DevLan/vispubs huggingface dataset:
AIFiligree: A Generative AI Framework for Designing Exquisite Filigree Artworks
Authors: Ye Tao 0001, Xiaohui Fu, Jiaying Wu, Ze Bian, Aiyu Zhu, Qi Bao, Weiyue Zheng, Yubo Wang 0009, Bin Zhu 0013, Cheng Yang 0014, Chuyi Zhou
Published at CHI, 2025

Filigree art, which represents typical intricate metalwork, has been captivating audiences worldwide with its delicate lace-like patterns and interwoven metal wires' refined aesthetics. Particularly, Chinese Intangible Cultural Heritage filigree craftsmanship has a unique aesthetic value in fine patterns and complex three-dimensional shapes. However, designing and creating filigree artworks is a labor-intensive and technically complex task and often requires extensive training and a deep understanding of the craft, which limits its design aesthetic and cultural continuity. Aiming to overcome these challenges, this study proposes an artificial intelligence (AI) -aided method that uses AI-generated content (AIGC) technology to accelerate the visualization process of this time-consuming and intricate craft by investigating the role of AI in craft design. First, a comprehensive study of filigree art culture is conducted to identify more than ten historic filigree techniques to obtain AI opportunities. Then, an AI-powered framework called AIFiligree is developed by optimizing culture-based labels and training parameters, enabling the generation of highly authentic fine filigree structures. Further, user workflows are introduced to support diverse design scenarios. Through user studies involving 22 filigree experts and 16 designers, we finally gained insights into AI's opportunities and challenges in cultural learning, expression, and design.
An Investigation of Interaction and Information Needs for Protocol Reverse Engineering Automation
Authors: Samantha Katcher, James Mattei, Jared Chandler, Daniel Votipka
Published at CHI, 2025

Protocol reverse engineering (ProtocolREing) consists of taking streams of network data and inferring the communication protocol. ProtocolREing is critical task in malware and system security analysis. Several ProtocolREing automation tools have been developed, however, in practice, they are not used because they offer limited interaction. Instead, reverse engineers (ProtocolREs) perform this task manually or use less complex visualization tools. To give ProtocolREs the power of more complex automation, we must first understand ProtocolREs processes and information and interaction needs to design better interfaces. We interviewed 16 ProtocolREs, presenting a paper prototype ProtocolREing automation interface, and ask them to discuss their approach to ProtocolREing while using the tool and suggest missing information and interactions. We designed our prototype based on existing ProtocolREing tool features and prior reverse engineering research's usability guidelines. We found ProtocolREs follow a flexible, hypothesis-driven process and identified multiple information and interaction needs when validating the automation's inferences. We provide suggestions for future interaction design.
ARticulate: Interactive Visual Guidance for Demonstrated Rotational Degrees of Freedom in Mobile AR
Authors: Nhan (Nathan) Tran, Ethan Yang, Abe Davis
Published at CHI, 2025

Mobile Augmented Reality (AR) offers a powerful way to provide spatially-aware guidance for real-world applications. In many cases, these applications involve the configuration of a camera or articulated subject, asking users to navigate several spatial degrees of freedom (DOF) at once. Most guidance for such tasks relies on decomposing available DOF into subspaces that can be more easily mapped to simple 1D or 2D visualizations. Unfortunately, different factorizations of the same motion often map to very different visual feedback, and finding the factorization that best matches a user's intuition can be difficult. We propose an interactive approach that infers rotational degrees of freedom from short user demonstrations. Users select one or two DOFs at a time by demonstrating a small range of motion, which we use to learn a rotational frame that best aligns with user control of the object. We show that deriving visual feedback from this inferred learned rotational frame leads to improved task completion times on 6DOF guidance tasks compared to standard default reference frames used in most mixed reality applications.
Augmented Journeys: Interactive Points of Interest for In-Car Augmented Reality
Authors: Robin Connor Schramm, Ginevra Fedrizzi, Markus Sasalovici, Jann Philipp Freiwald, Ulrich Schwanecke
Published at CHI, 2025

As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experiences by enabling interaction with the environment through NDRTs using world-fixed Points of Interest (POIs). However, the effectiveness of existing interaction techniques and visualization methods for in-car AR remains unclear. Based on a survey (N=110) and a pre-study (N=10), we developed an interactive in-car AR system using a video see-through head-mounted display to engage with POIs via eye-gaze and pinch. Users could explore passed and upcoming POIs using three visualization techniques: List, Timeline, and Minimap. We evaluated the system's feasibility in a field study (N=21). Our findings indicate general acceptance of the system, with the List visualization being the preferred method for exploring POIs. Additionally, the study highlights limitations of current AR hardware, particularly the impact of vehicle movement on 3D interaction.
AVEC: An Assessment of Visual Encoding Ability in Visualization Construction
Authors: Lily W. Ge, Yuan Cui, Matthew Kay 0001
Published at CHI, 2025

Visualization literacy is the ability to both interpret and construct visualizations. Yet existing assessments focus solely on visualization interpretation. A lack of construction-related measurements hinders efforts in understanding and improving literacy in visualizations. We design and develop avec, an assessment of a person's visual encoding ability—a core component of the larger process of visualization construction—by: (1) creating an initial item bank using a design space of visualization tasks and chart types, (2) designing an assessment tool to support the combinatorial nature of selecting appropriate visual encodings, (3) building an autograder from expert scores of answers to our items, and (4) refining and validating the item bank and autograder through an analysis of test tryout data with 95 participants and feedback from the expert panel. We discuss recommendations for using avec, potential alternative scoring strategies, and the challenges in assessing higher-level visualization skills using constructed-response tests. Supplemental materials are available at: https://osf.io/hg7kx/.
Becoming One with Kuddi: Touching Data through an Intimate Data Physicalisation
Authors: Guðrún Margrét Ívansdóttir, Joo Young Park, Anna Ståhl, Madeline Balaam
Published at CHI, 2025

Kuddi is a haptic data physicalisation in the form of a soft pillow which combines 12 inflatable pockets to dynamically touch and be touched in relation to the changing menstruating body. This paper presents the soma design process that led to Kuddi's design, as well as Kuddi's evaluation through an auto-ethnographic approach, where the first author lived with Kuddi for two menstrual cycles. The resulting dataset was analysed by the research team using a narrative-led approach. Based on this analysis, we present five thick descriptions that capture how the experience of living with Kuddi led to a changing relation with menstrual pain. We contribute a design case of a haptic data physicalisation intended to touch the body and discuss how the material and interaction design choices embodied in Kuddi led to data visceralisation - a way of feeling data in ways which promote new somatic knowledge and experience.
Beyond Time and Accuracy: Strategies in Visual Problem-Solving
Authors: Eric Mörth, Zona Kostic, Nils Gehlenborg, Hanspeter Pfister, Johanna Beyer, Carolina Nobre
Published at CHI, 2025

In this paper, we explore viewers' strategies in visual problem-solving tasks. We build on the traditional metrics of accuracy and time to better understand the learning that occurs as individuals interact with visualizations. We conducted an in-lab eye-tracking user study with 53 participants from diverse demographic backgrounds. Using questions from the Visualization Literacy Assessment Test (VLAT), we examined participants' problem-solving strategies. We employed a mixed-methods approach capturing quantitative data on performance and gaze patterns, as well as qualitative data through think-alouds and sketches by participants as they reported on their problem-solving approach. Our analysis reveals not only the various cognitive strategies leading to correct answers but also the nature of mistakes and the conceptual misunderstandings that underlie them. This research contributes to the enhancement of visualization design guidelines by incorporating insights into the diverse strategies and cognitive processes employed by users.
Blending the Worlds: An evaluation of World-Fixed Visual Appearances in Automotive Augmented Reality
Authors: Robin Connor Schramm, Markus Sasalovici, Jann Philipp Freiwald, Michael Martin Otto, Melissa Reinelt, Ulrich Schwanecke
Published at CHI, 2025

With the transition to fully autonomous vehicles, non-driving related tasks (NDRTs) become increasingly important, allowing passengers to use their driving time more efficiently. In-car Augmented Reality (AR) gives the possibility to engage in NDRTs while also allowing passengers to engage with their surroundings, for example, by displaying world-fixed points of interest (POIs). This can lead to new discoveries, provide information about the environment, and improve locational awareness. To explore the optimal visualization of POIs using in-car AR, we conducted a field study (N = 38) examining six parameters: positioning, scaling, rotation, render distance, information density, and appearance. We also asked for intention of use, preferred seat positions and preferred automation level for the AR function in a post-study questionnaire. Our findings reveal user preferences and general acceptance of the AR functionality. Based on these results, we derived UX-guidelines for the visual appearance and behavior of location-based POIs in in-car AR.
CalliSense: An Interactive Educational Tool for Process-based Learning in Chinese Calligraphy
Authors: Xinya Gong, Wenhui Tao, Yuxin Ma
Published at CHI, 2025

Process-based learning is crucial for the transmission of intangible cultural heritage, especially in complex arts like Chinese calligraphy, where mastering techniques cannot be achieved by merely observing the final work. To explore the challenges faced in calligraphy heritage transmission, we conducted semi-structured interviews (N=8) as a formative study. Our findings indicate that the lack of calligraphy instructors and tools makes it difficult for students to master brush techniques, and teachers struggle to convey the intricate details and rhythm of brushwork. To address this, we collaborated with calligraphy instructors to develop an educational tool that integrates writing process capture and visualization, showcasing the writing rhythm, hand force, and brush posture. Through empirical studies conducted in multiple teaching workshops, we evaluated the system's effectiveness with teachers (N=4) and students (N=12). The results show that the tool significantly enhances teaching efficiency and aids learners in better understanding brush techniques.
Chartist: Task-driven Eye Movement Control for Chart Reading
Authors: Danqing Shi, Yao Wang 0018, Yunpeng Bai, Andreas Bulling, Antti Oulasvirta
Published at CHI, 2025

To design data visualizations that are easy to comprehend, we need to understand how people with different interests read them. Computational models of predicting scanpaths on charts could complement empirical studies by offering estimates of user performance inexpensively; however, previous models have been limited to gaze patterns and overlooked the effects of tasks. Here, we contribute Chartist, a computational model that simulates how users move their eyes to extract information from the chart in order to perform analysis tasks, including value retrieval, filtering, and finding extremes. The novel contribution lies in a two-level hierarchical control architecture. At the high level, the model uses LLMs to comprehend the information gained so far and applies this representation to select a goal for the lower-level controllers, which, in turn, move the eyes in accordance with a sampling policy learned via reinforcement learning. The model is capable of predicting human-like task-driven scanpaths across various tasks. It can be applied in fields such as explainable AI, visualization design evaluation, and optimization. While it displays limitations in terms of generalizability and accuracy, it takes modeling in a promising direction, toward understanding human behaviors in interacting with charts.
Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics
Authors: Yuexi Chen, Yimin Xiao, Kazi Tasnim Zinat, Naomi Yamashita, Ge Gao 0001, Zhicheng Liu 0001
Published at CHI, 2025

Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary challenges in analyzing writing behaviors are data complexity and the uncertainties introduced by automated methods. In response, we present COALA, a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters, generating behavior summaries using large language models, and visualizing writing-related actions at multiple granularities. We validated the effectiveness of COALA through user studies with domain experts (N=2+2) and researchers with relevant experience (N=8). We present the insights discovered by participants using COALA, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.
Confirmation Bias: The Double-Edged Sword of Data Facts in Visual Data Communication
Authors: Shiyao Li, Thomas James Davidson, Cindy Xiong Bearfield, Emily Wall 0001
Published at CHI, 2025

Incorporating data facts, which are natural language descriptions of data patterns, alongside visualizations can guide readers and enhance the visibility of data patterns. However, data facts might also induce confirmation bias in visual analysis. We conducted a series of crowdsourced experiments to explore the biasing effects of data facts. Our findings show that the presentation style, strength, and alignment of data facts with pre-existing beliefs significantly impact confirmation bias. Data facts that support prior beliefs can exacerbate confirmation bias, whereas those that refute an individual's beliefs can mitigate it. This effect is amplified when data facts are used in combination with visual annotations. Data facts describing variable correlations are perceived to be more compelling than ones describing average values and are associated with higher levels of confirmation bias. We underscore the persuasive influence of data facts in visualizations and caution against their indiscriminate use in efforts to mitigate bias.
CPVis: Evidence-based Multimodal Learning Analytics for Evaluation in Collaborative Programming
Authors: Gefei Zhang, Shenming Ji, Yicao Li, Jingwei Tang, Jihong Ding, Meng Xia 0002, Guodao Sun, Ronghua Liang
Published at CHI, 2025

As programming education becomes more widespread, many college students from non-computer science backgrounds begin learning programming. Collaborative programming emerges as an effective method for instructors to support novice students in developing coding and teamwork abilities. However, due to limited class time and attention, instructors face challenges in monitoring and evaluating the progress and performance of groups or individuals. To address this issue, we collect multimodal data from real-world settings and develop CPVis, an interactive visual analytics system designed to assess student collaboration dynamically. Specifically, CPVis enables instructors to evaluate both group and individual performance efficiently. CPVis employs a novel flower-based visual encoding to represent performance and provides time-based views to capture the evolution of collaborative behaviors. A within-subject experiment (N=22), comparing CPVis with two baseline systems, reveals that users gain more insights, find the visualization more intuitive, and report increased confidence in their assessments of collaboration.
Crowdsourced Think-Aloud Studies
Authors: Zach Cutler, Lane Harrison, Carolina Nobre, Alexander Lex
Published at CHI, 2025

The think-aloud (TA) protocol is a useful method for evaluating user interfaces, including data visualizations. However, TA studies are time-consuming to conduct and hence often have a small number of participants. Crowdsourcing TA studies would help alleviate these problems, but the technical overhead and the unknown quality of results have restricted TA to synchronous studies. To address this gap we introduce CrowdAloud, a system for creating and analyzing asynchronous, crowdsourced TA studies. CrowdAloud captures audio and provenance (log) data as participants interact with a stimulus. Participant audio is automatically transcribed and visualized together with events data and a full recreation of the state of the stimulus as seen by participants. To gauge the value of crowdsourced TA studies, we conducted two experiments: one to compare lab-based and crowdsourced TA studies, and one to compare crowdsourced TA studies with crowdsourced text prompts. Our results suggest that crowdsourcing is a viable approach for conducting TA studies at scale.
Data at Hand: Exploring the Tactile Perception of Data Physicalizations
Authors: Dietmar Offenhuber, Laura J. Perovich, Bernice E. Rogowitz
Published at CHI, 2025

Data physicalizations are tangible objects, and touching them may improve their interpretation. However, little is known about how people actually touch physicalizations. We recorded verbal and tactile responses to data physicalizations in three consecutive conditions: as an unspecified object, as a representation of unknown data, and with full information about data and encoding. Our two stimulus objects present data for nine countries in a 3x3 grid. We varied vertical axis polarity, with positive data values either above (convex) or below (concave) baseline. Using an analog tracer method, we examine whether some components of the physicalization are touched more than others, whether touch varies by task and the impact of axis polarity. We found large differences in the degree to which different components were touched and that the effect of vertical axis polarity depended on task. We describe additional tactile and verbal behaviors that can inform the design of data physicalizations.
Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way
Authors: Chenglong Wang, Bongshin Lee, Steven Mark Drucker, Dan Marshall, Jianfeng Gao 0001
Published at CHI, 2025

Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis. Although many AI-powered systems have been introduced to reduce the effort of visualization authoring, existing systems are not well suited for iterative authoring. They typically require analysts to provide, in a single turn, a text-only prompt that fully describe a complex visualization. We introduce Data Formulator 2 (Df2 for short), an AI-powered visualization system designed to overcome this limitation. Df2 blends graphical user interfaces and natural language inputs to enable users to convey their intent more effectively, while delegating data transformation to AI. Furthermore, to support efficient iteration, Df2 lets users navigate their iteration history and reuse previous designs, eliminating the need to start from scratch each time. A user study with eight participants demonstrated that Df2 allowed participants to develop their own iteration styles to complete challenging data exploration sessions.
Datamancer: Bimanual Gesture Interaction in Multi-Display Ubiquitous Analytics Environments
Authors: Biswaksen Patnaik, Marcel Borowski, Huaishu Peng, Clemens Nylandsted Klokmose, Niklas Elmqvist
Published at CHI, 2025

We introduce Datamancer, a wearable device enabling bimanual gesture interaction across multi-display ubiquitous analytics environments. Datamancer addresses the gap in gesture-based interaction within data visualization settings, where current methods are often constrained by limited interaction spaces or the need for installing bulky tracking setups. Datamancer integrates a finger-mounted pinhole camera and a chest-mounted gesture sensor, allowing seamless selection and manipulation of visualizations on distributed displays. By pointing to a display, users can acquire the display and engage in various interactions, such as panning, zooming, and selection, using both hands. Our contributions include (1) an investigation of the design space of gestural interaction for physical ubiquitous analytics environments; (2) a prototype implementation of the Datamancer system that realizes this model; and (3) an evaluation of the prototype through demonstration of application scenarios, an expert review, and a user study.
Decision Theoretic Foundations for Experiments Evaluating Human Decisions
Authors: Jessica Hullman, Alex Kale, Jason D. Hartline
Published at CHI, 2025

Decision-making with information displays is a key focus of research in areas like human-AI collaboration and data visualization. However, what constitutes a decision problem, and what is required for an experiment to conclude that decisions are flawed, remain imprecise. We present a widely applicable definition of a decision problem synthesized from statistical decision theory and information economics. We claim that to attribute loss in human performance to bias, an experiment must provide the information that a rational agent would need to identify the normative decision. We evaluate whether recent empirical research on AI-assisted decisions achieves this standard. We find that only 10 (26%) of 39 studies that claim to identify biased behavior presented participants with sufficient information to make this claim in at least one treatment condition. We motivate the value of studying well-defined decision problems by describing a characterization of performance losses they allow to be conceived.