OpenCD: Empowering Diagnosis of Children's Mathematical Cognition through Open-ended Multimodal TasksAssessing children’s cognitive development in early mathematics is vital for effective teaching. Compared to closed-ended questions, which may fail to capture nuanced developmental spectrum, open-ended elicitation tasks (e.g., asking students to manipulate objects or draw to represent numbers) serve as a promising approach to reveal deeper cognitive processes. However, their diverse and unstructured nature makes systematic analysis challenging for teachers. We present OpenCD, a teacher-facing system that automatically analyzes multimodal student responses to capture individualized insights. Based on Evidence-Centered Design, it combines Vision-Language Models (VLMs) and expert models to generate interactive diagnostic graphs and reports with traceability back to behavioral evidence. In our two-part evaluation, a validation study found 90.3% of the system’s diagnoses “completely reasonable,” and a user study showed that OpenCD reduced teachers’ analysis burden and enhanced their insights into student thinking. Our work contributes to scalable process-based assessment for mathematical literacy.2026ZZZhi Zheng et al.Tsinghua UniversityIntelligent Tutoring Systems & Learning AnalyticsProgramming Education & Computational ThinkingUser Research Methods (Interviews, Surveys, Observation)CHI
Redundant is Not Redundant: Automating Efficient Categorical Palettes Design Unifying Color & Shape Encodings with CatPAWColors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combinations are limited. This paper presents four crowdsourced experiments evaluating redundant color–shape encodings and identifying high-performing configurations across different category numbers. Results show that redundancy significantly improves accuracy in assessing class-level correlations, with the strongest benefits for 5–8 categories. We also find pronounced interaction effects between colors and shapes, underscoring the need for careful pairing in designing redundant encodings. Drawing on these findings, we introduce a categorical palette design tool that enables designers to construct empirically grounded palettes for effective categorical visualization. Our work advances understanding of categorical perception in data visualization by systematically identifying effective redundant color–shape combinations and embedding these insights into a practical palette design tool.2026CTChin Tseng et al.University of North Carolina-Chapel HillInteractive Data VisualizationVisualization Perception & CognitionCHI
From Expectation to Evaluation: Expectation Cues Systematically Bias LLM and Human JudgmentExpectation cues such as source labels, expertise signals, or identity-based indicators can bias how humans interpret and evaluate information. In high-stakes domains like healthcare, education, and law, such biases threaten the objectivity of decision-making. As LLMs increasingly provide decision support in these contexts, this study aims to examine whether LLMs exhibit expectation-driven bias akin to that of humans. Across two experiments (N = 1260), we manipulated expectations via priming statements and measured shifts in judgment scores. In both humans and LLMs, higher expectations led to more favorable evaluations for suggestions of equivalent quality, and greater mismatches between expectations and actual performance produced stronger judgment distortions. Notably, humans tended to adjust their evaluations unconsciously, whereas LLMs revised their outputs in a consistent and traceable manner. These findings reveal both shared sensitivities and distinct adjustment patterns, offering design insights for building expectation-aware AI systems that promote fair and transparent human–AI interaction.2026SYSun Yiteng et al.The Hong Kong Polytechnic UniversityHuman-LLM CollaborationExplainable AI (XAI)Privacy by Design & User ControlCHI
Selecting Tangible Media for Immersive Exploration of Volumetric Scientific DataImmersive scientific data exploration faces challenges in precise and efficient interaction. Tangible media offer a potential solution; but designers lack clear guidance on choosing the appropriate physical dimensionality (1D, 2D, or 3D) for different tasks. To address this problem, we present a design space structuring the relationship between the representative techniques on scientific data visualization and exploration, tangible interactions, and media dimensionality. We further developed a prototype to empirically explore these relationships according to our design space. In a controlled user study, we compared 1D, 2D, and 3D tangible media across seven core techniques. The results demonstrated that the 3D media (e.g., a box) were preferred when tasks required manipulating the entire volumetric data and acted as a proxy. Regarding the tasks requiring 2D operations or interior localization, the 2D media (e.g., a card) offered superior performance. For single-parameter techniques like histogram-based filtering, the 1D media (e.g., a pen) were overwhelmingly preferred for their simplicity and perceived ease of use.2026ZWZhouhao Wu et al.Renmin University of ChinaTangible User Interface DesignPhysical-Digital Hybrid InteractionMedical & Scientific Data VisualizationCHI
When AI Gets It Wrong: Scaffolding AI Hallucination Detection for Children Through Chatbot CreationChildren increasingly interact with generative AI systems that can produce hallucinated content, potentially reinforcing misconceptions and undermining critical thinking skills. We investigate how children detect and respond to hallucinations while building and testing LLM-powered chatbots in a development environment. We integrated hallucination-awareness scaffolds such as confidence indicators, fact-checking, repeated questioning, and model comparison. Through a study with 48 middle school learners aged 10-14, participants showed significant pre-to-post gains in AI knowledge, hallucination awareness, and confidence in building trustworthy chatbots. They developed multi-layered strategies, including probing inconsistencies and cross-checking with external sources. Key challenges included over-reliance on visible cues, fragmented use of scaffolds, and a tension between creativity and reliability. These findings highlight design implications for children’s AI literacy for responsible AI development: supporting proactive, iterative engagement in the development cycle, integrating scaffolds into coherent workflows, and balancing creativity with accuracy.2026XTXiaoyi Tian et al.North Carolina State UniversityHuman-LLM CollaborationChildren's AI Literacy & Data LiteracyParticipatory DesignCHI
Inside the Mirror, Wearing My own Body: Why UX Should Engage Monstrous ExperiencesWhile engaging with four different wearable systems, we unexpectedly encountered felt experiences that resisted articulation and defied conventional classification. They were neither pleasant nor unpleasant, and yet both; neither comforting nor frightening, and yet both; neither recognizably human-like nor machinic, and yet both. Such ambiguous experiences might have gone unnoticed had we not attended to their somatic, felt dimensions. Existing user experience frameworks offered little guidance in making sense of these phenomena. However, through the lens of monster theory, these paradoxical experiences began to reveal their structure and significance. Drawing on concepts such as fusion, fission, massification, and incompleteness, we analyze and interpret the unexpected monstrous experiences arising from interacting with wearable systems. We argue that such experiences deserve a place in interaction design: not only for the enduring fascination of the monster, but also for its power to disrupt simplistic schemas, enrich design possibilities, and illuminate cultural shifts.2026PKPavel Karpashevich et al.Carl von Ossietzky University of OldenburgHaptic WearablesEmotion-Sensing WearablesAffective Feedback & Emotion Regulation InterfacesCHI
Uncovering How Scatterplot Features Skew Visual Class SeparationMulti-class scatterplots are essential for visually comparing data, such as examining class distributions in dimensionality reduction and evaluating classification models. Visual class separation (VCS) measures quantify human perception but are largely derived from and evaluated with datasets reflecting limited types of scatterplot features (e.g., data distribution, similar class densities). Quantitatively identifying which scatterplot features are influential to VCS tasks can enable more robust guidance for future measures. We analyze the alignment between VCS measures and people's perceptions of class separation through a crowdsourced study using 70 scatterplot features relevant to class separation. To cover a wide range of scatterplot features, we generated a set of multi-class scatterplots from 6,947 real-world datasets. Our results highlight that multiple combinations of features are needed to best explain VCS. From our analysis, we develop a composite feature model that identifies key scatterplot features for measuring VCS task performance.2025SBS. Sandra Bae et al.University of Colorado Boulder, ATLAS InstituteInteractive Data VisualizationVisualization Perception & CognitionCHI
"Different and Boundary-Pushing:" How Blind and Low Vision Youth Live Code TogetherLive coding, or real-time algorithmic performance, is a rich medium for engaging novices in informal creative STEM learning. However, despite inclusive and open-source communities, disabled practitioners are underrepresented in live coding, and prior work highlights numerous accessibility barriers. To understand the perspectives of Blind and Low Vision (BLV) live coders, we formed FiLOrk (Fil Laptop Orchestra) with five BLV teens. Across two semesters, FiLOrk performed three original works, each guided by a core concept and improvisational structure for manipulating code and maintaining shared awareness. We interviewed four musicians to understand how they felt about the learning environment and how their creative identities formed individually and in relation to one another. We reflect on FiLOrk's outcomes and propose strategies for future live coding ensembles to meaningfully include novices with and without disabilities.2024WPWilliam Christopher Payne et al.Teleoperated DrivingUniversal & Inclusive DesignSpecial Education TechnologyC&C
PD-Insighter: A Visual Analytics System to Monitor Daily Actions for Parkinson's Disease TreatmentPeople with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients’ motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions.2024JKJade Kandel et al.University of North Carolina at Chapel HillHuman Pose & Activity RecognitionMedical & Scientific Data VisualizationTelemedicine & Remote Patient MonitoringCHI
Investigating the Mechanisms by which Prevalent Online Community Behaviors Influence Responses to Misinformation: Do Perceived Norms Really Act as a Mediator?This study addresses two currently open questions about how behaviors of online community members influence others' responses to misinformation. First, in contrast to prior work, it directly measures norm perception to address whether (1) norm perception actually acts as a mediator, (2) others' behaviors directly influence individuals' responses to misinformation, (3) both direct and mediated effects occur. Second, it investigates norm perceptions about a behavior that is not readily observable in online communities, but is prone to misinformation, specifically, vaccination. To do so, it experimentally manipulates the prevalence of communicating about vaccination (an unobservable behavior) within an online community. The results demonstrate no evidence of a direct effect---the causal relationship between prevalence of communicating a behavior and intentions to respond to misinformation only occurs via norm perception as a mediator. The paper highlights implications of these findings for designing community-centered interventions to influence perceived norms, thereby mitigating misinformation spread and impacts.2024ZAZhila Aghajari et al.Lehigh UniversityMisinformation & Fact-CheckingCommunity Engagement & Civic TechnologyCHI
Cieran: Designing Sequential Colormaps via In-Situ Active Preference LearningQuality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that allows any data analyst to rapidly find quality colormaps while designing charts within Jupyter Notebooks. Our system employs an active preference learning paradigm to rank expert-designed colormaps and create new ones from pairwise comparisons, allowing analysts who are novices in color design to tailor colormaps to their data context. We accomplish this by treating colormap design as a path planning problem through the CIELAB colorspace with a context-specific reward model. In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs. Our work shows the potential of active preference learning for supporting efficient visualization design optimization.2024MHMatt-Heun Hong et al.University of North Carolina at Chapel HillInteractive Data VisualizationVisualization Perception & CognitionCHI
Usable News Authentication: How the Presentation and Location of Cryptographic Information Impacts the Usability of Provenance Information and Perceptions of News ArticlesCryptographic tools for authenticating the provenance of web-based information are a promising approach to increasing trust in online news and information. However, making these tools' technical assurances sufficiently usable for news consumers is essential to realizing their potential. We conduct an online study with 160 participants to investigate how the presentation (visual vs. textual) and location (on a news article page or a third-party site) of the provenance information affects news consumers' perception of the content's credibility and trustworthiness, as well as the usability of the tool itself. We find that although the visual presentation of provenance information is more challenging to adopt than its text-based counterpart, this approach leads its users to put more faith in the credibility and trustworthiness of digital news, especially when situated internally to the news article.2024EIErrol Francis II et al.Clemson UniversityAlgorithmic Transparency & AuditabilityPrivacy Perception & Decision-MakingMisinformation & Fact-CheckingCHI
Negotiating Sociotechnical Boundaries: Moderation Work to Counter Racist Attacks in Online CommunitiesOnline communities are susceptible to racist attacks, even when community policies explicitly prohibit racism. Drawing on the concept of symbolic boundary, we explored how community members sustained their communities against the perpetuation of racist logics and practices on Reddit. We drew on trace ethnography to analyze conversations about crime in two city subreddits (i.e., r/baltimore and r/chicago). The findings illustrate that the fragility of community boundaries was contributed by race baiting posts, covert racism, and racist brigading. At the same time, our research highlights that moderation efforts maintained and established institutional, cultural, and geographical boundaries to combat racist attacks. We discuss boundary as a design technique for building safe spaces for community members. Content warning: This work contains racist quotes that can upset or harm some readers.2024QWQunfang Wu et al.University of North Carolina at Chapel HillOnline Harassment & Counter-ToolsContent Moderation & Platform GovernanceGender & Race Issues in HCICHI
Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionDesigners often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations---line graphs, bar graphs, and scatterplots---to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.2024GQGhulam Jilani Quadri et al.University of North CarolinaInteractive Data VisualizationVisualization Perception & CognitionCHI
The Cyber-Physical Control Room: A Mixed Reality Interface for Mobile Robot Teleoperation and Human-Robot TeamingIn this work, we present the design and evaluation of an immersive Cyber-Physical Control Room interface for remote mobile robots that provides users with both robot-egocentric and robot-exocentric 3D perspectives. We evaluate the Cyber-Physical Control room against a traditional robot interface in a mock disaster response scenario that features a mixed human-robot field team. In our evaluation, we found that the Cyber-Physical Control Room improved robot operator effectiveness by 28% while navigating a complex warehouse environment and performing a visual search. The Cyber-Physical Control Room also enhanced various aspects of human-robot teaming, including conversational engagement, the ability of a remote robot teleoperator to track their human partner in the field, and opinions of human teammate leadership qualities.2024MWMichael Walker et al.Mixed Reality WorkspacesContext-Aware ComputingHuman-Robot Collaboration (HRC)HRI
GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory SearchWhen people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only finding relevant documents but also synthesizing important concepts and the relationships that connect those concepts both within and across documents. This article describes a novel interactive approach designed to help users construct a mental model of an unfamiliar information space during exploratory search. We propose a new semantic search system to organize and visualize important concepts and their relations for a set of search results. A user study (n=20) was conducted to compare the proposed approach against a baseline faceted search system on exploratory literature search tasks. Experimental results show that the proposed approach is more effective in helping users recognize relationships between key concepts, leading to a more sophisticated understanding of the search topic while maintaining similar functionality and usability as a faceted search system.2024MGMengtian Guo et al.Interactive Data VisualizationData StorytellingIUI
Assessing User Trust in Active Learning Systems: Insights from Query Policy and Uncertainty VisualizationActive learning systems have become increasingly popular for various applications in machine learning (ML), including medical imaging, environmental monitoring, and geospatial analysis. These systems rely on inputs dynamically queried from people to enhance classification. Ensuring appropriate analyst trust in these systems remains a significant obstacle, as analysts may over-rely or under-rely on the system. Common active learning (AL) strategies enhance classification models by asking an analyst to provide labels for data points with the highest degree of uncertainty. However, model-centric policies do not consider potential priming effects on the analyst and how they will affect people's trust in the system post-training. In this paper, we present an empirical study assessing how AL query policies and visualizations that enhance transparency in a classifier’s certainty influence trust in automated image classifiers. We found that query policy may significantly influence an analyst’s perception of the system’s capabilities, while the level of visual transparency into classifier certainty may influence an analyst’s ability to perform the classification task. Our study informs the design of interactive labeling systems to help mitigate the effects of over-reliance.2024ITIan Thomas et al.Explainable AI (XAI)Interactive Data VisualizationUncertainty VisualizationIUI
GlassMessaging: Towards Ubiquitous Messaging Using OHMDshttps://doi.org/10.1145/36109312023NJNuwan Janaka et al.Context-Aware ComputingUbiquitous ComputingUbiComp
"How Do You Quantify How Racist Something Is?": Color-Blind Moderation in Decentralized GovernanceVolunteer moderators serve as gatekeepers for problematic content, such as racism and other forms of hate speech, on digital platforms. Prior studies have reported volunteer moderators' diverse roles in different governance models, highlighting the tensions between moderators and other stakeholders (e.g., administrative teams and users). Building upon prior research, this paper focuses on how volunteer moderators moderate racist content and how a platform's governance influences these practices. To understand how moderators deal with racist content, we conducted in-depth interviews with 13 moderators from city subreddits on Reddit. We found that moderators heavily relied on AutoMod to regulate racist content and racist user accounts. However, content that was crafted through covert racism and ``color-blind'' racial frames was not addressed well. We attributed these challenges in moderating racist content to (1) moderators' concerns of power corruption, (2) arbitrary moderator team structures, and (3) evolving forms of covert racism. Our results demonstrate that decentralized governance on Reddit could not support local efforts to regulate color-blind racism. Finally, we discuss the conceptual and practical ways to disrupt color-blind moderation.2023QWQunfang Wu et al.Race and BiasCSCW
Transparency, Trust, and Security Needs for the Design of Digital News Authentication ToolsAmericans' trust in news is declining, and authenticity and transparency challenges in digital publishing contexts pose unique challenges to the ability to effectively gratify their information-seeking needs via online media. Cryptographic technologies and web-based provenance indicators have the potential to enhance the trustworthiness and transparency of digital communication, but better understandings of news consumers practices and needs are required to develop practical tools. Through a representative online survey of 400 digital news consumers and 19 follow-up interviews, we investigate how users authenticate and assign trust to news content, and identify specific needs pertaining to news transparency and authentication that could be met by digital news authentication tools. While many users currently rely on political ideology to assess news trustworthiness, we find that users of all political orientations see value in independent provenance and authentication tools for digital news.2023EIErrol Francis II et al.Security and TrustCSCW