UI Remix: Supporting UI Design Through Interactive Example Retrieval and RemixingDesigning user interfaces (UIs) is a critical step when launching products, building portfolios, or personalizing projects, yet end users without design expertise often struggle to articulate their intent and to trust design choices. Existing example-based tools either promote broad exploration, which can cause overwhelm and design drift, or require adapting a single example, risking design fixation. We present UI Remix, an interactive system that supports mobile UI design through an example-driven design workflow. Powered by a multimodal retrieval-augmented generation (MMRAG) model, UI Remix enables iterative search, selection, and adaptation of examples at both the global (whole interface) and local (component) level. To foster trust, it presents source transparency cues such as ratings, download counts, and developer information. In an empirical study with 24 end users, UI Remix significantly improved participants' ability to achieve their design goals, facilitated effective iteration, and encouraged exploration of alternative designs. Participants also reported that source transparency cues enhanced their confidence in adapting examples. Our findings suggest new directions for AI-assisted, example-driven systems that empower end users to design with greater control, trust, and openness to exploration.2026JWJunling Wang et al.Department of Computer Science, ETH AI CenterPrototyping & User TestingGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationIUI
StepMIND: A Visual Framework for Stepwise, Multimodal, and Bidirectional Explanations of AI-Generated Data Analysis PipelineArtificial intelligence (AI) enables users to generate data visualizations from natural language descriptions, lowering the barrier to data exploration. However, AI-generated visualizations often present only the final output, lacking transparency and limiting users' ability to verify, interpret, or refine the results. To address this, we introduce \stepmindnospace, a generalizable visual framework that enhances explainability and interactivity in AI-generated data analysis pipelines. \stepmind integrates four dimensions: (1) Stepwise Refinement, allowing users to engage in the AI decision process; (2) Multimodal Explanations, combining natural language, structured notation, direct manipulation, and content visualization for accessible interpretation; (3) Bidirectional Editing, enabling seamless updates across modalities; and (4) Familiar Interaction Models, such as code editor and spreadsheet-based manipulations, to support both technical and non-technical users. To demonstrate its utility, we apply \stepmind in \stagenospace, a case study system for AI-assisted data visualization. A within-subject user study (N=20) shows that \stage significantly improves user confidence and trust, reduces cognitive load, and facilitates both exploratory and corrective refinements. Our findings further suggest that \stepmind can generalize to broader AI-assisted workflows, offering a visible and interactive approach to explainable AI.2026YWYang Wu et al.ETH ZurichExplainable AI (XAI)Interactive Data VisualizationAI-Assisted Decision-Making & AutomationIUI
AI and My Values: User Perceptions of LLMs’ Ability to Extract, Embody, and Explain Human Values from Casual ConversationsDoes AI understand human values? While this remains an open philosophical question, we take a pragmatic stance by introducing VAPT, the Value-Alignment Perception Toolkit, for studying how LLMs reflect people's values and how people judge those reflections. 20 participants texted a chatbot over a month, then completed a 2-hour interview with our toolkit evaluating AI's ability to extract (pull details regarding), embody (make decisions guided by), and explain (provide proof of) their values. 13 participants ultimately left our study convinced that AI can understand human values. Thus, we warn about "weaponized empathy": a design pattern that may arise in interactions with value-aware, yet welfare-misaligned conversational agents. VAPT offers a new way to evaluate value-alignment in AI systems. We also offer design implications to evaluate and responsibly build AI systems with transparency and safeguards as AI capabilities grow more inscrutable, ubiquitous, and posthuman into the future.2026BYBhada Yun et al.ETH ZürichHuman-LLM CollaborationExplainable AI (XAI)AI Ethics, Fairness & AccountabilityCHI
Exploring the Impacts and Challenges of Vibe Coding Paradigm to Children's Programming Learning and PracticesRecent advances in generative AI have introduced a new programming paradigm—vibe coding, a natural language–driven mode of AI collaboration. While promising for adults, little is known about how children engage with this approach, especially in block-based environments. To explore this gap, we conducted workshops with children of varying Scratch experience (n=41) and interviewed five Scratch teachers. Our study investigates how vibe coding impacts children’s programming learning and practice, and what challenges arise. Findings show that vibe coding has both positive and negative impacts across three key contexts of children’s programming experience: acquisition, application, and creation. Across the stages of vibe coding—goal articulation, information interpretation, and outcome evaluation—children encounter distinct challenges. By examining the mismatches between core assumptions of vibe coding and children’s needs, and analyzing its applicability across different contexts, we offer child-centered design implications for future vibe coding systems and GenAI tools.2026JSJanice Jianing SI et al.University of MacauProgramming Education & Computational ThinkingChildren's AI Literacy & Data LiteracyHuman-LLM CollaborationCHI
Git Takes Two: Split-View Awareness for Collaborative Learning of Distributed Workflows in GitGit is widely used for collaborative software development, but it can be challenging for newcomers. While most learning tools focus on individual workflows, Git is inherently collaborative. We present GitAcademy, a browser-based learning platform that embeds a full Git environment with a split-view collaborative mode: learners work on their own local repositories connected to a shared remote repository, while simultaneously seeing their partner's actions mirrored in real time. This design is not intended for everyday software development, but rather as a training simulator to build awareness of distributed states, coordination, and collaborative troubleshooting. In a within-subjects study with 13 pairs of learners, we found that the split-view interface enhanced social presence, supported peer teaching, and was consistently preferred over a single-view baseline, even though performance gains were mixed. We further discuss how split-view awareness can serve as a training-only scaffold for collaborative learning of Git and other distributed technical systems.2026JBJoel Bucher et al.ETH ZürichCollaborative Learning & Peer TeachingDistributed Team CollaborationCrowdsourcing Task Design & Quality ControlCHI
"Bespoke Bots'': Diverse Instructor Needs for Customizing Generative AI Classroom ChatbotsInstructors are increasingly experimenting with AI chatbots for classroom support. To investigate how instructors adapt chatbots to their own contexts, we first analyzed existing resources that provide prompts for educational purposes. We identified ten common categories of customization, such as persona, guardrails, and personalization. We then conducted interviews with ten university STEM instructors and asked them to card-sort the categories into priorities. We found that instructors consistently prioritized the ability to customize chatbot behavior to align with course materials and pedagogical strategies and de-prioritized customizing persona/tone. However, their prioritization of other categories varied significantly by course size, discipline, and teaching style, even across courses taught by the same individual, highlighting that no single design can meet all contexts. These findings suggest that modular AI chatbots may provide a promising path forward. We offer design implications for educational developers building the next generation of customizable classroom AI systems.2026IHIrene Hou et al.University of California, San DiegoHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsConversational ChatbotsCHI
From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software EngineeringJuniors enter as AI‑natives, seniors adapted mid‑career. AI is not just changing how engineers code–it is reshaping who holds agency across work and professional growth. We contribute junior–senior accounts on their usage of agentic AI through a three-phase mixed-methods study: ACTA combined with a Delphi process with 5 seniors, an AI-assisted debugging task with 10 juniors, and blind reviews of junior prompt histories by 5 more seniors. We found that agency in software engineering is primarily constrained by organizational policies rather than individual preferences, with experienced developers maintaining control through detailed delegation while novices struggle between over-reliance and cautious avoidance. Seniors leverage pre-AI foundational instincts to steer modern tools and possess valuable perspectives for mentoring juniors in their early AI-encouraged career development. From synthesis of results, we suggest three practices that focus on preserving agency in software engineering for coding, learning, and mentorship, especially as AI grows increasingly autonomous.2026DFDana Feng et al.NoneHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationGenerative AI (Text, Image, Music, Video)CHI
The Elephant in the Syntax: A Comparative Study of Semantics‑First, Block‑Based, and Textual ProgrammingSyntax remains a major barrier for novices. Although block-based systems reduce or eliminate syntax errors, conditionals still challenge learners, likely because their semantics remain implicit. In this paper, we address this problem by introducing a semantics-first, state-visible programming approach inspired by the classic visual language Stagecast Creator. To demonstrate its usefulness, we designed Elephant, a unified, Karel-like research platform that supports three equally expressive programming paradigms: (i) semantics-first programming, (ii) block-based programming with the Blockly library, and (iii) text-based programming in JavaScript with domain-specific libraries. We then deployed Elephant in two within-subjects studies with secondary-school students (N = 39) to compare semantics-first programming to textual and block-based baselines, keeping the program semantics constant across modes and reducing cross-tool confounds. Results indicate, among other things, that semantics-first programming yields significantly higher task performance, suggesting that increasing the visibility of the program state during program composition could support greater outcomes in secondary computing education.2026TWTheo B. Weidmann et al.ETH ZurichProgramming Education & Computational ThinkingK-12 Digital Education ToolsCHI
Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot InteractionAs AI chatbots shift from tools to companions, critical questions arise: who controls the conversation in human-AI chatrooms? This paper explores perceived human and AI agency in sustained conversation. We report a month-long longitudinal study with 22 adults who chatted with "Day", an LLM companion we built, followed by a semi-structured interview with post-hoc elicitation of notable moments, cross-participant chat reviews, and a 'strategy reveal' disclosing "Day's" goal for each conversation. We discover agency manifests as an emergent, shared experience: as participants set boundaries and the AI steered intentions, control was co-constructed turn-by-turn. We introduce a 3-by-4 framework mapping actors (Human, AI, Hybrid) by their action (Intention, Execution, Adaptation, Delimitation), modulated by individual and environmental factors. We argue for translucent design (transparency-on-demand) and provide implications for agency self-aware conversational agents.2026BYBhada Yun et al.ETH ZürichAgent Personality & AnthropomorphismHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media ConversationsSocial media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.2025XSXiaotian Su et al.Toxic and Anti-Social BehaviorCSCW
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich SoftwareLarge Language Model (LLM)-based in-application assistants, or copilots, can automate software tasks, but users often prefer learning by doing, raising questions about the optimal level of automation for an effective user experience. We investigated two automation paradigms by designing and implementing a fully automated copilot (AutoCopilot) and a semi-automated copilot (GuidedCopilot) that automates trivial steps while offering step-by-step visual guidance. In a user study (N=20) across data analysis and visual design tasks, GuidedCopilot outperformed AutoCopilot in user control, software utility, and learnability, especially for exploratory and creative tasks, while AutoCopilot saved time for simpler visual tasks. A follow-up design exploration (N=10) enhanced GuidedCopilot with task-and state-aware features, including in-context preview clips and adaptive instructions. Our findings highlight the critical role of user control and tailored guidance in designing the next generation of copilots that enhance productivity, support diverse skill levels, and foster deeper software engagement.2025AKAnjali Khurana et al.Simon Fraser University, Computing ScienceHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Towards Dialogic and On-Demand Metaphors for Interdisciplinary ReadingThe interdisciplinary field of Human-Computer Interaction (HCI) thrives on productive engagement with different domains, yet this engagement often breaks due to idiosyncratic writing styles and unfamiliar concepts. Inspired by the dialogic model of abstract metaphors, as well as the potential of Large Language Models (LLMs) to produce on-demand support, we investigate the use of metaphors to facilitate engagement between Science and Technology Studies (STS) and System HCI. Our reflective-style survey with early-career HCI researchers (N=48) reported that limited prior exposure to STS research can hinder perceived openness of the work, and ultimately interest in reading. The survey also revealed that metaphors enhance likelihood to continue reading STS papers, and alternative perspectives can build critical thinking skills to mitigate potential risks of LLM-generated metaphors. We lastly offer a specified model of metaphor exchange (within this generative context) that incorporates alternative perspectives to construct shared understanding in interdisciplinary engagement.2025MYMatin Yarmand et al.University of California San Diego, Computer Science and Engineering; University of California San Diego, The Design LabHuman-LLM CollaborationPrivacy by Design & User ControlTechnology Ethics & Critical HCICHI
DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-DecompositionDecomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering independent problem-solving and critical thinking. To address this, we introduce Decomposition Box (DBox), an interactive LLM-based system that scaffolds and adapts to learners' personalized construction of a step tree through a "learner-LLM co-decomposition" approach, providing tailored support at an appropriate level. A within-subjects study (N=24) found that compared to the baseline, DBox significantly improved learning gains, cognitive engagement, and critical thinking. Learners also reported a stronger sense of achievement and found the assistance appropriate and helpful for learning. Additionally, we examined DBox's impact on cognitive load, identified usage patterns, and analyzed learners' strategies for managing system errors. We conclude with design implications for future AI-powered tools to better support algorithmic programming education.2025SMShuai Ma et al.The Hong Kong University of Science and TechnologyHuman-LLM CollaborationProgramming Education & Computational ThinkingIntelligent Tutoring Systems & Learning AnalyticsCHI
Towards Feature Engineering with Human and AI’s Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering DesignAs AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where both human ingenuity and AI capabilities play pivotal roles. Despite the existence of AI-generated recommendations for FE, there remains a limited understanding of how to effectively integrate and utilize humans' and AI's knowledge. To address this gap, we design a readily usable prototype, human\&AI-assisted FE in Jupyter notebooks. It harnesses the strengths of humans and AI to provide feature suggestions to users, seamlessly integrating these recommendations into practical workflows. Using the prototype as a research probe, we conducted an exploratory study to gain valuable insights into data science practitioners' perceptions, usage patterns, and their potential needs when presented with feature suggestions from both humans and AI. Through qualitative analysis, we discovered that the "Creator" of the feature (i.e., AI or human) significantly influences users' feature selection, and the semantic clarity of the suggested feature greatly impacts its adoption rate. Furthermore, our findings indicate that users perceive both differences and complementarity between features generated by humans and those generated by AI. Lastly, based on our study results, we derived a set of design recommendations for future human & AI FE design. Our findings unveil vast collaborative potential between humans and AI in the field of FE.2024QZQian Zhu et al.Human-LLM CollaborationAI-Assisted Decision-Making & AutomationComputational Methods in HCIDIS