DuoDrama: Supporting Screenplay Refinement Through LLM-Assisted Human ReflectionAI has been increasingly integrated into screenwriting practice. In refinement, screenwriters expect AI to provide feedback that supports reflection across the internal perspective of characters and the external perspective of the overall story. However, existing AI tools cannot sufficiently coordinate the two perspectives to meet screenwriters' needs. To address this gap, we present DuoDrama, an AI system that generates feedback to assist screenwriters' reflection in refinement. To enable DuoDrama, based on performance theories and a formative study with nine professional screenwriters, we design the Experience-Grounded Feedback Generation Workflow for Human Reflection (ExReflect). In ExReflect, an AI agent adopts an experience role to generate experience and then shifts to an evaluation role to generate feedback based on the experience. A study with fourteen professional screenwriters shows that DuoDrama improves feedback quality and alignment and enhances the effectiveness, depth, and richness of reflection. We conclude by discussing broader implications and future directions.2026YTYuying Tang et al.The Hong Kong University of Science and TechnologyHuman-LLM CollaborationAI-Assisted Creative WritingCreative Collaboration & Feedback SystemsCHI
DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent BehaviorsLarge language model (LLM)-based multi-agent systems have demonstrated impressive capabilities in handling complex tasks. However, the complexity of agentic behaviors makes these systems difficult to understand. When failures occur, developers often struggle to identify root causes and to determine actionable paths for improvement. Traditional methods that rely on inspecting raw log records are inefficient, given both the large volume and complexity of data. To address this challenge, we propose a framework and an interactive system, DiLLS, designed to reveal and structure the behaviors of multi-agent systems. The key idea is to organize information across three levels of query completion: activities, actions, and operations. By probing the multi-agent system through natural language, DiLLS derives and organizes information about planning and execution into a structured, multi-layered summary. Through a user study, we show that DiLLS significantly improves developers’ effectiveness and efficiency in identifying, diagnosing, and understanding failures in LLM-based multi-agent systems.2026RSRui Sheng et al.The Hong Kong University of Science and TechnologyHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationExplainable AI (XAI)CHI
When LLMs Enter Everyday Feminism on Chinese Social Media: Opportunities and Risks for Women’s EmpowermentEveryday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like ''women's growth''. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek's advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women's empowerment, and offer design implications for leveraging LLMs to better support such practices.2026RZRunhua ZHANG et al.The Hong Kong University of Science and TechnologyAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasGender & Race Issues in HCICHI
Wearable AR for Restorative Breaks: How Interactive Narrative Experiences Support Relaxation for Young PeopleYoung adults often take breaks from screen-intensive work by consuming digital content on mobile phones, which undermines rest through visual fatigue and inactivity. We introduce a design framework that embeds light break activities into media content on AR smart glasses, balancing engagement and recovery, which employs three strategies: (1) seamlessly guiding users by embedding activity cues aligned with media elements; (2) transitioning to audio-centric formats to reduce visual load while sustaining immersion; and (3) structuring sessions with "rise–peak–closure" pacing for smooth transitions. In a within-subjects study (N=16) comparing passive viewing, reminder-based breaks, and non-narrative activities, InteractiveBreak instantiated from our framework seamlessly guided activities, sustained engagement, and enhanced break quality. These findings demonstrate wearable AR's potential to support restorative relaxation by transforming breaks into engaging, meaningful experiences.2026JWJindu Wang et al.The Hong Kong University of Science and TechnologySocial & Collaborative VRImmersion & Presence ResearchIdentity & Avatars in XRCHI
Interaction-Augmented Instruction: Modeling the Synergy of Prompts and Interactions in Human-GenAI CollaborationText prompt is the most common way for human-generative AI (GenAI) communication. Though convenient, it is challenging to convey fine-grained and referential intent. One promising solution is to combine text prompts with precise GUI interactions, like brushing and clicking. However, there lacks a formal model to capture synergistic designs between prompts and interactions, hindering their comparison and innovation. To fill this gap, via an iterative and deductive process, we develop the Interaction-Augmented Instruction (IAI) model, a compact entity–relation graph formalizing how the combination of interactions and text prompts enhances human-GenAI communication. With the model, we distill twelve recurring and composable atomic interaction paradigms from prior tools, verifying our model’s capability to facilitate systematic design characterization and comparison. Four usage scenarios further demonstrate the model’s utility in applying, refining, and innovating these paradigms. These results illustrate the IAI model’s descriptive, discriminative, and generative power for shaping future GenAI systems.2026LSLeixian Shen et al.The Hong Kong University of Science and TechnologyGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationPrototyping & User TestingCHI
Collaposer: Transforming Photo Collections into Visual Assets for Storytelling with CollagesDigital collage is an artistic practice that combines image cutouts to tell stories. However, preparing cutouts from a set of photos remains a tedious and time-consuming task. A formative study identified three main challenges: 1) inefficient search for relevant photos, 2) manual image cutout, and 3) difficulty in organizing large sets of cutouts. To meet these challenges and facilitate asset preparation for collage, we propose Collaposer, a tool that transforms a collection of photos into organized, ready-to-use visual cutouts based on user-provided story descriptions. Collaposer tags, detects, and segments photos, and then uses an LLM to select central and related labels based on the user-provided story description. Collaposer presents the resulting visuals in varying sizes, clustered according to semantic hierarchy. Our evaluation shows that Collaposer effectively automates the preparation process to produce diverse sets of visual cutouts adhering to the storyline, allowing users to focus on collaging these assets for storytelling.2026JZJiayi Zhou et al.The Hong Kong University of Science and TechnologyGraphic Design & Typography ToolsCreative Collaboration & Feedback SystemsAI-Assisted Writing & Text GenerationCHI
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of ScreenwritersGenerative AI has greatly transformed creative work in various domains, such as screenwriting. To understand this transformation, prior research often focused on capturing a snapshot of human-AI co-creation practice at a specific moment, with less attention to how humans mobilize, regulate, and reflect to form the practice gradually. Motivated by Bandura's theory of human agency, we conducted a two-week study with 19 professional screenwriters to investigate how they embraced AI in their creation process. Our findings revealed that screenwriters not only mindfully planned, foresaw, and responded to AI usage, but, more importantly, through reflections on practice, they developed themselves and human-AI co-creation paradigms, such as cognition, strategies, and workflows. They also expressed various expectations for how future AI should better support their agency. Based on our findings, we conclude this paper with extensive discussion and actionable suggestions to screenwriters, tool developers, and researchers for sustainable human-AI co-creation.2026YTYuying Tang et al.The Hong Kong University of Science and TechnologyGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCreative Collaboration & Feedback SystemsCHI
The Jade Gateway to Trust: Exploring How Socio-Cultural Perspectives Shape Trust Within Chinese NFT CommunitiesToday’s world is witnessing an unparalleled rate of technological transformation. The emergence of non-fungible tokens (NFTs) has transformed how we handle digital assets and value. These tokens have captured the interest of scholars and businesspeople alike. However, NFTs have recently seen a sharp decline in popularity. While cryptocurrency volatility and monetary policies greatly influenced NFT market trends, the community aspects of NFT projects–particularly trust-based interactions–also play a crucial role in NFT adoption and sustainability. From a social computing perspective, understanding these trust dynamics offers valuable insights for the development of both the NFT ecosystem and the broader digital economy. China presents a compelling context for examining these dynamics, offering a unique intersection of technological innovation and traditional cultural values. Through an in-depth qualitative study of Chinese NFT communities, we examine how socio-cultural factors influence trust formation and development. We analyzed discussions from eight prominent WeChat groups dedicated to NFTs and conducted 21 semi-structured interviews with three types of NFT community members. We found that trust in Chinese NFT communities is significantly molded by local cultural values. To be precise, Confucian virtues, such as benevolence, propriety, and integrity, play a crucial role in shaping these trust relationships. Our research identifies three critical trust dimensions in China’s NFT market: (1) technological, (2) institutional, and (3) social. We examined the challenges in cultivating each dimension. Based on these insights, we developed tailored trust-building guidelines for Chinese NFT stakeholders. These guidelines address trust issues that factor into NFT’s declining popularity and could offer valuable strategies for CSCW researchers, developers, and designers aiming to enhance trust in global NFT communities. Our research urges CSCW scholars to take into account the unique socio-cultural contexts when developing trust-enhancing strategies for digital innovations and online interactions.2025YCYi-Fan Cao et al.User Experiences in Online CommunitiesCSCW
CoGrader: Transforming Instructors' Assessment of Project Reports through Collaborative LLM IntegrationGrading project reports are increasingly significant in today’s educational landscape, where they serve as key assessments of students' comprehensive problem-solving abilities. However, it remains challenging due to the multifaceted evaluation criteria involved, such as creativity and peer-comparative achievement. Meanwhile, instructors often struggle to maintain fairness throughout the time-consuming grading process. Recent advances in AI, particularly large language models, have demonstrated potential for automating simpler grading tasks, such as assessing quizzes or basic writing quality. However, these tools often fall short when it comes to complex metrics, like design innovation and the practical application of knowledge, that require an instructor’s educational insights into the class situation. To address this challenge, we conducted a formative study with six instructors and developed CoGrader, which introduces a novel grading workflow combining human-LLM collaborative metrics design, benchmarking, and AI-assisted feedback. CoGrader was found effective in improving grading efficiency and consistency while providing reliable peer-comparative feedback to students. We also discuss design insights and ethical considerations for the development of human-AI collaborative grading systems.2025ZCZixin Chen et al.Human-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsSTEM Education & Science CommunicationUIST
NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding ModificationConversational LLMs have been widely adopted by domain users with limited programming experience to solve domain problems. However, these users often face misalignment between their intent and generated code, resulting in frustration and rounds of clarification. This work first investigates the cause of this misalignment, which dues to bidirectional ambiguity: both user intents and coding tasks are inherently nonlinear, yet must be expressed and interpreted through linear prompts and code sequences. To address this, we propose direct intent–task matching, a new human–LLM interaction paradigm that externalizes and enables direct manipulation of the LLM understanding, i.e., the coding tasks and their relationships inferred by the LLM prior to code generation. As a proof-of-concept, this paradigm is then implemented in NeuroSync, which employs a knowledge distillation pipeline to extract LLM understanding, user intents, and their mappings, and enhances the alignment by allowing users to intuitively inspect and edit them via visualizations. We evaluate the algorithmic components of NeuroSync via technical experiments, and assess its overall usability and effectiveness via a user study (N=12). The results show that it enhances intent–task alignment, lowers cognitive effort, and improves coding efficiency.2025WZWenshuo ZHANG et al.Human-LLM CollaborationExplainable AI (XAI)UIST
PaperBridge: Crafting Research Narratives through Human-AI Co-ExplorationResearchers frequently need to synthesize their own publications into coherent narratives that demonstrate their scholarly contributions. To suit diverse communication contexts, exploring alternative ways to organize one’s work while maintaining coherence is particularly challenging, especially in interdisciplinary fields like HCI where individual researchers' publications may span diverse domains and methodologies. In this paper, we present PaperBridge, a human–AI co-exploration system informed by a formative study and content analysis. PaperBridge assists researchers in exploring diverse perspectives for organizing their publications into coherent narratives. At its core is a bi-directional analysis engine powered by large language models, supporting iterative exploration through both top-down user intent (e.g., determining organization structure) and bottom-up refinement on narrative components (e.g., thematic paper groupings). Our user study (N=12) demonstrated PaperBridge's usability and effectiveness in facilitating the exploration of alternative research narratives. Our findings also provided empirical insights into how interactive systems can scaffold academic communication tasks.2025RZRunhua ZHANG et al.Human-LLM CollaborationData StorytellingComputational Methods in HCIUIST
CineVision: An Interactive Pre-visualization Storyboard System for Director–Cinematographer CollaborationEffective communication between directors and cinematographers is fundamental in film production, yet traditional approaches relying on visual references and hand-drawn storyboards often lack the efficiency and precision necessary during pre-production. We present CineVision, an AI-driven platform that integrates scriptwriting with real-time visual pre-visualization to bridge this communication gap. By offering dynamic lighting control, style emulation based on renowned filmmakers, and customizable character design, CineVision enables directors to convey their creative vision with heightened clarity and rapidly iterate on scene composition. In a 24-participant lab study, CineVision yielded shorter task times and higher usability ratings than two baseline methods, suggesting a potential to ease early-stage communication and accelerate storyboard drafts under controlled conditions. These findings underscore CineVision’s potential to streamline pre-production processes and foster deeper creative synergy among filmmaking teams, particularly for new collaborators. Our code and demo are available at https://github.com/TonyHongtaoWu/CineVision.2025ZWZheng Wei et al.AI-Assisted Creative WritingVideo Production & Editing3D Modeling & AnimationUIST
InSituTale: Enhancing Augmented Data Storytelling with Physical ObjectsAugmented data storytelling enhances narrative delivery by integrating visualizations with physical environments and presenter actions. Existing systems predominantly rely on body gestures or speech to control visualizations, leaving interactions with physical objects largely underexplored. We introduce augmented physical data storytelling, an approach enabling presenters to manipulate visualizations through physical object interactions. To inform this approach, we first conducted a survey of data-driven presentations to identify common visualization commands. We then conducted workshops with nine HCI/VIS researchers to collect mappings between physical manipulations and these commands.Guided by these insights, we developed InSituTale, a prototype that combines object tracking via a depth camera with Vision-LLM for detecting real-world events. Through physical manipulations, presenters can dynamically execute various visualization commands, delivering cohesive data storytelling experiences that blend physical and digital elements. A user study with 12 participants demonstrated that InSituTale enables intuitive interactions, offers high utility, and facilitates an engaging presentation experience.2025KTKentaro Takahira et al.Interactive Data VisualizationContext-Aware ComputingInteractive Narrative & Immersive StorytellingUIST
Branch Explorer: Leveraging Branching Narratives to Support Interactive 360° Video Viewing for Blind and Low Vision Users360° videos enable users to freely choose their viewing paths, but blind and low vision (BLV) users are often excluded from this interactive experience. To bridge this gap, we present Branch Explorer, a system that transforms 360° videos into branching narratives—stories that dynamically unfold based on viewer choices—to support interactive viewing for BLV audiences. Our formative study identified three key considerations for accessible branching narratives: providing diverse branch options, ensuring coherent story progression, and enabling immersive navigation among branches. To address these needs, Branch Explorer employs a multi-modal machine learning pipeline to generate diverse narrative paths, allowing users to flexibly make choices at detected branching points and seamlessly engage with each storyline through immersive audio guidance. Evaluation with 12 BLV viewers showed that Branch Explorer significantly enhanced user agency and engagement in 360° video viewing. Users also developed personalized strategies for exploring 360° content. We further highlight implications for supporting accessible exploration of videos and virtual environments.2025SXShuchang Xu et al.360° Video & Panoramic ContentAccessible GamingUIST
RhythmTA: A Visual-Aided Interactive System for ESL Rhythm Training via Dubbing PracticeEnglish speech rhythm, the temporal patterns of stressed syllables, is essential for English as a second language (ESL) learners to produce natural-sounding and comprehensible speech. Rhythm training is generally based on imitation of native speech. However, it relies heavily on external instructor feedback, preventing ESL learners from independent practice. To address this gap, we present RhythmTA, an interactive system for ESL learners to practice speech rhythm independently via dubbing, an imitation-based approach. The system automatically extracts rhythm from any English speech and introduces novel visual designs to support three stages of dubbing practice: (1) Synchronized listening with visual aids to enhance perception, (2) Guided repeating by visual cues for self-adjustment, and (3) Comparative reflection from a parallel view for self-monitoring. Our design is informed by a formative study with nine spoken English instructors, which identified current practices and challenges. A user study with twelve ESL learners demonstrates that RhythmTA effectively enhances learners’ rhythm perception and shows significant potential for improving rhythm production.2025CCChang Chen et al.Collaborative Learning & Peer TeachingSTEM Education & Science CommunicationSpecial Education TechnologyUIST
"You'll Be Alice Adventuring in Wonderland!" Processes, Challenges, and Opportunities of Creating Animated Virtual Reality StoriesAnimated virtual reality (VR) stories, combining the presence of VR and the artistry of computer animation, offer a compelling way to deliver messages and evoke emotions. Motivated by the growing demand for immersive narrative experiences, more creators are creating animated VR stories. However, a holistic understanding of their creation processes and challenges involved in crafting these stories is still limited. Based on semi-structured interviews with 21 animated VR story creators, we identify ten common stages in their end-to-end creation processes, ranging from idea generation to evaluation, which form diverse workflows that are story-driven or visual-driven. Additionally, we highlight nine unique issues that arise during the creation process, such as a lack of reference material for multi-element plots, the absence of specific functionalities for story integration, and inadequate support for audience evaluation. We compare the creation of animated VR stories to general XR applications and distill several future research opportunities.2025LYLin-Ping Yuan et al.The Hong Kong University of Science and Technology, Department of Computer Science and EngineeringImmersion & Presence ResearchInteractive Narrative & Immersive StorytellingCHI
Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-CreationWith the rise of AI technologies and their growing influence in the screenwriting field, understanding the opportunities and concerns related to AI's role in screenwriting is essential for enhancing human-AI co-creation. Through semi-structured interviews with 23 screenwriters, we explored their creative practices, attitudes, and expectations in collaborating with AI for screenwriting. Based on participants' responses, we identified the key stages in which they commonly integrated AI, including story structure and plot development, screenplay text, goal and idea generation, and dialogue. Then, we examined how different attitudes toward AI integration influence screenwriters' practices across various workflow stages and their broader impact on the industry. Additionally, we categorized their expected assistance using four distinct roles of AI: actor, audience, expert, and executor. Our findings provide insights into AI's impact on screenwriting practices and offer suggestions on how AI can benefit the future of screenwriting.2025YTYuying Tang et al.Hong Kong University of Science and Technology , Academy of Interdisciplinary StudiesHuman-LLM CollaborationAI-Assisted Creative WritingCHI
InterLink: Linking Text with Code and Output in Computational NotebooksComputational notebooks, widely used for ad-hoc analysis and often shared with others, can be difficult to understand because the standard linear layout is not optimized for reading. In particular, related text, code, and outputs may be spread across the UI making it difficult to draw connections. In response, we introduce InterLink, a plugin designed to present the relationships between text, code, and outputs, thereby making notebooks easier to understand. In a formative study, we identify pain points and derive design requirements for identifying and navigating relationships among various pieces of information within notebooks. Based on these requirements, InterLink features a new layout that separates text from code and outputs into two columns. It uses visual links to signal relationships between text and associated code and outputs and offers interactions for navigating related pieces of information. In a user study with 12 participants, those using InterLink were 13.6% more accurate at finding and integrating information from complex analyses in computational notebooks. These results show the potential of notebook layouts that make them easier to understand.2025YLYanna Lin et al.The Hong Kong University of Science and Technology, Department of Computer Science and Engineering; Human-Computer Interaction Institute, Carnegie Mellon UniversityInteractive Data VisualizationKnowledge Worker Tools & WorkflowsCHI
Reflecting on Design Paradigms of Animated Data Video ToolsAnimated data videos have gained significant popularity in recent years. However, authoring data videos remains challenging due to the complexity of creating and coordinating diverse components (e.g., visualization, animation, audio, etc.). Although numerous tools have been developed to streamline the process, there is a lack of comprehensive understanding and reflection of their design paradigms to inform future development. To address this gap, we propose a framework for understanding data video creation tools along two dimensions: what data video components to create and coordinate, including visual, motion, narrative, and audio components, and how to support the creation and coordination. By applying the framework to analyze 46 existing tools, we summarized key design paradigms of creating and coordinating each component based on the varying work distribution for humans and AI in these tools. Finally, we share our detailed reflections, highlight gaps from a holistic view, and discuss future directions to address them.2025LSLeixian Shen et al.The Hong Kong University of Science and Technology, Department of Computer Science and EngineeringGenerative AI (Text, Image, Music, Video)Data StorytellingCHI
Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling ScriptsData analysts frequently employ code completion tools in writing custom scripts to tackle complex tabular data wrangling tasks. However, existing tools do not sufficiently link the data contexts such as schemas and values with the code being edited. This not only leads to poor code suggestions, but also frequent interruptions in coding processes as users need additional code to locate and understand relevant data. We introduce Xavier, a tool designed to enhance data wrangling script authoring in computational notebooks. Xavier maintains users' awareness of data contexts while providing data-aware code suggestions. It automatically highlights the most relevant data based on the user's code, integrates both code and data contexts for more accurate suggestions, and instantly previews data transformation results for easy verification. To evaluate the effectiveness and usability of Xavier, we conducted a user study with 16 data analysts, showing its potential to streamline data wrangling scripts authoring.2025YZYunfan Zhou et al.Zhejiang University, State Key Lab of CAD&CGInteractive Data VisualizationComputational Methods in HCICHI