DevLan/vispubs huggingface dataset:
Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis
Authors: Brian Y. Lim, Joseph P. Cahaly, Chester Y. F. Sng, Adam Chew
Published at CHI, 2025

Many visualizations have been developed for explainable AI (XAI), but they often require further reasoning by users to interpret. Investigating XAI for high-stakes medical diagnosis, we propose improving domain alignment with diagrammatic and abductive reasoning to reduce the interpretability gap. We developed DiagramNet to predict cardiac diagnoses from heart auscultation, select the best-fitting hypothesis based on criteria evaluation, and explain with clinically-relevant murmur diagrams. The ante-hoc interpretable model leverages domain-relevant ontology, representation, and reasoning process to increase trust in expert users. In modeling studies, we found that DiagramNet not only provides faithful murmur shape explanations, but also has better performance than baseline models. We demonstrate the interpretability and trustworthiness of diagrammatic, abductive explanations in a qualitative user study with medical students, showing that clinically-relevant, diagrammatic explanations are preferred over technical saliency map explanations. This work contributes insights into providing domain-aligned explanations for user-centric XAI in complex domains.
Disentangling the Power Dynamics in Participatory Data Physicalisation
Authors: Silvia Cazacu, Georgia Panagiotidou, Thérèse Steenberghen, Andrew Vande Moere
Published at CHI, 2025

Participatory data physicalisation (PDP) is recognised for its potential to support data-driven decisions among stakeholders who collaboratively construct physical elements into commonly insightful visualisations. Like all participatory processes, PDP is however influenced by underlying power dynamics that might lead to issues regarding extractive participation, marginalisation, or exclusion, among others. We first identified the decisions behind these power dynamics by developing an ontology that synthesises critical theoretical insights from both visualisation and participatory design research, which were then systematically applied unto a representative corpus of 23 PDP artefacts. By revealing how shared decisions are guided by different agendas, this paper presents three contributions: 1) a cross-disciplinary ontology that facilitates the systematic analysis of existing and novel PDP artefacts and processes; which leads to 2) six PDP agendas that reflect the key power dynamics in current PDP practice, revealing the diversity of orientations towards stakeholder participation in PDP practice; and 3) a set of critical considerations that should guide how power dynamics can be balanced, such as by reflecting on how issues are represented, data is contextualised, participants express their meanings, and how participants can dissent with flexible artefact construction. Consequently, this study advances a feminist research agenda by guiding researchers and practitioners in openly reflecting on and sharing responsibilities in data physicalisation and participatory data visualisation.
Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis
Authors: Venkatesh Sivaraman, Zexuan Li, Adam Perer
Published at CHI, 2025

Analyzing data subgroups is a common data science task to build intuition about a dataset and identify areas to improve model performance. However, subgroup analysis is prohibitively difficult in datasets with many features, and existing tools limit unexpected discoveries by relying on user-defined or static subgroups. We propose exploratory subgroup analysis as a set of tasks in which practitioners discover, evaluate, and curate interesting subgroups to build understanding about datasets and models. To support these tasks we introduce Divisi, an interactive notebook-based tool underpinned by a fast approximate subgroup discovery algorithm. Divisi's interface allows data scientists to interactively re-rank and refine subgroups and to visualize their overlap and coverage in the novel Subgroup Map. Through a think-aloud study with 13 practitioners, we find that Divisi can help uncover surprising patterns in data features and their interactions, and that it encourages more thorough exploration of subtypes in complex data.
DysVis: A User-Centred Data Visualization System for Dyslexia Pre-screening
Authors: Ka Yan Fung, Lik Hang Lee, Linping Yuan, Kwong Chiu Fung, Kuen Fung Sin, Tze-Leung Rick Lui, Huamin Qu, Shenghui Song 0001
Published at CHI, 2025

Dyslexia is a common neurobiological learning disorder significantly impacting reading, writing, and spelling worldwide. Early identification and intervention are essential, but most pre-screening tools focus on Latin languages, leaving Chinese-speaking students underserved. To address this gap, we conduct semi-structured interviews with special education (special-ed) teachers to gather their needs for dyslexia pre-screening tailored to Chinese contexts. Using their insights, we have developed DysVis, a user-centered data visualization system that combines handwriting analysis, body movement keypoint conversion, and a comprehensive visualization interface. DysVis provides teachers with multi-level visualizations, such as performance overviews, task analyses, handwriting observations, and behavioural insights, enabling them to identify the root causes of learning difficulties. Our evaluations, including case studies, a user study, and expert interviews, demonstrate that DysVis is user-friendly and effective in quickly identifying at-risk students, ultimately enhancing learning outcomes for Chinese-speaking students with dyslexia.
Effects of Alternative Scatterplot Designs on Belief
Authors: Gabriel Strain, Andrew J. Stewart, Caroline Jay, Charlotte Rutherford, Paul A. Warren
Published at CHI, 2025

Viewers tend to underestimate correlation in positively correlated scatterplots. However, systematically changing the size and opacity of scatterplot points can bias estimates upwards, correcting for this underestimation. Here, we examine whether the application of these visualisation techniques goes beyond a simple perceptual effect and could actually influence beliefs about information from trusted news sources. We present a fully-reproducible study in which we demonstrate that scatterplot manipulations that are able to correct for the correlation underestimation bias can also induce stronger levels of belief change compared to conventional scatterplots presenting identical data. Consequently, we show that novel visualisation techniques can be used to drive belief change, and suggest future directions for extending this work with regards to altering attitudes and behaviours.
Exploratory Visual Analysis of Transcripts for Interaction Analysis in Human-Computer Interaction
Authors: Ben Rydal Shapiro, Rogers Hall, Arpit Mathur, Edwin Zhao
Published at CHI, 2025

Transcripts are central to qualitative research in HCI, particularly for researchers using methods of Conversation Analysis (CA) and Interaction Analysis (IA) who study the socially situated nature of human-computer interaction. However, CA and IA researchers continue to highlight the significant need for more dynamic ways to visualize transcripts to support interaction analysis. This need is particularly evident in HCI, where transcripts as a form of data have received little attention. In this article, we make three contributions to HCI research. First, we present Transcript Explorer, an open-source visualization system that integrates three visualization techniques we have developed to interactively visualize transcripts linked to videos: Distribution Diagrams, Turn Charts and Contribution Clouds. Second, we present findings from a qualitative analysis of focus group interviews with three different qualitative research groups who engaged with this system to analyze common transcript data. Finally, we expand upon transcripts as a unique form of data for HCI research and propose directions for future research.
Exploring Design Spaces to Facilitate Household Collaboration for Cohabiting Couples
Authors: Gahyeon Bae, Seo Kyoung Park, Taewan Kim, Hwajung Hong
Published at CHI, 2025

Household collaboration among cohabiting couples presents unique challenges due to the intimate nature of the relationships and the lack of external rewards. Current efficiency-oriented technologies neglect these distinct dynamics. Our study aims to examine the real-world context and underlying needs of couples in their collaborative homemaking. We conducted a 10-day empirical investigation involving six Korean couples, supplemented by a probe approach to facilitate reflection on their current homemaking practices. We identified the requirement for ideal household collaboration as a 'shared ritual for celebratory interaction' and pinpointed the challenges in achieving this goal. We propose three design opportunities for domestic technology to address this gap: strengthening the meaning of housework around family values, supporting recognition of the partner's efforts through visualization, and initiating negotiation through defamiliarization. These insights extend the design considerations for domestic technologies, advocating for a broader understanding of the values contributing to satisfactory homemaking activities within the household.
Exploring Mobile Touch Interaction with Large Language Models
Authors: Tim Zindulka, Jannek Maximilian Sekowski, Florian Lehmann, Daniel Buschek
Published at CHI, 2025

Interacting with Large Language Models (LLMs) for text editing on mobile devices currently requires users to break out of their writing environment and switch to a conversational AI interface. In this paper, we propose to control the LLM via touch gestures performed directly on the text. We first chart a design space that covers fundamental touch input and text transformations. In this space, we then concretely explore two control mappings: spread-to-generate and pinch-to-shorten, with visual feedback loops. We evaluate this concept in a user study (N=14) that compares three feedback designs: no visualisation, text length indicator, and length + word indicator. The results demonstrate that touch-based control of LLMs is both feasible and user-friendly, with the length + word indicator proving most effective for managing text generation. This work lays the foundation for further research into gesture-based interaction with LLMs on touch devices.
Exploring the Design Space of Privacy-Driven Adaptation Techniques for Future Augmented Reality Interfaces
Authors: Shwetha Rajaram, Macarena Peralta, Janet G. Johnson, Michael Nebeling
Published at CHI, 2025

Modern augmented reality (AR) devices with advanced display and sensing capabilities pose significant privacy risks to users and bystanders. While previous context-aware adaptations focused on usability and ergonomics, we explore the design space of privacy-driven adaptations that allow users to meet their dynamic needs. These techniques offer granular control over AR sensing capabilities across various AR input, output, and interaction modalities, aiming to minimize degradations to the user experience. Through an elicitation study with 10 AR researchers, we derive 62 privacy-focused adaptation techniques that preserve key AR functionalities and classify them into system-driven, user-driven, and mixed-initiative approaches to create an adaptation catalog. We also contribute a visualization tool that helps AR developers navigate the design space, validating its effectiveness in design workshops with six AR developers. Our findings indicate that the tool allowed developers to discover new techniques, evaluate tradeoffs, and make informed decisions that balance usability and privacy concerns in AR design.
From Concept to Clinic: Multidisciplinary Design, Development, and Clinical Validation of Augmented Reality-Assisted Open Pancreatic Surgery
Authors: Hamraz Javaheri, Omid Ghamarnejad, Paul Lukowicz, Gregor Alexander Stavrou, Jakob Karolus
Published at CHI, 2025

Wearable augmented reality (AR) systems have significant potential to enhance surgical outcomes through in-situ visualization of patient-specific data. Yet, efforts to develop AR-based systems for open surgery have been limited, lacking comprehensive interdisciplinary research and actual clinical evaluations in real surgical environments. Our research addresses this gap by presenting a user-centered design and development process of ARAS, an AR assistance for open pancreatic surgery. ARAS provides in-situ visualization of critical structures, such as the vascular system and the tumor, while offering a robust dual-layer registration method ensuring accurate registration during relevant phases of the surgery. We evaluated ARAS in clinical trials of 20 patients with pancreatic tumors. Accuracy validation and postoperative surgeon interviews confirmed its successful deployment, supporting surgeons in vascular localization and critical decision-making. Our work showcases AR's potential to fundamentally transform procedures for complex surgical operations, advocating a research shift toward ecological validation in open surgery.
GenPara: Enhancing the 3D Design Editing Process by Inferring Users' Regions of Interest with Text-Conditional Shape Parameters
Authors: Jiin Choi 0001, Seung Won Lee, Kyung Hoon Hyun
Published at CHI, 2025

In 3D design, specifying design objectives and visualizing complex shapes through text alone proves to be a significant challenge. Although advancements in 3D GenAI have significantly enhanced part assembly and the creation of high-quality 3D designs, many systems still to dynamically generate and edit design elements based on the shape parameters. To bridge this gap, we propose GenPara, an interactive 3D design editing system that leverages text-conditional shape parameters of part-aware 3D designs and visualizes design space within the Exploration Map and Design Versioning Tree. Additionally, among the various shape parameters generated by LLM, the system extracts and provides design outcomes within the user's regions of interest based on Bayesian inference. A user study (N = 16) revealed that GenPara enhanced the comprehension and management of designers with text-conditional shape parameters, streamlining design exploration and concretization. This improvement boosted efficiency and creativity of the 3D design process.
GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents
Authors: Ruishi Zou, Yinqi Tang, Jingzhu Chen, Siyu Lu, Yan Lu, Yingfan Yang, Chen Ye 0002
Published at CHI, 2025

Data-rich documents are ubiquitous in various applications, yet they often rely solely on textual descriptions to convey data insights. Prior research primarily focused on providing visualization-centric augmentation to data-rich documents. However, few have explored using automatically generated word-scale visualizations to enhance the document-centric reading process. As an exploratory step, we propose GistVis, an automatic pipeline that extracts and visualizes data insight from text descriptions. GistVis decomposes the generation process into four modules: Discoverer, Annotator, Extractor, and Visualizer, with the first three modules utilizing the capabilities of large language models and the fourth using visualization design knowledge. Technical evaluation including a comparative study on Discoverer and an ablation study on Annotator reveals decent performance of GistVis. Meanwhile, the user study (N=12) showed that GistVis could generate satisfactory word-scale visualizations, indicating its effectiveness in facilitating users' understanding of data-rich documents (+5.6% accuracy) while significantly reducing their mental demand (p=0.016) and perceived effort (p=0.033).
HaptiCoil: Soft Programmable Buttons with Hydraulically Coupled Haptic Feedback and Sensing
Authors: Jung-Hwan Youn, Seung Heon Lee, Craig Shultz
Published at CHI, 2025

We present HaptiCoil, an embedded system and interaction method for prototyping low-cost, compact, and customizable wide bandwidth (1-500 Hz) soft haptic buttons. HaptiCoil devices are built using mass-produced, waterproof planar micro-speakers which are adapted to direct energy to the skin using a novel hydraulic coupling mechanism. They can sense force input, using a measurement of self-inductance, and provide output in a single package, yielding a flexible all-in-one button solution. Our devices offer a wider perceptual range of tactile stimuli than industry standard approaches, while maintaining comparable power threshold levels (typical threshold under 40 mW). We detail the construction and underlying principles of our approach, as well as an extensive physical quantification of both input and output. We share psychophysical data on device bandwidth, and show three illustrative examples of how HaptiCoil buttons can implemented in use cases such as spatial computing, digital inking, and remote control.
How Do People Perceive Bundling? An Experiment
Authors: Markus Wallinger, Osman Akbulut, Kabir Ahmed Rufai, Helen C. Purchase, Daniel Archambault
Published at CHI, 2025

We present an exploratory study on how people perceive visualizations of spatial social networks generated by edge bundling algorithms. Although these algorithms successfully minimize clutter in node-link diagrams, they do so through various methods that can sometimes create false connections between nodes. We conducted a qualitative experiment involving participants with technical expertise but no prior knowledge of edge bundling algorithms. Participants described their perceptions of both bundled and straight-line visualizations in open-ended tasks. Analysis of their annotations and transcripts revealed a general preference for bundled visualizations. However, when it came to false connections, participants tended to follow them in tightly bundled diagrams while also vocalizing that these drawings were more ambiguous. The routing of bundles influenced the perception of clusters and participants assigned more or fewer nodes to the clusters, depending on the routing of bundles. Participants' unfamiliarity with the dataset led them to use analogies to describe the bundled drawings, potentially adding perceived semantic meaning to the data.
How Scientists Use Large Language Models to Program
Authors: Gabrielle O'Brien
Published at CHI, 2025

Scientists across disciplines write code for critical activities like data collection and generation, statistical modeling, and visualization. As large language models that can generate code have become widely available, scientists may increasingly use these models during research software development. We investigate the characteristics of scientists who are early-adopters of code generating models and conduct interviews with scientists at a public, research-focused university. Through interviews and reviews of user interaction logs, we see that scientists often use code generating models as an information retrieval tool for navigating unfamiliar programming languages and libraries. We present findings about their verification strategies and discuss potential vulnerabilities that may emerge from code generation practices unknowingly influencing the parameters of scientific analyses.
How Visualization Designers Perceive and Use Inspiration
Authors: Ali Baigelenov, Prakash Shukla, Paul Parsons
Published at CHI, 2025

Inspiration plays an important role in design, yet its specific impact on data visualization design practice remains underexplored. This study investigates how professional visualization designers perceive and use inspiration in their practice. Through semi-structured interviews, we examine their sources of inspiration, the value they place on them, and how they navigate the balance between inspiration and imitation. Our findings reveal that designers draw from a diverse array of sources, including existing visualizations, real-world phenomena, and personal experiences. Participants describe a mix of active and passive inspiration practices, often iterating on sources to create original designs. This research offers insights into the role of inspiration in visualization practice, the need to expand visualization design theory, and the implications for the development of visualization tools that support inspiration and for training future visualization designers.
IdeationWeb: Tracking the Evolution of Design Ideas in Human-AI Co-Creation
Authors: Hanshu Shen, Lyukesheng Shen, Wenqi Wu, Kejun Zhang
Published at CHI, 2025

Due to the remarkable content generation capabilities, large language models (LLMs) have demonstrated potential in supporting early-stage conceptual design. However, current interaction paradigms often struggle to effectively facilitate multi-round idea exploration and selection, leading to random outputs, unclear iterations, and cognitive overload. To address these challenges, we propose a human-AI co-ideation framework aimed at tracking the evolution of design ideas. This framework leverages a structured idea representation, an analogy-based reasoning mechanism and interactive visualization techniques. It guides both designers and AI to systematically explore design spaces. We also develop a prototype system, IdeationWeb, which integrates an intuitive, mind map-like visual interface and interactive methods to support co-ideation. Our user study validates the framework's feasibility, demonstrating enhanced collaboration and creativity between humans and AI. Furthermore, we identified collaborative design patterns from user behaviors, providing valuable insights for future human-AI interaction design.
Interactive Debugging and Steering of Multi-Agent AI Systems
Authors: Will Epperson, Gagan Bansal, Victor C. Dibia, Adam Fourney, Jack Gerrits, Erkang (Eric) Zhu, Saleema Amershi
Published at CHI, 2025

Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users. What challenges do developers face when trying to build and debug these AI agent teams? In formative interviews with five AI agent developers, we identify core challenges: difficulty reviewing long agent conversations to localize errors, lack of support in current tools for interactive debugging, and the need for tool support to iterate on agent configuration. Based on these needs, we developed an interactive multi-agent debugging tool, AGDebugger, with a UI for browsing and sending messages, the ability to edit and reset prior agent messages, and an overview visualization for navigating complex message histories. In a two-part user study with 14 participants, we identify common user strategies for steering agents and highlight the importance of interactive message resets for debugging. Our studies deepen understanding of interfaces for debugging increasingly important agentic workflows.
Intra, Extra, Read all about it! How Readers Interpret Visualizations with Intra- and Extratextual Information
Authors: Alyxander Burns, Claudia Gonzalez-Vazquez
Published at CHI, 2025

A reader's interpretation of a visualization is informed by both intratextual information (the information directly represented in the visualization) and extratextual information (information not represented in the visualization but known by the reader). Yet, we do not know what kinds of intra- and extratextual information readers use or how they integrate it to form meaning. To explore this area, we conducted semi-structured interviews about four real-world visualizations. We used thematic analysis to understand the types of information that participants used and diffractive reading to reveal how participants blended intra- and extratextual information. Our thematic analysis showed that participants utilized a broad assortment of information from both expected and unexpected sources. Additionally, our diffractive reading exposed three ways that participants incorporated intra- and extratextual information: to decide what to look at, to make (in)accurate assumptions about what the visualization showed, and to discover insights beyond what was directly encoded.