From Fear to Control: Developing a Three-Factor Scale for Cybersecurity Anxiety (CybAS)Cybersecurity anxiety captures the persistent worry, stress and perceived lack of control individuals experience when navigating digital threats. While prior research has examined privacy concerns, computer anxiety and related constructs, no validated instrument exists to specifically measure anxiety in cybersecurity contexts. We address this gap with the Cybersecurity Anxiety Scale (CybAS), a 15-item psychometric instrument developed through literature review, item generation, and multiple survey studies. CybAS consists of three factors: Present (current concerns), Future (anticipated threats), and Control (perceived control over outcomes). Analyses confirm strong reliability and validity, and the concise format makes CybAS suitable for both research and applied settings. Beyond measurement, CybAS offers HCI researchers a diagnostic framework for detecting misalignments between users’ mental models and security technologies, enabling the design of anxiety-aware security systems that directly address emotional barriers, bridging the gap between usability, trust, and security.2026NDNikolaj Dall et al.University of Southern DenmarkPrivacy Perception & Decision-MakingCybersecurity Training & AwarenessDark Patterns RecognitionCHI
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
``I want to think like an SLP'': A Design Exploration of AI-Supported Home Practice in Speech TherapyParents of children in speech therapy play a crucial role in delivering consistent, high-quality home practice, which is essential for helping children generalize new speech skills to everyday situations. However, this responsibility is often complicated by uncertainties in implementing therapy techniques and keeping children engaged. In this study, we explore how varying levels of AI oversight can provide informational, emotional, and practical support to parents during home speech therapy practice. Through semi-structured interviews with 20 parents, we identified key challenges they face and their ideas for AI assistance. Using these insights, we developed six design concepts, which were then evaluated by 20 Speech-Language Pathologists (SLPs) for their potential impact, usability, and alignment with therapy goals. Our findings contribute to the discourse on AI’s role in supporting therapeutic practices, offering design considerations that address the needs and values of both families and professionals.2025ADAayushi Dangol et al.University of Washington, Human Centered Design & EngineeringElectrical Muscle Stimulation (EMS)Agent Personality & AnthropomorphismAugmentative & Alternative Communication (AAC)CHI
The Impact of Risk Appeal Approaches on Users’ Sharing Confidential InformationEnd-to-end encrypted email can help users prevent unauthorized access of their sensitive information. However, many users struggle to utilize encryption tools due to usability issues and low understanding. Thus, we designed video messaging interventions to persuade users to use email encryption software (Virtru). Our first intervention combined Protection Motivation Theory with Anticipated Regret (PMT+AR), and was designed to help participants understand the benefits of using encrypted email. Our second intervention also included Action Planning (PMT+AR+AP), and was designed to help participants recognize opportunities to use encrypted email. We conducted online interviews with 121 participants and used a follow-up survey to evaluate our interventions. Pre-intervention, participants believed that Gmail encrypted standard email content by default. Post-intervention, both messages made participants more likely to utilize encrypted email in a simulated information sharing scenario compared to Control. Our results suggest that our interventions can help people adopt protective technologies and address their misconceptions about them.2024EQElham Al Qahtani et al.University of JeddahPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
User Perspectives on Ethical Challenges in Human-AI Co-Creativity: A Design Fiction StudyIn a human-AI co-creation, AI not only categorizes, evaluates and interprets data but also generates new content and interacts with humans. As co-creative AI is a form of intelligent technology that directly involves humans, it is critical to anticipate and address ethical issues during all design stages. The open-ended nature of human-AI interactions in co-creation poses many challenges for designing ethical co-creative AI systems. Researchers have been exploring ethical issues associated with autonomous AI in recent years, but ethics in human-AI co-creativity is a relatively new research area. In order to design human-centered ethical AI, it is important to understand the perspectives, expectations, and ethical concerns of potential users. In this paper, we present a study with 18 participants to explore ethical dilemmas and challenges in human-AI co-creation from the perspective of potential users using design fiction (DF). DF is a speculative research method that depicts a new concept or technology through stories as an intangible prototype. We present the findings from the study as potential users' concerns, stances and expectations around ethical challenges in human-AI co-creativity to devise guidelines for designing human-centered ethical AI partners for human-AI co-creation.2023JRJeba Rezwana et al.Generative AI (Text, Image, Music, Video)AI Ethics, Fairness & AccountabilityDesign FictionC&C
Co-designing Community-based Sharing of Smarthome Devices for the Purpose of Co-monitoring In-home EmergenciesWe conducted 26 co-design interviews with 50 smarthome device owners to understand the perceived benefits, drawbacks, and design considerations for developing a smarthome system that facilitates co-monitoring with emergency contacts who live outside of one's home. Participants felt that such a system would help ensure their personal safety, safeguard from material loss, and give them peace of mind by ensuring quick response and verifying potential threats. However, they also expressed concerns regarding privacy, overburdening others, and other potential threats, such as unauthorized access and security breaches. To alleviate these concerns, participants designed flexible and granular access control and fail-safe back-up features. Our study reveals why peer-based co-monitoring of smarthomes for emergencies may be beneficial but also difficult to implement. Based on the insights gained from our study, we provide recommendations for designing technologies that facilitate such co-monitoring while mitigating its risks.2023LALeena Alghamdi et al.University of Central FloridaSmart Home Interaction DesignSmart Home Privacy & SecurityParticipatory DesignCHI
When do Data Visualizations Persuade? The Impact of Prior Attitudes on Learning about Correlations from Scatterplot VisualizationsData visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior.2023DMDouglas Markant et al.University of North Carolina at CharlotteData StorytellingUncertainty VisualizationVisualization Perception & CognitionCHI
Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative SystemsHuman-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In a creative collaboration, communication is an essential component among collaborators. In many existing co-creative systems, users can communicate with the AI, usually using buttons or sliders. Typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners rather than just a tool. This paper presents a study with 38 participants to explore the impact of two interaction designs, with and without AI-to-human communication, on user engagement, collaborative experience and user perception of a co-creative AI. The study involves user interaction with two prototypes of a co-creative system that contributes sketches as design inspirations during a design task. The results show improved collaborative experience and user engagement with the system incorporating AI-to-human communication. Users perceive co-creative AI as more reliable, personal, and intelligent when the AI communicates to users. The findings can be used to design effective co-creative systems, and the insights can be transferred to other fields involving human-AI interaction and collaboration.2022JRJeba Rezwana et al.Generative AI (Text, Image, Music, Video)AI-Assisted Creative WritingCreative Collaboration & Feedback SystemsC&C
Smart Home Beyond the Home: A Case for Community-Based Access ControlAs smart devices are becoming commonplace in homes, we need to explore the needs of not just the residents of the home, but also of secondary stakeholders who may be granted access to these devices from outside of the home. We conducted a mixed methods study, which included a survey of 163 smart home device owners and a follow-up interview with 13 individuals who currently share their smart home devices with others outside of their home. Nearly half (47.8%) of our survey participants shared at least one smart home device with someone that did not live with them. Individuals sought greater safety and security by providing remote access to trusted family members or friends. By understanding users' perspectives about privacy and trust in relation to sharing smart home devices beyond the home, we build a case for community-based access control of smart home devices in the Internet of Things.2020MTMadiha Tabassum et al.University of North Carolina at CharlotteSmart Home Interaction DesignHome Energy ManagementSmart Home Privacy & SecurityCHI
Studying the Effects of Cognitive Biases in Evaluation of Conversational AgentsHumans quite frequently interact with conversational agents. The rapid advancement in generative language modeling through neural networks has helped advance the creation of intelligent conversational agents. Researchers typically evaluate the output of their models through crowdsourced judgments, but there are no established best practices for conducting such studies. Moreover, it is unclear if cognitive biases in decision-making are affecting crowdsourced workers' judgments when they undertake these tasks. To investigate, we conducted a between-subjects study with 77 crowdsourced workers to understand the role of cognitive biases, specifically anchoring bias, when humans are asked to evaluate the output of conversational agents. Our results provide insight into how best to evaluate conversational agents. We find increased consistency in ratings across two experimental conditions may be a result of anchoring bias. We also determine that external factors such as time and prior experience in similar tasks have effects on inter-rater consistency.2020SSSashank Santhanam et al.University of North Carolina at CharlotteConversational ChatbotsUser Research Methods (Interviews, Surveys, Observation)CHI
Du Bois Wrapped Bar Chart: Visualizing Categorical Data with Disproportionate ValuesWe propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.2020AKAlireza Karduni et al.University of North Carolina at CharlotteInteractive Data VisualizationUncertainty VisualizationCHI
Vulnerable to Misinformation? Verifi!We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. Verifi2 uses state-of-the-art computational methods to highlight linguistic, network, and image features that can distinguish suspicious news accounts. By exploring news on a source and document level in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, communications, education, and psychology who study misinformation. Our interviews highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.2019AKAlireza Karduni et al.Interactive Data VisualizationMisinformation & Fact-CheckingComputational Methods in HCIIUI
Towards Rapid Interactive Machine Learning: Evaluating Tradeoffs of Classification without RepresentationOur contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a ``tip of the iceberg'' approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an $O(n)$ classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.2019DADustin L Arendt et al.Human-LLM CollaborationPrototyping & User TestingIUI
Co-designing for Community Oversight: Helping People Make Privacy and Security Decisions TogetherCollective feedback can support an individual’s decision-making process. For instance, individuals often seek the advice of friends, family, and co-workers to help them make privacy decisions. However, current technologies often do not provide mechanisms for this type of collaborative interaction. To address this gap, we propose a novel model of Community Oversight for Privacy and Security (“CO-oPS”), which identifies mechanisms for users to interact with people they trust to help one another make digital privacy and security decisions. We apply our CO-oPS model in the context of mobile applications (“apps”). To interrogate and refine this model, we conducted participatory design sessions with 32 participants in small groups of 2-4 people who know one another, with the goal of designing a mobile app that facilitates collaborative privacy and security decision-making. We describe and reflect on the opportunities and challenges that arise from the unequal motivation and trust in seeking support and giving support within and beyond a community. Through this research, we contribute a novel framework for collaborative digital privacy and security decision-making and provide empirical evidence towards how researchers and designers might translate this framework into design-based features.2019CCChhaya Chouhan et al.Privacy and SecurityCSCW
Cross-Platform Immersive Web Browsing for Online 3D Neuron Database ExplorationWeb services have become one major way for people to obtain and explore information nowadays. However, web browsers currently only offer limited data analysis capabilities, especially for large-scale 3D datasets. This project presents a method of immersive web browsing (ImWeb) to enable effective exploration of multiple datasets over the web with augmented reality (AR) techniques. The ImWeb system allows inputs from both the web browser and AR and provides a set of immersive analytics methods for enhanced web browsing, exploration, comparison, and summary tasks. We have also integrated 3D neuron mining and abstraction approaches to support efficient analysis functions. The architecture of ImWeb system flexibly separates the tasks on web browser and AR and supports smooth networking among the system, so that ImWeb can be adopted by different platforms, such as desktops, large displays, and tablets. We use an online 3D neuron database to demonstrate that ImWeb enables new experiences of exploring 3D datasets over the web. We expect that our approach can be applied to various other online databases and become one useful addition to future web services.2019WFWillis Fulmer et al.AR Navigation & Context AwarenessInteractive Data VisualizationIUI
Interactive Storytelling for Movie Recommendation through Latent Semantic AnalysisRecommendation is essential to many online services, however current systems often provide limited interaction and visualization mechanisms. This paper presents an interactive recommendation approach for the general public without any knowledge of recommendation or visualization algorithms. Our approach emphasizes interactivity, explicit user input, and semantic information convey with the following two components. First, we propose a Latent Semantic Model that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation, so that high-dimensional spectral space from the rating records can be understood and interact with directly. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. We demonstrate and evaluate our approach with a real dataset. Our approach can also be extended to other applications including various online recommendation systems.2018KWKodzo Wegba et al.Human-LLM CollaborationRecommender System UXData StorytellingIUI
Security During Application Development: an Application Security Expert PerspectiveMany of the security problems that people face today, such as security breaches and data theft, are caused by security vulnerabilities in application source code. Thus, there is a need to understand and improve the experiences of those who can prevent such vulnerabilities in the first place - software developers as well as application security experts. Several studies have examined developers' perceptions and behaviors regarding security vulnerabilities, demonstrating the challenges they face in performing secure programming and utilizing tools for vulnerability detection. We expand upon this work by focusing on those primarily responsible for application security - security auditors. In an interview study of 32 application security experts, we examine their views on application security processes, their workflows, and their interactions with developers in order to further inform the design of tools and processes to improve application security.2018TTTyler W. Thomas et al.University of North Carolina at CharlottePrivacy by Design & User ControlPasswords & AuthenticationCHI
Increasing User Attention with a Comic-based PolicyEnd user license agreements, terms of service agreements and privacy policies all suffer from many of the same problems: people rarely read them and yet still agree to whatever is contained within them. There are many usability challenges with these policies: they are often lengthy, with jargon filled language that is difficult to quickly comprehend. However, these notices are the primary tool for users to understand the privacy implications of their digital activities and make informed decisions on which websites and software they use. Prior research has explored alternative designs for such notices, using more visual and structured interfaces for conveying information. We expand upon these results by exploring a comic-based interface, examining whether it can engage users to pay more attention to a terms of service agreement. Our results indicate that the comic version did hold user attention for longer than text-based alternatives, encouraging deeper investigation into comic-based interfaces.2018MTMadiha Tabassum et al.University of North Carolina at CharlottePrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Computer-Human Interaction Mentoring (CHIMe) 2018HCI is a field where diversity should be considered in the systems we build and study. As such, it is important to cultivate a growing group of diverse researchers with a range of experiences to contribute to difficult design, research, and computational problems. Therefore, the CHIMe organizers invite graduate and undergraduate students to attend. CHIMe intends to provide a welcoming environment for mentoring and collaboration amongst peers, faculty, and industry experts in HCI.2018RBRobin Brewer et al.University of MichiganParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Surprise Me If You Can: Serendipity in Health InformationOur natural tendency to be curious is increasingly important now that we are exposed to vast amounts of information. We often cope with this overload by focusing on the familiar: information that matches our expectations. In this paper we present a framework for interactive serendipitous information discovery based on a computational model of surprise. This framework delivers information that users were not actively looking for, but which will be valuable to their unexpressed needs. We hypothesize that users will be surprised when presented with information that violates the expectations predicted by our model of them. This surprise model is balanced by a value component which ensures that the information is relevant to the user. Within this framework we have implemented two surprise models, one based on association mining and the other on topic modeling approaches. We evaluate these two models with thirty users in the context of online health news recommendation. Positive user feedback was obtained for both of the computational models of surprise compared to a baseline random method. This research contributes to the understanding of serendipity and how to “engineer” serendipity that is favored by users.2018XNXi Niu et al.University of North Carolina at CharlotteHuman-LLM CollaborationExplainable AI (XAI)Recommender System UXCHI