DevLan/vispubs huggingface dataset:
Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves
Authors: Cathy Mengying Fang, Phoebe Chua, Samantha W. T. Chan, Joanne Leong, Andria Bao, Pattie Maes
Published at CHI, 2025

Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental imagery to envision ideal selves, but may be less effective for individuals who struggle with visualization. This paper introduces Emotional Self-Voice (ESV), a novel system combining emotionally expressive language models and voice cloning technologies to render customized responses in the user's own voice. We investigate the potential of ESV to nudge individuals towards their ideal selves in a study with 60 participants. Across all three conditions (ESV, text-only, and mental imagination), we observed an increase in resilience, confidence, motivation, and goal commitment, and the ESV condition was perceived as uniquely engaging and personalized. We discuss the implications of designing generated self-voice systems as a personalized behavioral intervention for different scenarios.
Libra: An Interaction Model for Data Visualization
Authors: Yue Zhao 0033, Yunhai Wang, Xu Luo, Yanyan Wang, Jean-Daniel Fekete
Published at CHI, 2025

While existing visualization libraries enable the reuse, extension, and combination of static visualizations, achieving the same for interactions remains nearly impossible. Therefore, we contribute an interaction model and its implementation to achieve this goal. Our model enables the creation of interactions that support direct manipulation, enforce software modularity by clearly separating visualizations from interactions, and ensure compatibility with existing visualization systems. Interaction management is achieved through an instrument that receives events from the view, dispatches these events to graphical layers containing objects, and then triggers actions. We present a JavaScript prototype implementation of our model called Libra.js, enabling the specification of interactions for visualizations created by different libraries. We demonstrate the effectiveness of Libra by describing and generating a wide range of existing interaction techniques. We evaluate Libra.js through diverse examples, a metric-based notation comparison, and a performance benchmark analysis.
LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation
Authors: Vivian Liu, Rubaiat Habib Kazi, Li-Yi Wei, Matthew Fisher, Timothy Langlois, Seth Walker, Lydia B. Chilton
Published at CHI, 2025

Creating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method performs visual analysis, instantiates a design concept, and conducts visual checking to generate animation code. LogoMotion provides novices with code-connected AI editing widgets that help them edit the motion, grouping, and timing of their animation. In a comparison study on 276 animations, LogoMotion was found to produce more content-aware animation than an industry-leading tool. In a user evaluation (n=16) comparing against a prompt-only baseline, these code-connected widgets helped users edit animations with control, iteration, and creative expression.
Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges
Authors: Katerina Batziakoudi, Florent Cabric, Stéphanie Rey, Jean-Daniel Fekete
Published at CHI, 2025

We explore the design of visualizations for values spanning multiple orders of magnitude; we call them Orders of Magnitude Values (OMVs). Visualization researchers have shown that separating OMVs into two components, the mantissa and the exponent, and encoding them separately overcomes limitations of linear and logarithmic scales. However, only a small number of such visualizations have been tested, and the design guidelines for visualizing the mantissa and exponent separately remain under-explored. To initiate this exploration, better understand the factors influencing the effectiveness of these visualizations, and create guidelines, we adopt a multi-stage workflow. We introduce a design space for visualizing mantissa and exponent, systematically generating and qualitatively evaluating all possible visualizations within it. From this evaluation, we derive guidelines. We select two visualizations that align with our guidelines and test them using a crowdsourcing experiment, showing they facilitate quantitative comparisons and increase confidence in interpretation compared to the state-of-the-art.
Making the Write Connections: Linking Writing Support Tools with Writer Needs
Authors: Zixin Zhao, Damien Masson, Young-Ho Kim, Gerald Penn, Fanny Chevalier
Published at CHI, 2025

This work sheds light on whether and how creative writers' needs are met by existing research and commercial writing support tools (WST). We conducted a need finding study to gain insight into the writers' process during creative writing through a qualitative analysis of the response from an online questionnaire and Reddit discussions on r/Writing. Using a systematic analysis of 115 tools and 67 research papers, we map out the landscape of how digital tools facilitate the writing process. Our triangulation of data reveals that research predominantly focuses on the writing activity and overlooks pre-writing activities and the importance of visualization. We distill 10 key takeaways to inform future research on WST and point to opportunities surrounding underexplored areas. Our work offers a holistic and up-to-date account of how tools have transformed the writing process, guiding the design of future tools that address writers' evolving and unmet needs.
OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience
Authors: Pascal Jansen, Mark Colley, Svenja Krauß, Daniel Hirschle, Enrico Rukzio
Published at CHI, 2025

Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.
PAIRcolator: Pair Collaboration for Sensemaking and Reflection on Personal Data
Authors: Di Yan, Jacky Bourgeois, Yen-Chia Hsu, Gerd Kortuem
Published at CHI, 2025

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration—characterized by dyadic comparison and structured roles for questioning and reasoning—has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, PAIRcolator. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.
Paratrouper: Exploratory Creation of Character Cast Visuals Using Generative AI
Authors: Joanne Leong, David Ledo, Thomas Driscoll, Tovi Grossman, George W. Fitzmaurice, Fraser Anderson
Published at CHI, 2025

Great characters are critical to the success of many forms of media, such as comics, games, and films. Designing visually compelling casts of characters requires significant skill and consideration, and there is a lack of specialized tools to support this endeavor. We investigate how AI-driven image-generation techniques can empower creatives to explore a variety of visual design possibilities for individual and groups of characters. Informed by interviews with character designers, Paratrouper is a multi-modal system that enables creating and experimenting with multiple permutations for character casts and visualizing them in various contexts as part of a holistic approach to design. We demonstrate how Paratrouper supports different aspects of the character design process, and share insights from its use by eight creators. Our work highlights the interplay between creative agency and serendipity, as well as the visual interrelationships among character aesthetics.
PiaMuscle: Improving Piano Skill Acquisition by Cost-effectively Estimating and Visualizing Activities of Miniature Hand Muscles
Authors: Ruofan Liu 0001, Yichen Peng, Takanori Oku, Chen-Chieh Liao, Erwin Wu, Shinichi Furuya, Hideki Koike
Published at CHI, 2025

Understanding neuromusculoskeletal mechanisms significantly impacts skill specialization and proficiency. While existing methods can infer large muscle activities during gross motor movements, the estimation of dexterous motor control involving miniature muscles remains underexplored. Targeting the coordinated hand muscles in advanced piano performance, we learn spatiotemporal discrete representations of electromyography (EMG) data and hand postures utilizing a multimodal dataset. Subsequently, we train a precise and cost-effective neural network model. Based on this model, PiaMuscle is introduced to investigate if visualizing muscle activities during piano training enhances piano performance. Quantitative and qualitative results of a user study with highly skilled professional pianists demonstrate that PiaMuscle provides reliable muscle activation data to support and optimize force control. Our research underscores the potential of a naturalistic workflow to estimate small muscles' activities from readily accessible human-centric information and more accurately when combined with tool-centric data, thereby enhancing skill acquisition.
Plume: Scaffolding Text Composition in Dashboards
Authors: Maxim Lisnic, Vidya Setlur, Nicole Sultanum
Published at CHI, 2025

Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a system to help authors craft effective dashboard text. Through a formative review of exemplar dashboards, we created a typology of text parameters and articulated the relationship between visual placement and semantic connections, which informed Plume's design. Plume employs large language models (LLMs) to generate contextually appropriate content and provides guidelines for writing clear, readable text. A preliminary evaluation with 12 dashboard authors explored how assisted text authoring integrates into workflows, revealing strengths and limitations of LLM-generated text and the value of our human-in-the-loop approach. Our findings suggest opportunities to improve dashboard authoring tools by better supporting the diverse roles that text plays in conveying insights.
PrivacyHub: A Functional Tangible and Digital Ecosystem for Interoperable Smart Home Privacy Awareness and Control
Authors: Maximiliane Windl, Philipp Thalhammer, David Müller 0009, Albrecht Schmidt 0001, Sebastian S. Feger
Published at CHI, 2025

Hubs are at the core of most smart homes. Modern cross-ecosystem protocols and standards enable smart home hubs to achieve interoperability across devices, offering the unique opportunity to integrate universally available smart home privacy awareness and control features. To date, such privacy features mainly focus on individual products or prototypical research artifacts. We developed a cross-ecosystem hub featuring a tangible dashboard and a digital web application to deepen our understanding of how smart home users interact with functional privacy features. The ecosystem allows users to control the connectivity states of their devices and raises awareness by visualizing device positions, states, and data flows. We deployed the ecosystem in six households for one week and found that it increased participants' perceived control, awareness, and understanding of smart home privacy. We further found distinct differences between tangible and digital mechanisms. Our findings highlight the value of cross-ecosystem hubs for effective privacy management.
PrivCAPTCHA: Interactive CAPTCHA to Facilitate Effective Comprehension of APP Privacy Policy
Authors: Shuning Zhang, Xin Yi 0001, Shixuan Li, Haobin Xing, Hewu Li
Published at CHI, 2025

Traditional app privacy policies are often lengthy and non-interactive, leading users to skip them and remain uninformed. To address this, we proposed PrivCAP, a technique to enhance user comprehension by presenting policies in a concise, interactive format. PrivCAP adopted a CAPTCHA-based design, requiring users to interact with clickable chunks of concise policy content, thus reducing physical and cognitive load. A formative study (N=38) demonstrated that participants valued informed consent alongside concerns over data collection and sharing, marking the first such evaluation among Chinese users. This study further found a preference for concise visualizations and interactable formats. PrivCAP, leveraging few-shot prompting on Large Language Models (LLMs), accurately translates privacy policies into clickable, chunked formats optimized for smartphone screens. In an evaluation (N=28), PrivCAP outperformed traditional policy presentations in improving user understanding, reducing cognitive load, and maintaining efficiency, with participants favoring its engaging design and reporting more informed decision-making1.
Progression Balancing × Baldur's Gate 3: Insights, Terms and Tools for Multi-Dimensional Video Game Balance
Authors: Johannes Pfau
Published at CHI, 2025

Internal game balancing is one of the major components that affect player experience, as it is responsible for a large share of development time, the majority of game update patches and long-term player satisfaction. This makes tools and methodologies of assessing and advancing game balance a valuable endeavor for industry and academia. During the past decades, scientific research produced numerous outputs to inform and enhance game balancing, yet most of them only adhere to a single dimension of balance: fixed (end-game) scenarios. However, games are usually experienced throughout a continuous spectrum of ever-changing constellations, which should be reflected. Using simulation, game-playing AI, visual analytics and informative metrics, we introduce a methodology and implementation of Progression Balancing, incorporating multi-dimensional game aspects. For the sake of exposition and ecological validity, we applied it in one of the most successful recent games (Baldur's Gate 3), and evaluated its efficacy with help of its player community.
Reciportrait: a Data Humanism Approach for Collaborative Sensemaking of Personal Data
Authors: Di Yan, Chenge Tang, Senthil Chandrasegaran, Gerd Kortuem
Published at CHI, 2025

Data Humanism has gained prominence in personal visualization and Personal Informatics, advocating for a subjective and slow approach to engage with personal data. Collaborative sensemaking has great potential for aiding the understanding of personal data, yet little is known about addressing requirements of structure and coordination when integrating Data Humanism into collaborative visualization. In this paper, we propose design principles for creating both subjective and effective collaborative visualizations, while coordinating the slow sensemaking process and promoting data awareness and communication. We operationalize these principles into a personal visualization toolkit, which we evaluate with an observational study involving 16 university students (8 pairs) analyzing each other's screen-time data. Our findings reveal that implementing the proposed design principles: (1) facilitated data comparison from shared subjective perspectives, (2) helped coordinate sensemaking while allowing time for understanding personal data, and (3) helped the contextualization of data patterns, in turn aiding self-reflection.
Reflecting on Design Paradigms of Animated Data Video Tools
Authors: Leixian Shen, Haotian Li 0001, Yun Wang 0012, Huamin Qu
Published at CHI, 2025

Animated data videos have gained significant popularity in recent years. However, authoring data videos remains challenging due to the complexity of creating and coordinating diverse components (e.g., visualization, animation, audio, etc.). Although numerous tools have been developed to streamline the process, there is a lack of comprehensive understanding and reflection of their design paradigms to inform future development. To address this gap, we propose a framework for understanding data video creation tools along two dimensions: what data video components to create and coordinate, including visual, motion, narrative, and audio components, and how to support the creation and coordination. By applying the framework to analyze 46 existing tools, we summarized key design paradigms of creating and coordinating each component based on the varying work distribution for humans and AI in these tools. Finally, we share our detailed reflections, highlight gaps from a holistic view, and discuss future directions to address them.
RemapVR: An Immersive Authoring Tool for Rapid Prototyping of Remapped Interaction in VR
Authors: Tianren Luo, Tong Wu, Chaoyong Jiang, Xinran Duan, Jiafu Lv, Nianlong Li, Yachun Fan, Teng Han, Feng Tian 0001
Published at CHI, 2025

Remapping techniques in VR such as repositioning, redirection, and resizing have been extensively studied. Still, interaction designers rarely have the opportunity to use them due to high technical and knowledge barriers. In the paper, we extract common features of 24 existing remapping techniques and develop a high-fidelity immersive authoring tool, namely RemapVR, for rapidly building and experiencing prototypes of remapped space properties in VR that are unperceivable or acceptable to users. RemapVR provides designers with a series of functions for editing remappings and visualizing spatial property changes, mapping relationships between real and virtual worlds, sensory conflicts, etc. Designers can quickly build existing remappings via templates, and author new remappings by interactively recording spatial relations between input trajectory in real world and output trajectory in virtual world. User studies showed that the designs of RemapVR can effectively improve designers' authoring experience and efficiency, and support designers to author remapping prototypes that meet scene requirements and provide good user experience.
RidgeBuilder: Interactive Authoring of Expressive Ridgeline Plots
Authors: Shuhan Liu, Yangtian Liu, Junxin Li, Yanwei Huang, Yue Shangguan, Zikun Deng, Di Weng, Yingcai Wu
Published at CHI, 2025

Ridgeline plots are frequently employed to visualize the evolution or distributions of multiple series with a pile of overlapping line, area, or bar charts, highlighting the peak patterns. While traditionally viewed as small multiple visualizations, their ridge-like patterns have increasingly attracted graphic designers to create appealing customized ridgeline plots. However, many tools only support creating basic ridgeline plots and overlook their diverse layouts and styles. This paper introduces a comprehensive design space for ridgeline plots, focusing on their varied layouts and expressive styles. We present RidgeBuilder, an intuitive tool for creating expressive ridgeline plots with customizable layouts and styles. In particular, we summarize three goals for refining the layout of ridgeline plots and propose an optimization method. We assess RidgeBuilder's usability and usefulness through a reproduction study and evaluate the layout optimization algorithm through anonymized questionnaires. The effectiveness is demonstrated with a gallery of ridgeline plots created by RidgeBuilder.
Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations
Authors: Ian Steenstra, Farnaz Nouraei, Timothy W. Bickmore
Published at CHI, 2025

Learning therapeutic counseling involves significant role-play experience with mock patients, with current manual training methods providing only intermittent granular feedback. We seek to accelerate and optimize counselor training by providing frequent, detailed feedback to trainees as they interact with a simulated patient. Our first application domain involves training motivational interviewing skills for counselors. Motivational interviewing is a collaborative counseling style in which patients are guided to talk about changing their behavior, with empathetic counseling an essential ingredient. We developed and evaluated an LLM-powered training system that features a simulated patient and visualizations of turn-by-turn performance feedback tailored to the needs of counselors learning motivational interviewing. We conducted an evaluation study with professional and student counselors, demonstrating high usability and satisfaction with the system. We present design implications for the development of automated systems that train users in counseling skills and their generalizability to other types of social skills training.
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Authors: Sheng Long, Angelos Chatzimparmpas, Emma Alexander, Matthew Kay 0001, Jessica Hullman
Published at CHI, 2025

Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style transfer. We explore the application of such metrics to judgments of visualization similarity. We extend a similarity metric using five ML architectures and three pre-trained weight sets. We replicate results from previous crowdsourced studies on scatterplot and visual channel similarity perception. Notably, our metric using pre-trained ImageNet weights outperformed gradient-descent tuned MS-SSIM, a multi-scale similarity metric based on luminance, contrast, and structure. Our work contributes to understanding how deep-feature-based metrics can enhance similarity assessments in visualization, potentially improving visual analysis tools and techniques. Supplementary materials are available at https://osf.io/dj2ms/.