Designing AI Peers for Collaborative Mathematical Problem Solving with Middle School Students: A Participatory Design StudyCollaborative problem solving (CPS) is a fundamental practice in middle-school mathematics education; however, student groups frequently stall or struggle without ongoing teacher support. Recent work has explored how Generative AI tools can be designed to support one-on-one tutoring, but little is known about how AI can be designed as peer learning partners in collaborative learning contexts. We conducted a participatory design study with 24 middle school students, who first engaged in mathematics CPS tasks with AI peers in a technology probe, and then collaboratively designed their ideal AI peer. Our findings reveal that students envision an AI peer as competent in mathematics yet explicitly deferential, providing progressive scaffolds such as hints and checks under clear student control. Students preferred a tone of friendly expertise over exaggerated personas. We also discuss design recommendations and implications for AI peers in middle school mathematics CPS.2026WLWenhan Lyu et al.William & MaryIntelligent Tutoring Systems & Learning AnalyticsCollaborative Learning & Peer TeachingHuman-LLM CollaborationCHI
Exploring Customizable Interactive Tools for Therapeutic Homework Support in Mental Health CounselingTherapeutic homework (i.e., tasks assigned by therapists for clients to complete between sessions) is essential for effective psychotherapy, yet therapists often interpret fragmented client logs, assessments, and reflections within limited preparation time. Our formative study with licensed therapists revealed three critical design requirements: support for interpreting unstructured client self-reports, customization aligned with clinical objectives, and seamless integration across multiple data sources. We then designed and developed TheraTrack, a customizable, therapist-facing tool that integrates multi-dimensional data and leverages large language models to generate traceable summaries and support natural-language queries, to streamline between-session homework tracking. Our pilot study with 14 therapists showed that TheraTrack reduced their cognitive load, enabled verification through direct navigation from AI summaries to original data entries, and was adapted differently for private analysis compared to in-session use, with dependence varying based on therapist experience and usage duration. We also discuss design implications for clinician-centered AI for mental health.2026YWYimeng Wang et al.William & MaryHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI
Understanding Attitudes and Trust of Generative AI Chatbots for Social Anxiety SupportSocial anxiety (SA) has become increasingly prevalent. Traditional coping strategies often face accessibility challenges. Generative AI (GenAI), known for their knowledgeable and conversational capabilities, are emerging as alternative tools for mental well-being. With the increased integration of GenAI, it is important to examine individuals' attitudes and trust in GenAI chatbots' support for SA. Through a mixed-method approach that involved surveys (n = 159) and interviews (n = 17), we found that individuals with severe symptoms tended to trust and embrace GenAI chatbots more readily, valuing their non-judgmental support and perceived emotional comprehension. However, those with milder symptoms prioritized technical reliability. We identified factors influencing trust, such as GenAI chatbots' ability to generate empathetic responses and its context-sensitive limitations, which were particularly important among individuals with SA. We also discuss the design implications and use of GenAI chatbots in fostering cognitive and emotional trust, with practical and design considerations.2025YWYimeng Wang et al.William & MaryConversational ChatbotsHuman-LLM CollaborationMental Health Apps & Online Support CommunitiesCHI
Profiling the Dynamics of Trust & Distrust in Social Media: A Survey StudyIn the era of digital communication, misinformation on social media threatens the foundational trust in these platforms. While myriad measures have been implemented to counteract misinformation, the complex relationship between these interventions and the multifaceted dynamics of trust and distrust on social media remains underexplored. To bridge this gap, we surveyed 1,769 participants in the U.S. to gauge their trust and distrust in social media and examine their experiences with anti-misinformation features. Our research demonstrates how trust and distrust in social media are not simply two ends of a spectrum; but can also co-exist, enriching the theoretical understanding of these constructs. Furthermore, participants exhibited varying patterns of trust and distrust across demographic characteristics and platforms. Our results also show that current misinformation interventions helped heighten awareness of misinformation and bolstered trust in social media, but did not alleviate underlying distrust. We discuss theoretical and practical implications for future research.2024YZYixuan Zhang et al.William & MarySocial Platform Design & User BehaviorMisinformation & Fact-CheckingCHI
What Do We Mean When We Talk about Trust in Social Media? A Systematic ReviewDo people trust social media? If so, why, in what contexts, and how does that trust impact their lives? Researchers, companies, and journalists alike have increasingly investigated these questions, which are fundamental to understanding social media interactions and their implications for society. However, trust in social media is a complex concept, and there is conflicting evidence about the antecedents and implications of trusting social media content, users, and platforms. More problematic is that we lack basic agreement as to what trust means in the context of social media. Addressing these challenges, we conducted a systematic review to identify themes and challenges in this field. Through our analysis of 70 papers, we contribute a synthesis of how trust in social media is defined, conceptualized, and measured, a summary of trust antecedents in social media, an understanding of how trust in social media impacts behaviors and attitudes, and directions for future work.2023YZYixuan Zhang et al.Georgia Institute of TechnologyPrivacy by Design & User ControlSocial Platform Design & User BehaviorCHI
Shifting Trust: Examining How Trust and Distrust Emerge, Transform, and Collapse in COVID-19 Information SeekingDuring crises like COVID-19, individuals are inundated with conflicting and time-sensitive information that drives a need for rapid assessment of the trustworthiness and reliability of information sources and platforms. This parallels evolutions in information infrastructures, ranging from social media to government data platforms. Distinct from current literature, which presumes a static relationship between the presence or absence of trust and people’s behaviors, our mixed-methods research focuses on situated trust, or trust that is shaped by people's information-seeking and assessment practices through emerging information platforms (e.g., social media, crowdsourced systems, COVID data platforms). Our findings characterize the shifts in trustee (what/who people trust) from information on social media to the social media platform(s), how distrust manifests skepticism in issues of data discrepancy, the insufficient presentation of uncertainty, and how this trust and distrust shift over time. We highlight the deep challenges in existing information infrastructures that influence trust and distrust formation.2022YZYixuan Zhang et al.Georgia Institute of TechnologyContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
Mapping the Landscape of COVID-19 Crisis VisualizationsIn response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people’s attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.2021YZYixuan Zhang et al.Georgia Institute of TechnologyInteractive Data VisualizationMedical & Scientific Data VisualizationCHI
How Guiding Questions Facilitate Feedback Exchange in Project-Based LearningPeer feedback is essential for learning in project-based disciplines. However, students often need guidance when acting as either a feedback provider or a feedback receiver, both to gain from peer feedback and to criticize their peers' work. This paper explores how to more effectively scaffold this exchange such that peers more deeply engage in the feedback process. Within a game design course, we introduced different processes for feedback receivers to write questions to guide peer feedback. Feedback receivers wrote four main types of guiding questions: improve, share, brainstorm, critique. We found that "improve'' questions tended to lead to better feedback (more specific, critical, and actionable) than other question types, but feedback receivers wrote improve questions least often. We offer insights on how best to scaffold the question-writing process to facilitate peer feedback exchange.2019ACAmy Cook et al.Carnegie Mellon UniversityCollaborative Learning & Peer TeachingUser Research Methods (Interviews, Surveys, Observation)CHI