: Understanding Public Engagement in Data Physicalization for Social Good
Authors: Yechun Peng, Runxi Wu, Nan Cao 0001, Yang Shi 0007
Published at CHI, 2026

Data physicalization, which encodes data in physical form, has been increasingly used to engage the public with issues of social good. While public engagement is often invoked as a motivation or expected outcome, it has not been systematically examined as a design objective. This gap raises two key challenges: what characterizes engagement in data physicalization for social good (Phys4Good), and how it can be effectively designed. In this work, we address these challenges by first curating a corpus of 45 Phys4Good projects and deriving a design space structured around a modified three-act framework comprising Stage, Encounter, and Impact. We then conducted semi-structured interviews with designers of eight projects to identify recurring challenges and strategies for fostering engagement. Finally, we demonstrated the effectiveness of our design space and strategies through a case study, which showed that they can guide designers in structuring engagement, anticipating barriers, and creating more impactful Phys4Good experiences.
GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users
Authors: Chu Li 0001, Rock Yuren Pang, Arnavi Chheda-Kothary, Ather Sharif, Henok Assalif, Jeffrey Heer, Jon E. Froehlich
Published at CHI, 2026

Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.
Glass Chirolytics: Reciprocal Compositing and Shared Gestural Control for Face-to-Face Collaborative Visualization at a Distance
Authors: Dion Barja, Matthew Brehmer
Published at CHI, 2026

Videoconference conversations about data often entail screen sharing visualization artifacts, in which nonverbal communication goes largely ignored. Beyond presentation use cases, conversations supported by visualization also arise in collaborative decision making, technical interviews, and tutoring: use cases that benefit from participants being able to see one another as they exchange questions about the data. In this paper, we employ a reciprocal compositing of visualization and interface widgets over the mirrored video of one's conversation partner, suggestive of a pane of glass, in which both parties can simultaneously manipulate composited elements via bimanual gestures. We demonstrate our approach with implementations of several visualization interfaces spanning the aforementioned use cases, and we evaluate our approach in a study (N = 16) comparing it to videoconferencing while using a mouse to interact with a collaborative web application. Our findings suggest that our approach promotes feelings of presence and mutual awareness of analytical intent.
Haptixel: Encoding Data through Cutaneous Force-based Encountered-Type Fingertip Haptics
Authors: Elodie Bouzbib, Louis Badr, Claudio Pacchierotti, Anatole Lécuyer, Arnaud Prouzeau
Published at CHI, 2026

Data visualization benefits from non-visual cues to enable people to understand information by engaging with it through multimodality, yet most approaches rely on cumbersome technologies or large scale artifacts, making them difficult to adapt to dynamic or complex datasets. In this paper, we explore the use of cutaneous haptics as a lightweight quantitative channel for visualization tasks, allowing users to feel data and interact with it dynamically. We present Haptixel, an open-source DIY encountered-type wearable providing force-feedback on the users' fingertips' pulp. We propose an interaction framework illustrating how Haptixel can be used to complement visualization tasks through combinations of force levels and contact types. We evaluate our approach in a pixel-art-like VR user study (n=16) where pixels color/height are associated to forces as a univariate value mapping. Results show that participants can retrieve information with Haptixel, and significantly discriminate 3D-data with at least four levels of forces; suggesting that cutaneous force-feedback can function for quantitative distinctions in visualization tasks.
HieraVisVR: Hierarchical Visual Analytics for Motion-Centric VR Playtesting
Authors: Yongqi Zhang, Erdem Murat, Liuchuan Yu, Haikun Huang, Minsoo Choi 0001, Christos Mousas, Lap-Fai Yu
Published at CHI, 2026

Playtesting is widely used in the game industry to identify design flaws and evaluate player experience, yet little research explores how to effectively visualize and analyze playtesting data. This challenge is particularly pronounced in motion-based VR games, which involve physical movements and interactions tracked through multimodal inputs, resulting in complex multidimensional data. To better understand the challenges designers face, we conducted a formative study with 30 practitioners in the VR domain to characterize playtesting workflows and associated tasks. Based on these findings, we present HieraVisVR, a hierarchical visual analytics framework that incorporates body-motion-related data to help designers identify player behaviors and critical game moments, simplifying their workflow. We demonstrate the applicability of HieraVisVR in three different applications and evaluate our system with playtesting experts through an analysis of motion-based game data. The study results suggest that our system enhances playtesters' understanding of the gameplay and improves their data analysis workflow.
How Do Operators Use Network Diagrams? Characterising Visualisation Tasks in an Energy Control Room
Authors: Merry Kim Hoang, Sarah Goodwin, Roger Dargaville, Tim Dwyer
Published at CHI, 2026

Decision-making in energy control rooms relies on visualising complex, dynamic networks through single-line diagrams (SLDs), yet the tasks these views support remain under-characterised. From a systematic review of 42 papers, we coded 202 tasks using Munzner's WHY–WHAT–HOW framework and Lee et al.'s graph-task taxonomy. Analysis identified six task themes coalescing into five archetypes: topology exploration using shape/orientation; line-attribute comparison through size/alignment; temporal evolution via animation; alarm triage using colour/luminance; and overview synthesis through coordinated views. Assessment with 42 field tasks from real control room observations demonstrated that the archetypes show coverage, clean boundaries, and distinctive visual channels. Our findings reveal critical needs for semantic zoom, temporal continuity, and adaptive view coordination in control room design. Beyond infrastructure domains, this work highlights fundamental visualisation challenges in representing dynamic topologies, encoding multiple attributes in dense configurations, and managing attention across views. Further study should balance geographic reality with operational clarity.
Human-AI Interaction for Time-Critical Sensemaking in Missing Persons Investigations
Authors: Pola Zuzanna Labedzka, Dorian Peters, John J. Dudley, Miri Zilka
Published at CHI, 2026

Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.
HyperMOOC: Augmenting MOOC Videos with Concept-based Embedded Visualizations
Authors: Li Ye, Lei Wang 0194, Lihong Cai, Ruiqi Yu, Yong Wang 0021, Yigang Wang, Wei Chen 0001, Zhiguang Zhou
Published at CHI, 2026

Massive Open Online Courses (MOOCs) have become increasingly popular worldwide. However, learners primarily rely on watching videos, easily losing knowledge context and reducing learning effectiveness. We propose HyperMOOC, a novel approach augmenting MOOC videos with concept-based embedded visualizations to help learners maintain knowledge context. Informed by expert interviews and literature review, HyperMOOC employs multi-glyph designs for different knowledge types and multi-stage interactions for deeper understanding. Using a timeline-based radial visualization, learners can grasp cognitive paths of concepts and navigate courses through hyperlink-based interactions. We evaluated HyperMOOC through a user study with 36 MOOC learners and interviews with two instructors. Results demonstrate that HyperMOOC enhances learners' learning effect and efficiency on MOOCs, with participants showing higher satisfaction and improved course understanding compared to traditional video-based learning approaches.
HyPockeTuner: Bringing Hyperparameter Optimization to Mobile Devices
Authors: Donghee Hong, Bongshin Lee, Jinwook Seo, Jaemin Jo
Published at CHI, 2026

Hyperparameter optimization (HPO) is a long-running process that can span hours or even days. While recent Human-in-the-Loop HPO systems enable monitoring and steering of the process, they are typically designed for desktop environments, which limits their effectiveness in managing prolonged experiments in practice. To address these limitations, we present HyPockeTuner, an interactive mobile system that enables users to monitor, steer, and reflect on HPO experiments anytime, anywhere from smartphones. Its mobile-tailored interface supports tracking experiment history and visualizing the relationship between user interventions and performance changes. HyPockeTuner also employs a notification workflow that alerts users to important events, reducing the burden of constant monitoring while enabling timely interventions. In a pilot study, we validated that users could readily identify critical events, such as performance improvements and intervention points, through our visualization. Furthermore, two five-day deployment studies with follow-up reflection sessions demonstrated that users could integrate experiment management into their daily routines and reflect on past decisions, generating insights for future improvement.
Interaction Through Instruments: Extending Surgical Instruments as Interaction Devices
Authors: Nour Karoui, Isabelle Bloch, Ignacio Avellino
Published at CHI, 2026

Interaction while performing physical tasks is inherently challenging, as both hands are fully engaged. In Minimally-Invasive Surgery (MIS), for instance, navigating images requires either delegation, causing frustration and delays, or hand de-sterilization, increasing risk. We introduce Interaction Through Instruments, an interaction paradigm in which task instruments become interaction devices. To design this technique in MIS, we first conduct a survey (N=23) identifying intraoperative needs, interaction strategies and workarounds, and persistent challenges. Then, through five participatory design workshops (N=10), we identify challenges in blending a user interface into views of a physical space, informing the design of InteractOR, a system that combines surgical instrument segmentation with pinch-gesture recognition to enable interaction within the surgical view. Finally, in a Comparative Structured Observation study (N=12) we compare two visualization strategies (side-by-side and overlay) against delegation, showing that interaction through instruments can reduce focus shifts, increase efficiency, and foster autonomy.
Interactive Explainable Ranking
Authors: Chao Zhang 0082, Abe Davis
Published at CHI, 2026

We propose an interactive decision-making tool for discovering and exploring explainable rankings for a given set of choices (e.g., job offers, vacation destinations, award candidates). We define an explainable ranking as an ordering of choices based on some consistent weighting of measured criteria. Our tool is designed to help users explore different orderings, criteria, and criterion weights in search of an explainable ranking that reflects their own personal preferences. To achieve this, we combine visualization, optimization, and (optionally) the integration of AI to help users identify and correct or explain inconsistencies in their evaluation of different choices. Through user experiments, we demonstrate that our tool leads to more consistent explainable rankings with greater user confidence.
Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics
Authors: Srishti Palani, Vidya Setlur
Published at CHI, 2026

Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiring programming expertise, overlooking real-world complexity, and lacking interpretable metrics for multi-format (visualizations and text) outputs. Through interviews with 22 CVA developers and 16 end-users, we identified use cases, evaluation criteria and workflows. We present Lexara, a user-centered evaluation toolkit for CVA that operationalizes these insights into: (i) test cases spanning real-world scenarios; (ii) interpretable metrics covering visualization quality (data fidelity, semantic alignment, functional correctness, design clarity) and language quality (factual grounding, analytical reasoning, conversational coherence) using rule-based and LLM-as-a-Judge methods; and (iii) an interactive toolkit enabling experimental setup and multi-format and multi-level exploration of results without programming expertise. We conducted a two-week diary study with six CVA developers, drawn from our initial cohort of 22. Their feedback demonstrated Lexara's effectiveness for guiding appropriate model and prompt selection.
Living with Data: Exploring Physicalization Approaches to Sedentary Behavior Intervention for Older Adults in Everyday Life
Authors: Siying Hu, Zhenhao Zhang
Published at CHI, 2026

Sedentary behavior is a critical health risk for older adults. Although digital interventions are widely available, they primarily rely on screen-based notifications that can feel clinical or cognitively demanding, and are thus often ignored over time. This paper presents a three-phase Research through Design methodology to explore data physicalization approaches that ambiently represent sedentary data patterns using decor artifacts in older adults' homes. These artifacts transformed abstract data into aesthetic, evolving forms that became part of the domestic landscape. Our research revealed how these physicalizations fostered self-reflection, family conversations, and encouraged active lifestyles. We demonstrate how qualities like aesthetic ambiguity and slow revelation can empower older adults, fostering a reflective relationship with their well-being. Ultimately, we argue that creating data physicalizations for older adults necessitates a shift from merely informing users to enabling them to live with and through their data.
MarioChart: Autonomous Tangibles as Active Proxy Interfaces for Embodied Casual Data Exploration
Authors: Shaozhang Dai, Kadek Ananta Satriadi, Jim Smiley, Barrett Ens, Lonni Besançon, Tim Dwyer
Published at CHI, 2026

We introduce the notion of an Active Proxy interface, i.e. tangible models as proxies for physical data referents, supporting interactive exploration of data through active manipulation. We realise an active proxy data visualisation system, "MarioChart", using robot carts relocating themselves on a tabletop, e.g., to align with their data referents in a map or other visual layout. We consider a casual-data exploration scenario involving a multivariate campus sustainability dataset, using scale models as proxies for their physical building data referents. Our empirical study (n=12) compares active proxy use with conventional tablet interaction, finding that our active proxy system enhances short-term spatial memory of data and enables faster completion of certain data analytic tasks. It shows no significant differences compared to traditional touchscreens in long-term memory, physical fatigue, mental workload, or user engagement. Our study offers an initial baseline for active proxy techniques and advances understanding of tangible interfaces in situated data visualisation.
Meme, Myself and AR: Exploring Memes Sharing in Face-to-face Conversation using Augmented Reality
Authors: Yanni Mei, Samuel Wendt, Florian Müller 0003, Jan Gugenheimer
Published at CHI, 2026

Internet memes are central to online communication, yet their visual humor is often lost in face-to-face (F2F) conversations. Augmented reality (AR) offers new ways to bring memes into F2F interactions, but it is unclear how memes can be integrated into F2F conversations using AR, and how they impact conversational dynamics. We surveyed meme users (N=29) to understand motivations and challenges in visualising memes in F2F conversations. With these insights, we developed an AR meme-sharing prototype and invited 12 pairs of friends to design AR visualizations for their memes and use them in conversations. Our analysis reveals two AR-unique visualizations: merging memes with one's body (The-Meme-On-Me) and situating oneself in meme environment (Me-In-The-Meme). We observed two integration patterns: using speech as setup before a meme punchline, and showing memes simultaneously with speech to amplify humor. We report users' reactions toward AR memes, showing how it enables playful social interaction.