Interactive Explainable RankingWe propose an interactive decision-making tool for discovering and exploring explainable rankings for a given set of choices (e.g., job offers, vacation destinations, award candidates). We define an explainable ranking as an ordering of choices based on some consistent weighting of measured criteria. Our tool is designed to help users explore different orderings, criteria, and criterion weights in search of an explainable ranking that reflects their own personal preferences. To achieve this, we combine visualization, optimization, and (optionally) the integration of AI to help users identify and correct or explain inconsistencies in their evaluation of different choices. Through user experiments, we demonstrate that our tool leads to more consistent explainable rankings with greater user confidence.2026CZChao Zhang et al.Cornell UniversityExplainable AI (XAI)Interactive Data VisualizationAI-Assisted Decision-Making & AutomationCHI
Narrix: Remixing Narrative Strategies from Examples for Story WritingExperienced storytellers decompose stories into local narrative strategies and how these strategies shape higher-level arcs. This decomposition helps writers recognize patterns in others' work and adapt those patterns to tell new stories. Novices, however, struggle to identify these strategies or to reuse them effectively. We present Narrix, a novel writing tool that helps novice writers recognize narrative strategies in example stories and repurpose these strategies in their own writing. Narrix analyzes strategies in example stories, highlights them with color-coded lexical cues and explanations, and situates them on an interactive story arc for exploration by emotional shifts and turning points. Writers then drag strategies onto multi-dimensional tracks and apply block-scoped edits to revise or continue their drafts through controlled generation steered by specified strategies. Through a within-subjects study (N=12), Narrix showed improved participants' retention, confidence, and creative adaptation of narrative strategies compared to a baseline chat-based writing interface.2026CZChao Zhang et al.Cornell UniversityAI-Assisted Creative WritingAI-Assisted Writing & Text GenerationCreative Collaboration & Feedback SystemsCHI
EvaluAId: Human-AI Collaborative Evaluation of Open-Ended Student EssaysOpen-ended writing assignments are central to higher education, yet heterogeneous submissions and scale make evaluation difficult. Automated writing evaluation (AWE) promises speed but often trades away transparency and sidelines human judgment. This paper repositions AI as an on-demand collaborator that can provide specific, targeted support. In a formative study, we expose leverage points in three cognitive dimensions: evidence identification, comparative judgment, and feedback composition. Guided by these insights, we build EvaluAId, which supports interactive rubric-content mapping, adaptive benchmarking and self-calibration, and personalized, rubric-aligned feedback synthesis. Through a within-subjects study with 12 TAs, we evaluate how this approach supports grading compared with a rubric+LLM chatbot and an LLM-based AWE; EvaluAId improved alignment with expert ratings and increased graders' satisfaction. Finally, interviews with TAs, instructors, and students underscored the value of thoughtfulness supported by EvaluAId while surfacing practical considerations for integration into classroom. Together, our results argue for deliberate, evidence-first, human-in-the-loop evaluation.2026CZChao Zhang et al.Cornell UniversityHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsUser Research Methods (Interviews, Surveys, Observation)CHI
Behind the Same Mask: Understanding the Practice of Spontaneous Collective Anonymity on Chinese Social PlatformsAnonymity plays a crucial role in social interactions online. Recently, a new phenomenon has emerged on Chinese social platforms where users collectively adopt a uniform avatar and nickname "momo", thereby achieving anonymity. However, understanding such spontaneous collective anonymity within Chinese cultural and contextual factors remains limited since much of the anonymity research focuses on Western users. Yet, it is unclear how users perceive the usage of "momo", their motivations, and how using this collective anonymity impacts their social interaction. To answer these questions, we conducted interviews with 20 "momo" users. We found that the shared identity "momo" provides an additional layer of anonymity on identity-constrained Chinese social platforms. Users adopted "momo" to engage in more inclusive discussions and to balance anonymity and self-presentation. Moreover, this collective anonymity fosters connections and forms a meaningful group identity in a loosely organized community. We also identified the benefits and risks associated with this unique collective anonymity. This work makes significant contributions to CSCW and HCI research by (1) extending the knowledge of anonymity practices and privacy concerns within non-Western and mainly Chinese contexts. (2) advancing the work on anonymity models by revealing the dual role of the Momo identity in facilitating collective anonymity and community bonds. (3) providing design implications to support future social technologies in identity design and anonymous communities.2025SLSuqi Lou et al.Designing for PrivacyCSCW
Synthia: Visually Interpreting and Synthesizing Feedback for Writing RevisionWhile recent advances in HCI and generative AI have improved authors' access to feedback on their work, the abundance of critiques can overwhelm writers and obscure actionable insights. We introduce Synthia, a system that visually scaffolds feedback-based writing revision with LLM-powered synthesis. Synthia helps authors strategize their revisions by breaking down large feedback collections into interactive visual bubbles that can be clustered, colored, and resized to reveal patterns and highlight valuable suggestions. Bidirectional highlighting links each feedback unit to its original context and relevant parts of the text. Writers can selectively combine feedback units to generate alternative drafts, enabling rapid, parallel exploration of revision possibilities. These interactions support feedback curation, interpretation, and experimentation throughout the revision process. A within-subjects study (N=12) showed that Synthia helped participants identify more helpful feedback, explore more diverse revisions, and revise with greater intentionality and transparency than a GPT-4-based writing interface.2025CZChao Zhang et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationInteractive Data VisualizationUIST
Navigating the Fog: How University Students Recalibrate Sensemaking Practices to Address Plausible Falsehoods in LLM OutputsLLM interfaces, such as ChatGPT, are widely used by students in higher education. However, their reliability is compromised by the tendency to generate plausible yet factually inaccurate content. This issue is particularly critical as the HCI community shows growing interest in designing LLM-based educational technology. Despite this interest, we have yet to learn how plausible falsehoods disrupt students' real-time sensemaking of outputs from imperfectly reliable LLMs, and how students currently attempt to mitigate these negative effects. Thus, we conducted a case study of 15 university students using ChatGPT through think-aloud tasks and semi-structured interviews. We identified recurring patterns of sensemaking, with students facing challenges such as relying on intuitive guesses and feeling overwhelmed by LLM's lengthy, sycophantic, and overconfident responses. They adapted by inducing inconsistencies from the LLM's responses and strategically dividing tasks between themselves and the LLM. Lastly, our study highlights several design implications for future reliable LLM interfaces.2025CZChao Zhang et al.Human-LLM CollaborationOnline Learning & MOOC PlatformsPrivacy by Design & User ControlCUI
"I Need Your Help!" : Facilitating Psychological Communication Between Left-Behind Children and Their Parents with an AI-Powered SandboxIn impoverished regions, limited resources, economic constraints, and low psychological health literacy among guardians often prevent timely support for children's mental health. The absence of migrant worker parents further exacerbates these issues, as they remain unaware of their children's psychological states. Existing AI advancements in psychological tools often overlook the specific needs of left-behind children and lack parental involvement. To address this, we developed DiSandbox, a low-cost AI-powered sandbox system that supports children in creating sandbox works for mental health assessments and engages parents in counseling. DiSandbox uses AI to guide children in sandbox play, analyze creations for psychological insights, and help parents understand their children's mental health, enabling timely intervention. By integrating large language models with sandbox play, DiSandbox is a scalable, reliable, and accessible tool for home use. Qualitative and quantitative studies confirm its usability and provide guidance for future AI applications in children's mental health.2025YSYan Shi et al.Hangzhou Dianzi University, School of Media and DesignCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Mental Health Apps & Online Support CommunitiesDeveloping Countries & HCI for Development (HCI4D)CHI
CharacterCritique: Supporting Children's Development of Critical Thinking through Multi-Agent Interaction in Story ReadingCritical thinking plays a crucial role in children's education for fostering cognitive development, cultivating independent thinking habits, and enhancing their ability to problem-solving. However, the current educational model places greater emphasis on children's understanding of factual knowledge, with relatively less focus on developing critical thinking skills. We present CharacterCritique to support children's critical thinking based on the theory of inquiry dialogue. This tool uses an analytical story as the medium, it encourages dialogue between parents, children, and story characters. Through this process, children continuously engage in interpretation, analysis, explanation, evaluation, and regulation, all of which promote critical thinking and decision-making. Such interaction is supported by multiple agents. In our between-subjects study (n=32), we compared CharacterCritique to traditional storybook reading. The results show that CharacterCritique is more effective at sparking children's interest in deeper discussions. It also better fosters critical thinking, problem-solving skills, and creates more opportunities for parent-child dialogue.2025ZWZizhen Wang et al.Zhejiang UniversityCollaborative Learning & Peer TeachingSTEM Education & Science CommunicationCHI
BrickSmart: Leveraging Generative AI to Support Children's Spatial Language Learning in Family Block PlayBlock-building activities are crucial for developing children's spatial reasoning and mathematical skills, yet parents often lack the expertise to guide these activities effectively. BrickSmart, a pioneering system, addresses this gap by providing spatial language guidance through a structured three-step process: Discovery & Design, Build & Learn, and Explore & Expand. This system uniquely supports parents in 1) generating personalized block-building instructions, 2) guiding parents to teach spatial language during building and interactive play, and 3) tracking children's learning progress, altogether enhancing children's engagement and cognitive development. In a comparative study involving 12 parent-child pairs children aged 6-8 years) for both experimental and control groups, BrickSmart demonstrated improvements in supportiveness, efficiency, and innovation, with a significant increase in children's use of spatial vocabularies during block play, thereby offering an effective framework for fostering spatial language skills in children.2025YLYujia Liu et al.Tsinghua UniversityHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Generative AI (Text, Image, Music, Video)Early Childhood Education TechnologyCHI
Friction: Deciphering Writing Feedback into Writing Revisions through LLM-Assisted ReflectionThis paper introduces Friction, a novel interface designed to scaffold novice writers in reflective feedback-driven revisions. Effective revision requires mindful reflection upon feedback, but the scale and variability of feedback can make it challenging for novice writers to decipher it into actionable, meaningful changes. Friction leverages large language models to break down large feedback collections into manageable units, visualizes their distribution across sentences and issues through a co-located heatmap, and guides users through structured reflection and revision with adaptive hints and real-time evaluation. Our user study (N=16) showed that Friction helped users allocate more time to reflective planning, attend to more critical issues, develop more actionable and satisfactory revision plans, iterate more frequently, and ultimately produce higher-quality revisions, compared to the baseline system. These findings highlight the potential of human-AI collaboration to foster a balanced approach between maximum efficiency and deliberate reflection, supporting the development of creative mastery.2025CZChao Zhang et al.Cornell UniversityHuman-LLM CollaborationAI-Assisted Creative WritingCHI
From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UXThe study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer’s perspective on education and advocacy for ethical designs; and (2) the policymaker’s perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.2024YLYuwen Lu et al.Session 2c: Protecting Users: Legislative Insights, Dark Patterns, and CybersecurityCSCW
Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative StorytellingMathematical language is a cornerstone of a child's mathematical development, and children can effectively acquire this language through storytelling with a knowledgeable and engaging partner. In this study, we leverage the recent advances in large language models to conduct free-form, creative conversations with children. Consequently, we developed Mathemyths, a joint storytelling agent that takes turns co-creating stories with children while integrating mathematical terms into the evolving narrative. This paper details our development process, illustrating how prompt-engineering can optimize LLMs for educational contexts. Through a user study involving 35 children aged 4-8 years, our results suggest that when children interacted with Mathemyths, their learning of mathematical language was comparable to those who co-created stories with a human partner. However, we observed differences in how children engaged with co-creation partners of different natures. Overall, we believe that LLM applications, like Mathemyths, offer children a unique conversational experience pertaining to focused learning objectives.2024CZChao Zhang et al.Cornell UniversityHuman-LLM CollaborationEarly Childhood Education TechnologySTEM Education & Science CommunicationCHI
Wrist-bound Guanxi, Jiazu, and Kuolie: Unpacking Chinese Adolescent Smartwatch-Mediated SocializationAdolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, is reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as "Bump-to-Connect'' and exclusive social platforms. Nonetheless, how these devices influence adolescents' peer experience remains unknown. Addressing this, we interviewed 18 Chinese adolescents (age: 11---16), discovering a smartwatch-mediated social ecosystem. Our findings highlight the ice-breaking role of smartwatches in friendship initiation and their use for secret messaging with local peers. Within the online smartwatch community, peer status is determined by likes and visibility, leading to diverse pursuit activities (eg., chu guanxi, jiazu, kuolie) and negative social dynamics. We discuss the core affordances of smartwatches and Chinese cultural factors that influence adolescent social behavior, and offer implications for designing future wearables that responsibly and safely support adolescent socialization.2024LLLanjing Liu et al.Virginia TechTeleoperated DrivingSmartwatches & Fitness BandsCHI