DevLan/vispubs huggingface dataset:
Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co‑Debugging System
Authors: Shuyu Shen, Sirong Lu, Leixian Shen, Yuyu Luo
Published at CHI, 2026

Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches, or how they can be effectively combined. We analyze Vega-Lite debugging cases from Stack Overflow, categorizing question types by askers, evaluating human responses, and assessing AI performance. Guided by these findings, we design a human-AI co-debugging system that combines LLM-generated suggestions with forum knowledge. We evaluated this system in a user study on 36 unresolved problems, comparing it with forum answers and LLM baselines. Our results show that while forum contributors provide accurate but slow solutions and LLMs offer immediate but sometimes misaligned guidance, the hybrid system resolves 86% of cases, higher than either alone.
Deconstructing Open-World Game Mission Design Formula: A Thematic Analysis Using an Action-Block Framework
Authors: Kaijie Xu 0002, Yiwei Zhang, Brian Yang, Clark Verbrugge
Published at CHI, 2026

Open-world missions often rely on repeated formulas, yet designers lack systematic ways to examine pacing, variation, and experiential balance across large portfolios. We introduce the Mission Action Quality Vector (MAQV), a six-dimensional framework—covering combat, exploration, narrative, emotion, problem-solving, and uniqueness—paired with an action block grammar representing missions as gameplay sequences. Using about 2200 missions from 20 AAA titles, we apply LLM-assisted parsing to convert community walkthroughs into structured action sequences and score them with MAQV. An interactive dashboard enables designers to reveal underlying mission formulas. In a mixed-methods study with experienced players and designers, we validate the pipeline's fidelity and the tool's usability, and use thematic analysis to identify recurring design trade-offs, pacing grammars, and systematic differences by quest type and franchise evolution. Our work offers a reproducible analytical workflow, a data-driven visualization tool, and reflective insights to support more balanced, varied mission design at scale.
Deepfake, Real Harm: A Participatory Approach for Imagining Infrastructures to Combat Deepfake Sexual Abuse
Authors: Saetbyeol LeeYouk, Joseph Seering
Published at CHI, 2026

With generative AI enabling easier production of sexually abusive content, deepfake sexual abuse has intensified, making anyone with visual data be a potential victim or perpetrator. Current moderation systems for non-consensual intimate imagery (NCII) are platform-centric, reactive, and poorly aligned with the workflows of real-time monitors and survivor supporters. To address this gap, we held participatory design workshops with 10 activists affiliated with victim advocacy and survivors experienced in combating deepfake sexual abuse in South Korea. Their insights revealed distinctive challenges, including ambiguity in content classification, barriers to evidence collection, and increased workloads and safety risks during monitoring. Participants suggested features for proactive protection, long-term case tracking, and cross-platform coordination, while emphasizing the need for conversations about data ownership and platform accountability. Based on these findings, we discuss design implications for system and policy that foster multi-stakeholder collaboration to prevent harm, strengthen cross-platform response, and reduce secondary trauma for activists.
DensityBars: A Space-Efficient Visualization for Event Temporal Distribution
Authors: Mingwei Lin, Qin Huang, Zikun Deng, Tobias Schreck, Yi Cai 0001
Published at CHI, 2026

Event temporal distribution analysis aims to capture both global (e.g., rises and peaks) and local patterns (e.g., frequent occurrences and sudden absences). Traditional charts typically rely on adjusting binning granularities to reveal such patterns. However, this strategy forces a trade-off between global clarity and local detail and may require considerably more screen space as the number of bins increases, which limits its applicability in space-constrained visual interface design. In this paper, we propose DensityBars, a space-efficient visualization that embeds fine-grained density heatmaps of event occurrences into the coarse-grained bar chart to convey both global and local patterns simultaneously. Two real-world use cases and two formal user studies demonstrate its effectiveness and usability. Insights from studies provide valuable implications for the visual design of temporal distribution visualizations.
Designing Computational Tools for Exploring Causal Relationships in Qualitative Data
Authors: Han Meng, Qiuyuan Lyu, Peinuan Qin, Yitian Yang, Renwen Zhang, Wen-Chieh Lin, Yi-Chieh Lee
Published at CHI, 2026

Figure 1: An overview of QualCausal. QualCausal consists of (a) Causal Network Construction through three steps: (a.1) extracting indicators from qualitative data based on the user-provided research overview, (a.2) interactively creating and mapping concepts with user control, and (a.3) extracting causal relationships; and (b) Causal Network Exploration through multiple coordinated views (i.e., Indicator View, Concept View, and Node View), with Details and Concepts Panels providing access to source data. Exploring causal relationships for qualitative data analysis in HCI and social science research enables the understanding of user needs and theory building. However, current computational tools primarily characterize and categorize qualitative data; the few systems that analyze causal relationships either inadequately consider context, lack credibility, or produce overly complex outputs. We first conducted a formative study with 15 participants interested in using computational tools for exploring causal relationships in qualitative data to understand their needs and derive design guidelines. Based on these findings, we designed and implemented QualCausal, a system that extracts and illustrates causal relationships through interactive causal network construction and multi-view visualization. A feedback study (n = 15) revealed that participants valued our system for reducing the analytical burden and providing cognitive scaffolding, yet navigated how such systems fit within their established research paradigms, practices, and habits. We discuss broader implications for designing computational tools that support qualitative data analysis.
Designing Group Activity Metrics for Social Interaction and Walking Adherence: A Social Prototyping Study Among Indian Older Adults
Authors: Pallavi Rao Gadahad, Anirudha Joshi, Riddhi Pramod Dumre
Published at CHI, 2026

Adherence to physical activity is critical for healthy ageing, yet older adults often struggle to maintain consistent routines. We introduce Group Activity Metrics (GAMs), a design construct comprising group visualisations shared within familiar, small social groups, and examine how they facilitate social interaction and support walking adherence among Indian older adults. Guided by Social Cognitive Theory (SCT) and informed by interviews and focus groups with 22 participants, we designed GAMs and deployed them in two walking cohorts. We evaluated them through social prototyping, an approach that captures early-stage social responses to design ideas. Our findings show that GAMs sparked curiosity and enabled social interaction, suggesting potential to support walking adherence. This work contributes empirical insights into the role of small-group social interaction in walking adherence, introduces GAMs as a theory-informed design construct, and positions social prototyping as a methodological approach for evaluating socially driven technologies in HCI.
Designing Movement Generation Models in Collaboration With Voguing And Dancehall Dancers
Authors: Léo Chédin, Jules Françoise, Baptiste Caramiaux, Sarah Fdili Alaoui
Published at CHI, 2026

Recent advances in Artificial Intelligence have enabled powerful generative models, yet few are tailored to dancers' practices. We present a long-term collaboration with a Voguing and Dancehall collective to design movement generation models trained on their repertoire. Our initial study with the dancers revealed that, despite limited physical realism, the generated movements inspired them. Iterative development led to Korai, an interactive tool for monitoring training, visualizing motion data, and prompting generation, which improved output quality. A subsequent structured observation study compared three model variants with high, medium, and low fidelity to the original dataset's style. Results show that dancers favored either highly faithful or highly unfaithful outputs, rejecting medium fidelity as neither authentic to their style nor creatively stimulating. Our findings highlight how direct collaboration with dancers not only informs model design but also deepens understanding of AI's role in supporting creative movement practices.
Do It Fast, Forget It Fast: How Timing and Limb Visualizations Affect First-Person Augmented Reality Instructions
Authors: Clara Sayffaerth, Ehbal Ablimit, Annika Köhler, Jonas Wombacher, Albrecht Schmidt 0001, Florian Müller 0003
Published at CHI, 2026

Acquiring tacit knowledge and practical skills often depends on direct observation and in situ training. AR offers an alternative by overlaying first-person step-by-step instructions that guide users through tasks such as assembly and repair. Previous work demonstrates the effectiveness of AR instruction for specific applications. In our experimental work, we systematically explore aspects of the broader design space. We conducted a controlled experiment (n = 40) to investigate three key factors identified in learning theory and XR embodiment research: imitation timing (parallel vs. sequential), limb visualization (hand vs. full arm), and limb visibility (opaque vs. semi-transparent). Across all conditions, participants followed AR instructions and afterward repeated the tasks from memory. We assessed performance, user experience, and retention. Our results show that parallel imitation is faster and increases embodiment, whereas sequential imitation enhances memory retention and comfort. Our findings provide guidance for the temporal and visual design of first-person AR tutorials.
Does Background Music Matter in Data Videos? A Study of Music's Impact on Persuasion, Engagement, and Recall
Authors: Hessam Djavaherpour, Leni Yang, Yvonne Jansen, Pierre Dragicevic, Narges Mahyar, Mahmood Jasim
Published at CHI, 2026

Data videos combine visualization, animation, narration, and often background music to tell stories with data. While music is widely believed to enhance emotion and persuasion, its impact in data videos remains unexplored. We conducted a preregistered between-subjects experiment comparing six widely-viewed data videos with or without background music. Using Bayesian modeling and thematic analysis, we did not observe consistent measurable effects of background music on persuasion, engagement, or information recall. Qualitative responses revealed a more nuanced picture: some participants described the music as distracting or mismatched, while others reported that it enhanced enjoyment, supported focus, or strengthened emotional resonance when well aligned with the video's tone. These findings suggest that the influence of background music in data videos is highly context-dependent, shaped by genre, familiarity, and its alignment with visual–narrative structure. We discuss possible reasons for the limited measurable effects observed in real-world videos and outline opportunities for future work on purpose-designed, incidental, or adaptive music for data-driven storytelling.
Embedded vs. Situated: An Evaluation of AR Facial Training Feedback
Authors: Avinash Ajit Nargund, Andrea M. Park, Tobias Höllerer, Misha Sra
Published at CHI, 2026

While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user's face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.
Embodying Facts, Figures, and Faiths in Narrative Artistic Performances in Rural Bangladesh
Authors: Sharifa Sultana, Jeffrey M. Rzeszotarski, Zinnat Sultana, Syed Ishtiaque Ahmed
Published at CHI, 2026

There is an increasing interest in telling serious stories with data. Designers organize information, construct narratives, and present findings to inform audiences. However, many of these practices emerge from modern information visualization rhetoric and ethical frameworks which may marginalize communities with low digital and media literacy. In a ten-month-long ethnographic study in three Bangladeshi villages, we investigated how these communities use entertainment and cultural practices, namely Puthi, Bhandari Gaan, and Pot music, to instruct, communicate traditional moral lessons and recall history. We found that these communities embrace polyvocality and multiple ethical frameworks in their performances, construct narratives combining factuality, emotionality, and aesthetics, and adapt their performances to changing technology and audience needs. Our findings provide HCI, visualization, and ethical data practitioners with implications for the design of accessible and culturally appropriate ways of presenting data narratives in data-driven systems.
Evaluation-First Design for Data Visualization Interfaces
Authors: Bijesh Shrestha, Hilson Shrestha, Karen Bonilla, R. Jordan Crouser, Lane Harrison
Published at CHI, 2026

Existing frameworks in visualization and HCI emphasize iteration, data grounding, and stakeholder needs; however, they have not fully explored how evaluation might persist across phases, adapt to compressed timelines, and aid stakeholder engagement and elicitation. Building on prior frameworks, we introduce Evaluation-first design EvalOps that centers evaluation as a material component in the design process, emphasizing tighter feedback loops, co-evaluation with stakeholders, malleable forms of evaluation, and goals-to-metrics grounding. We illustrate how EvalOps shapes design outcomes through two case studies of data-visualization and LLM-enabled reasoning tools, demonstrating how evaluation-driven design facilitates alignment and trust, uncovers opportunities earlier, and supports cohesiveness under rapidly changing constraints. We contrast EvalOps with current visualization design methodologies and discuss opportunities for expanding evaluation-centered framings to other active areas of design research.
Every Move You Make: Visualizing Near-Future Motion Under Delay for Telerobotics
Authors: Dries Cardinaels, Raf Ramakers, Tom Veuskens, Thomas Pietrzak, Gustavo Alberto Rovelo Ruiz, Kris Luyten
Published at CHI, 2026

Delays in direct teleoperation decouple operator input from robot feedback. We frame this not as a unitary problem but as three facets of operator uncertainty: (1) communication, when commands take effect, (2) trajectory, how inputs map to motion, and (3) environmental, how external factors alter outcomes. We externalized each facet through predictive visualizations: Network, Path, and Envelope. In a controlled study with 24 participants (novices in telerobotics) navigating a simulated robot under a fixed 2.56 s round-trip delay, we compared these visualizations against a delayed-video baseline. Path significantly shortened task time, lowered perceived cognitive load, and reduced reliance on reactive "move-and-wait" behavior. Envelope lowered cognitive load but did not significantly reduce reactive behavior or improve performance, while Network had no measurable effect. These results indicate that predictive support is effective only when trajectory uncertainty is externalized, enabling operators to move from reactive to more proactive control.
Examining Interpretation Strategies for Multiple Forecast Visualizations with Two and Four Forecasts
Authors: Lace M. K. Padilla, Racquel Fygenson, Connor Wilson, Kristi Potter, Spencer C. Castro
Published at CHI, 2026

Multiple forecast visualizations (MFVs) present curated sets of forecasts to support decision-making under uncertainty. However, the research community knows little about how people interpret and integrate competing forecasts. In this study, we investigate the strategies individuals use when predicting hypothetical future events with MFVs across five visualization types (median, 95% CIs, standard deviation intervals, density plots, and hypothetical outcome plots) and multiple probability distributions in two preregistered experiments (n = 500 each). Analysis of 18 participant strategies and open responses shows that whereas many participants attempted to visually average across forecasts, others adopted a winner-takes-all approach (e.g., selecting a single forecast as the most likely outcome), which deviates from rational agent expectations. We also observed reliance on visual artifacts, such as intersection points or end caps. These findings underscore the complexity of interpreting a range of forecasts and help explain why individuals may privilege particular predictions in real-world decision contexts.
Exploring Collaborative Immersive Visualization & Analytics for High-Dimensional Scientific Data through Domain Expert Perspectives
Authors: Fahim Arsad Nafis, Jie Li 0064, Simon Su, Songqing Chen, Bo Han 0001
Published at CHI, 2026

Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization & analytics research has emphasized individual interaction, leaving open how multi-user collaboration can be supported at scale. To fill this critical gap, we conduct semi-structured interviews with 20 domain experts from diverse academic, government, and industry backgrounds. Using deductive–inductive hybrid thematic analysis, we identify four collaboration-focused themes: workflow challenges, adoption perceptions, prospective features, and anticipated usability and ethical risks. These findings show how current ecosystems disrupt coordination and shared understanding, while highlighting opportunities for effective multi-user engagement. Our study contributes empirical insights into collaboration practices for high-dimensional scientific data visualization & analysis, offering design implications to enhance coordination, mutual awareness, and equitable participation in next-generation collaborative immersive platforms. These contributions point toward future environments enabling distributed, cross-device teamwork on high-dimensional scientific data.
Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek
Authors: Yanwei Huang, Arpit Narechania
Published at CHI, 2026

Web AI agents such as ChatGPT Agent and GenSpark are increasingly used for routine web-based tasks, yet they still rely on text-based input prompts, lack proactive detection of user intent, and offer no support for interactive data analysis and decision making. We present WebSeek, a mixed-initiative browser extension that enables users to discover and extract information from webpages to then flexibly build, transform, and refine tangible data artifacts–such as tables, lists, and visualizations–all within an interactive canvas. Within this environment, users can perform analysis–including data transformations such as joining tables or creating visualizations–while an in-built AI both proactively offers context-aware guidance and automation, and reactively responds to explicit user requests. An exploratory user study (N=15) with WebSeek as a probe reveals participants' diverse analysis strategies, underscoring their desire for transparency and control during human-AI collaboration.
FAME: Exploring Expressive Facial Avatars for Lyrical and Non-Lyrical Music Visualization for d/Deaf Individuals
Authors: Suhyeon Yoo, Yifang Pan, Ashish Ajin Thomas, Karan Singh 0004, Khai N. Truong
Published at CHI, 2026

d/Deaf and Hard of Hearing (DHH) individuals often engage with music through a multimodal approach, where visual modalities are also used rather than relying on sound alone. While tools like captions and visualizers offer partial support, they often fail to capture the emotional depth and structural nuances of music. To explore new possibilities, we adopted an iterative, probe-based approach. Through a formative study with 9 DHH participants, we identified key design requirements for visualizing rhythm, emotion, and lyrics. We developed FAME (Facial Avatar for Musical Expression), a design probe that conveys music through expressive facial animation, instrument highlights, and synchronized captions, lip-syncing to lyrics or scat-singing to melodies. Through a two-phase exploratory study with 12 DHH users, we examined FAME's efficacy, applicability, and requirements for representing musical elements. Our findings refine design requirements for avatar-based systems and highlight the potential of avatars as expressive and socially meaningful tools for music accessibility.
Framing Water: Exploring Tensions between Social Norms and Environmental Sustainability through a Data Physicalization Game
Authors: Eleonora Mencarini, Ann L. Kruger, Chiara Leonardi, Paolo Massa
Published at CHI, 2026

Mountain areas are especially vulnerable to climate change. In recent years, intermittent droughts have forced many Alpine huts to close early or rely on cable cars or helicopters to import water. To raise awareness of water scarcity among hut visitors, we developed Framing Water, a reflective game based on the data physicalization of each visitor's water consumption during an overnight stay. The game requires players to select their most essential water-using activities without exceeding a fixed limit. Yet it is designed not to provide a univocal answer about the right choices to make but to spark reflection and dialogue around trade-offs in daily practices. We evaluated it with 56 participants and found that water-use decisions are influenced by individual needs, values, and social norms. This work contributes to Sustainable HCI by showing how playful, data-driven artifacts can foster reflection and negotiation of resource use in response to climate challenges.