The Algorithmic Mirror: Knowledge Creation and Self-Perception in Dating ApplicationsAlgorithmic dating applications mediate romance through an "algorithmic mirror," subjecting users to data-driven classifications that shape their self-perception. However, the specific strategies users employ to interpret and strategically manage this reflection remain underexplored. Understanding this dynamic is critical, as navigating the algorithmic gaze demands significant emotional labor and has profound implications for user agency and well-being. Through semi-structured interviews with 15 OkCupid users, I investigated this process of sense-making. I contribute a novel typology of three knowledge forms, Folk, Personal, and Academic, that users construct to redefine themselves against the algorithm. Theoretically, this paper frames the "algorithmic other" as a statistical counterpart to Mead's "generalized other," revealing a core "dual-audience dilemma" where users perform for both humans and machines. These findings inform the design of more transparent and contestable systems that better support user agency.2026NVNadav ViduchinskyBar-Ilan UniversityOnline Dating Platform DesignDigital Emotional Expression & TransmissionCHI
Protosampling: Enabling Free-Form Convergence of Sampling and Prototyping through Canvas-Driven Visual AI GenerationAs an emergent process, creativity relies on explorations via sampling and prototyping for problem construction. These activities compile knowledge, provide a context enveloping the solution, and answer questions. With Generative AI, practitioners can go beyond sampling existing media towards instantly generating and remixing new ones. We refer to this convergence as 'Protosampling'. Using existing literature we ground a definition for protosampling and operationalize it through Atelier, a canvas-like system that leverages a variety of generative image and video models for visual creation. Atelier: (1) blends the spaces for thinking and creation, where both references and generated assets co-exist in one space, (2) provides various encapsulated technical workflows that focus on the activity at hand, and (3) enables navigating emergence through interactive visualizations, smart search, and collections. Protosampling as a lens reframes creative work to emphasize the process itself and how seemingly disjointed thoughts can tightly interweave into a final solution.2026AGAlicia Guo et al.Autodesk ResearchGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsVideo Production & EditingCHI
Understanding Parents’ Desires in Moderating Children’s Interactions with GenAI Chatbots through LLM-Generated ProbesThis paper studies how parents want to moderate children’s interactions with Generative AI Chatbots, with the goal of informing the design of future GenAI parental control tools. We first used an LLM to generate synthetic Child--GenAI Chatbot interaction scenarios and worked with four parents to validate their realism. From this dataset, we carefully selected 12 diverse examples that evoked varying levels of concern and were rated the most realistic. Each example included a prompt and GenAI Chatbot response. We presented these to parents (N=24) and asked whether they found them concerning, why, and how they would prefer to modify the responses and be informed. Our findings reveal three key insights: (1) parents express concern about interactions that current GenAI Chatbot parental controls neglect; (2) parents want fine-grained transparency and moderation at the conversation level; and (3) parents need personalized controls that adapt to their desired strategies and children's ages.2026JDJohn Driscoll et al.University of California San DiegoConversational ChatbotsMental Health Technology for YouthChildren's AI Literacy & Data LiteracyCHI
PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video PrevisualizationIn pre-production, filmmakers and 3D animation experts must rapidly prototype ideas to explore a film's possibilities before full-scale production, yet conventional approaches involve trade-offs in efficiency and expressiveness. Hand-drawn storyboards often lack spatial precision needed for complex cinematography, while 3D previsualization demands expertise and high-quality rigged assets. To address this gap, we present PrevizWhiz, a system that leverages rough 3D scenes in combination with generative image and video models to create stylized video previews. The workflow integrates frame-level image restyling with adjustable resemblance, time-based editing through motion paths or external video inputs, and refinement into high-fidelity video clips. A study with filmmakers demonstrates that our system lowers technical barriers for filmmakers, accelerates creative iteration, and effectively bridges the communication gap, while also surfacing challenges of continuity, authorship, and ethical consideration in AI-assisted filmmaking.2026EHErzhen Hu et al.Autodesk ResearchGenerative AI (Text, Image, Music, Video)3D Modeling & AnimationCreative Collaboration & Feedback SystemsCHI
GroundLink: Exploring How Contextual Meeting Snippets Can Close Common Ground Gaps in Editing 3D Scenes for Virtual ProductionVirtual Production (VP) professionals often face challenges accessing tacit knowledge and creative intent, which are important in forming common ground with collaborators and in contributing more effectively and efficiently to the team. From our formative study (N=23) with a follow-up interview (N=6), we identified the significance and prevalence of this challenge. To help professionals access knowledge, we present GroundLink, a Unity add-on that surfaces meeting-derived knowledge directly in the editor to support establishing common ground. It features a meeting knowledge dashboard for capturing and reviewing decisions and comments, constraint-aware feedforward that proactively informs the editor environment, and cross-modal synchronization that provides referential links between the dashboard and the editor. A comparative study (N=12) suggested that GroundLink help users build common ground with their team while improving perceived confidence and ease of editing the 3D scene. An expert evaluation with VP professionals (N=5) indicated strong potential for GroundLink in real-world workflows.2026GPGun Woo (Warren) Park et al.Autodesk ResearchMixed Reality WorkspacesCreative Collaboration & Feedback Systems3D Modeling & AnimationCHI
Lost in Translation: The Value of Verbalizations in Interpreting 3D Computer-Aided Design WorkflowsAI assistants are transforming creative and knowledge domains, holding similar promise for mechanical design via 3D CAD software. Yet, current AI assistance for CAD relies on geometry or command history, lacking rich design intent. We investigate think-aloud computing as a lightweight approach to capture designers' spoken intent and inform how future AI assistance could leverage this to provide in-situ feedback. Through a three-part study with 10 designers and 10 experts, we (1) recorded designers' think-aloud verbalizations during 3D modelling, (2) compared expert feedback with and without think-aloud recordings, and (3) interviewed the original designers to evaluate feedback quality. Findings show that verbalizations surface rationale, future actions, and challenges --- insights absent from geometric and command data --- that enable feedback attuned to designers' goals. By harnessing think-aloud data, we uncover when to intervene, what to prompt, and characteristics of effective feedback, paving the way for context-aware AI assistance for CAD.2026KCKathy Cheng et al.Autodesk ResearchGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationPrototyping & User TestingCHI
PointAloud: An Interaction Suite for AI-Supported Pointer-Centric Think-Aloud ComputingThink-Aloud Computing, a method for capturing users’ verbalized thoughts during software tasks, allows eliciting rich contextual insights into evolving intentions, struggles, and decision-making processes of users in real-time. However, existing approaches face practical challenges: users often lack awareness of what is captured by the system, are not effectively encouraged to speak, and miss or are interrupted by system feedback. Additionally, thinking aloud should feel worthwhile for users due to the gained contextual AI assistance. To better support and harness Think-Aloud Computing, we introduce PointAloud, a suite of novel AI-driven pointer-centric interactions for in-the-moment verbalization encouragement, low-distraction system feedback, and contextually rich work process documentation alongside proactive AI assistance. Our user study with 12 participants provides insights into the value of pointer-centric think-aloud computing for work process documentation and human-AI co-creation. We conclude by discussing the broader implications of our findings and design considerations for pointer-centric and AI-supported Think-Aloud Computing workflows.2026FGFrederic Gmeiner et al.Autodesk ResearchHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationPrototyping & User TestingCHI
PlayWrite: A Multimodal System for AI Supported Narrative Co-Authoring Through Play in XRCurrent AI writing tools, which rely on text prompts, poorly support the spatial and interactive nature of storytelling where ideas emerge from direct manipulation and play. We present PlayWrite, a mixed-reality system where users author stories by directly manipulating virtual characters and props. A multi-agent AI pipeline interprets these actions into Intent Frames—structured narrative beats visualized as rearrangeable story marbles on a timeline. A large language model then transforms the user’s assembled sequence into a final narrative. A user study (N=13) with writers from varying domains found that PlayWrite fosters a highly improvisational and playful process. Users treated the AI as a collaborative partner, using its unexpected responses to spark new ideas and overcome creative blocks. PlayWrite demonstrates an approach for co-creative systems that move beyond text to embrace direct manipulation and play as core interaction modalities.2026ETEsen K. Tütüncü et al.Autodesk ResearchIdentity & Avatars in XRCreative Collaboration & Feedback SystemsSocial & Collaborative VRCHI
Generative Rotoscoping: A First-Person Autobiographical Exploration on Generative Video-to-Video PracticesThis paper contributes a first-person exploration on AI video-to-video technologies, which I call "Generative Rotoscoping". This includes: insights on the creation process, a set of prototype explorations, and an integrated workflow for multi-modal video generation. Generative video is rapidly evolving and delivering higher quality outputs. While video generation models have potential for film-making and content creation, they lack controllability for creative expression: viable videos can require hundreds of unsuccessful attempts. To understand this emergent practice, and due to the constant evolution of models and limited number of early adopters, I explored Generative Rotoscoping over 12 months and created AI workflows leading to over 40,000 video/image files examining a variety of models and techniques including: structural guidance, frame consistency, image referencing and masks, compositing, among others. Insights from this work can serve as a starting point for designing the next generation of video authoring tools.2025DLDavid LedoGenerative AI (Text, Image, Music, Video)Video Production & EditingC&C
WhatIF: Branched Narrative Fiction Visualization for Authoring Emergent Narratives using Large Language ModelsBranched Narrative Fiction (BNF) are non-linear, text based narrative games, where the player of the game is an active participant shaping the story. Unlike linear narratives, BNF allows players to influence the direction, outcomes, and progression of the plot. A narrative fiction developer designs these branching storylines, creating a dynamic interaction between the player and the narrative which requires significant time and skill. In this work we build and investigate the use of a visual analytics tool to help narrative fiction developers generate and plan these parallel worlds within a BNF. We present WhatIF, a visual analytics tool that aids BNF developers to create BNF graphs, edit the graphs, obtain recommendations, visualize differences between storylines and finally verify their BNF on custom metrics. Through a formative study (3 participants) and a user study (11 participants), we observe that WhatIF helps users plan and prototype their BNF, provides avenues to support iterative refinement of narrative and also aids in removing writer's block. Furthermore, we explore how contemporary generative AI (GenAI) tools can empower game developers to build richer and more immersive narratives.2025AMAditi Mishra et al.Generative AI (Text, Image, Music, Video)AI-Assisted Creative WritingC&C
Exploring the Potential of Metacognitive Support Agents for Human-AI Co-CreationDespite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers’ reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.2025FGFrederic Gmeiner et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationCreative Collaboration & Feedback SystemsDIS
VideoMix: Aggregating How-To Videos for Task-Oriented LearningTutorial videos are a valuable resource for people looking to learn new tasks. People often learn these skills by viewing multiple tutorial videos to get an overall understanding of a task by looking at different approaches to achieve the task. However, navigating through multiple videos can be time-consuming and mentally demanding as these videos are scattered and not easy to skim. We propose VideoMix, a system that helps users gain a holistic understanding of a how-to task by aggregating information from multiple videos on the task. Insights from our formative study (N=12) reveal that learners value understanding potential outcomes, required materials, alternative methods, and important details shared by different videos. Powered by a Vision-Language Model pipeline, VideoMix extracts and organizes this information, presenting concise textual summaries alongside relevant video clips, enabling users to quickly digest and navigate the content. A comparative user study (N=12) demonstrated that VideoMix enabled participants to gain a more comprehensive understanding of tasks with greater efficiency than a baseline video interface, where videos are viewed independently. Our findings highlight the potential of a task-oriented, multi-video approach where videos are organized around a shared goal, offering an enhanced alternative to conventional video-based learning.2025SYSaelyne Yang et al.Online Learning & MOOC PlatformsIntelligent Tutoring Systems & Learning AnalyticsIUI
WhatELSE: Shaping Narrative Spaces at Configurable Level of Abstraction for AI-bridged Interactive StorytellingGenerative AI significantly enhances player agency in interactive narratives (IN) by enabling just-in-time content generation that adapts to player actions. While delegating generation to AI makes IN more interactive, it becomes challenging for authors to control the space of possible narratives - within which the final story experienced by the player emerges from their interaction with AI. In this paper, we present WhatELSE, an AI-bridged IN authoring system that creates narrative possibility spaces from example stories. WhatELSE provides three views (narrative pivot, outline, and variants) to help authors understand the narrative space and corresponding tools leveraging linguistic abstraction to control the boundaries of the narrative space. Taking innovative LLM-based narrative planning approaches, WhatELSE further unfolds the narrative space into executable game events. Through a user study (N=12) and technical evaluations, we found that WhatELSE enables authors to perceive and edit the narrative space and generates engaging interactive narratives at play-time.2025ZLZhuoran Lu et al.Autodesk Research; Purdue University, Computer ScienceGenerative AI (Text, Image, Music, Video)AI-Assisted Creative WritingInteractive Narrative & Immersive StorytellingCHI
From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered AnalysisAI-assisted decision making becomes increasingly prevalent, yet individuals often fail to utilize AI-based decision aids appropriately especially when the AI explanations are absent, potentially as they do not reflect on AI's decision recommendations critically. Large language models (LLMs), with their exceptional conversational and analytical capabilities, present great opportunities to enhance AI-assisted decision making in the absence of AI explanations by providing natural-language-based analysis of AI's decision recommendation, e.g., how each feature of a decision making task might contribute to the AI recommendation. In this paper, via a randomized experiment, we first show that presenting LLM-powered analysis of each task feature, either sequentially or concurrently, does not significantly improve people's AI-assisted decision performance. To enable decision makers to better leverage LLM-powered analysis, we then propose an algorithmic framework to characterize the effects of LLM-powered analysis on human decisions and dynamically decide which analysis to present. Our evaluation with human subjects shows that this approach effectively improves decision makers' appropriate reliance on AI in AI-assisted decision making.2025ZLZhuoyan Li et al.Purdue universityHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationAlgorithmic Fairness & BiasCHI
Paratrouper: Exploratory Creation of Character Cast Visuals Using Generative AIGreat characters are critical to the success of many forms of media, such as comics, games, and films. Designing visually compelling casts of characters requires significant skill and consideration, and there is a lack of specialized tools to support this endeavor. We investigate how AI-driven image-generation techniques can empower creatives to explore a variety of visual design possibilities for individual and groups of characters. Informed by interviews with character designers, Paratrouper is a multi-modal system that enables creating and experimenting with multiple permutations for character casts and visualizing them in various contexts as part of a holistic approach to design. We demonstrate how Paratrouper supports different aspects of the character design process, and share insights from its use by eight creators. Our work highlights the interplay between creative agency and serendipity, as well as the visual interrelationships among character aesthetics.2025JLJoanne Leong et al.MIT, MIT Media LabGenerative AI (Text, Image, Music, Video)3D Modeling & AnimationCHI
To Use or Not to Use: Impatience and Overreliance When Using Generative AI Productivity Support ToolsGenerative AI has the potential to assist people with completing various tasks, but increased productivity is not guaranteed due to challenges such as uncertainty in output quality and unclear processing time. Through an online crowdsourced experiment (N=508), leveraging a “paint by numbers” task to simulate properties of GenAI assistance, we explore how, and how well, users make decisions on whether to use or not use automation to maximize their productivity given varying waiting times and output quality. We observed gaps between user’s actual choices and their optimal choices and characterized these gaps as the “gulf of impatience” and the “gulf of overreliance”. We also distilled strategies that participants adopted when making their decisions. We discuss design considerations in supporting users to make more informed decisions when interacting with GenAI tools and make these tools more useful for improving users’ task performance, productivity and satisfaction.2025HQHan Qiao et al.Autodesk ResearchGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationCHI
AQuA: Automated Question-Answering in Software Tutorial Videos with Visual Anchors Tutorial videos are a popular help source for learning feature-rich software. However, getting quick answers to questions about tutorial videos is difficult. We present an automated approach for responding to tutorial questions. By analyzing 633 questions found in 5,944 video comments, we identified different question types and observed that users frequently described parts of the video in questions. We then asked participants (N=24) to watch tutorial videos and ask questions while annotating the video with relevant visual anchors. Most visual anchors referred to UI elements and the application workspace. Based on these insights, we built AQuA, a pipeline that generates useful answers to questions with visual anchors. We demonstrate this for Fusion 360, showing that we can recognize UI elements in visual anchors and generate answers using GPT-4 augmented with that visual information and software documentation. An evaluation study (N=16) demonstrates that our approach provides better answers than baseline methods.2024SYSaelyne Yang et al.Autodesk Research, School of Computing, KAISTHuman-LLM CollaborationOnline Learning & MOOC PlatformsCHI
SwitchSpace: Understanding Context-Aware Peeking Between VR and Desktop InterfacesCross-reality tasks, like creating or consuming virtual reality (VR) content, often involve inconvenient or distracting switches between desktop and VR. An initial formative study explores cross-reality switching habits, finding most switches are momentary "peeks" between interfaces, with specific habits determined by current context. The results inform a design space for context-aware "peeking" techniques that allow users to view or interact with desktop from VR, and vice versa, without fully switching. We implemented a set of peeking techniques and evaluated them in two levels of a cross-reality task: one requiring only viewing, and another requiring input and viewing. Peeking techniques made task completion faster, with increased input accuracy and reduced perceived workload.2024JWJohann Wentzel et al.University of WaterlooMixed Reality WorkspacesContext-Aware ComputingCHI
GlucoMaker: Enabling Collaborative Customization of Glucose MonitorsMillions of individuals with diabetes use glucose monitors to track blood sugar levels. Research shows that such individuals seek to customize different aspects of their interactions with these devices, including how they engage with, decorate, and wear them. However, it remains challenging to tailor both device form and function to accommodate individual needs. To address this challenge, we introduce GlucoMaker, a system for collaboratively customizing physical design aspects of glucose monitors. Prior to designing GlucoMaker, we conducted a prototyping and focus group study to understand customization preferences and collaboration benefits. GlucoMaker provides individuals with the ability to a) select monitor form and function preferences, b) alter predefined and downloadable digital model files, c) receive feedback on monitor designs from stakeholders, and d) learn technical design aspects. We further demonstrate the versatility and design space of GlucoMaker with three examples of different form and function use cases.2024SLSabrina Lakhdhir et al.University of VictoriaChronic Disease Self-Management (Diabetes, Hypertension, etc.)Customizable & Personalized ObjectsCHI
TimeTunnel: Integrating Spatial and Temporal Motion Editing for Character Animation in Virtual RealityEditing character motion in Virtual Reality is challenging as it requires working with both spatial and temporal data using controls with multiple degrees of freedom. The spatial and temporal controls are separated, making it difficult to adjust poses over time and predict the effects across adjacent frames. To address this challenge, we propose TimeTunnel, an immersive motion editing interface that integrates spatial and temporal control for 3D character animation in VR. TimeTunnel provides an approachable editing experience via KeyPoses and Trajectories. KeyPoses are a set of representative poses automatically computed to concisely depict motion. Trajectories are 3D animation curves that pass through the joints of KeyPoses to represent in-betweens. TimeTunnel integrates spatial and temporal control by superimposing Trajectories and KeyPoses onto a 3D character. We conducted two studies to evaluate TimeTunnel. In our quantitative study, TimeTunnel reduced the amount of time required for editing motion, and saved effort in locating target poses. Our qualitative study with domain experts demonstrated how TimeTunnel is an approachable interface that can simplify motion editing, while still preserving a direct representation of motion.2024QZQian Zhou et al.Autodesk ResearchImmersion & Presence Research3D Modeling & AnimationCHI