Frontend code, replicated across millions of page views, consumes significant energy and contributes directly to digital emissions. Yet current AI coding assistants, such as GitHub Copilot and Amazon CodeWhisperer, emphasize developer speed and convenience, with energy impact not yet a primary focus. At the same time, existing energy-focused guidelines and metrics have seen limited adoption among practitioners, leaving a gap between research and everyday coding practice. To address this gap, we introduce EcoAssist, an energy-aware assistant integrated into an IDE that analyzes AI-generated frontend code, estimates its energy footprint, and proposes targeted optimizations. We evaluated EcoAssist through benchmarks of 500 websites and a controlled study with 20 developers. Results show that EcoAssist reduced per-website energy by 13–16% on average, increased developers’ awareness of energy use, and maintained developer productivity. This work demonstrates how energy considerations can be embedded directly into AI-assisted coding workflows, supporting developers as they engage with energy implications through actionable feedback.

Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human behavior. However, existing datasets often lack the diversity needed to capture differences in culture, geography, and human-robot interaction-factors that strongly shape appropriate social behavior. To address this gap, we introduce ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation. A large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments, ACME is a large and diverse multi-modal dataset aimed at advancing social navigation research, providing 29.35 hours of onboard robot data and 43.5 hours of overhead pedestrian tracking data. Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech. To facilitate learning navigation policies and predicting pedestrian trajectories, ACME provides 3D and 2D scene features, odometry, interaction information, and human-annotated pedestrian trajectory labels. We make ACME easy to use by providing both human-readable data for each sensor modality as well as raw binary data. Our qualitative and quantitative analyses show that our dataset captures more challenging scenarios and a broader distribution of pedestrian behavior than previous datasets.

Recent advancements in multimodal generative AI (GenAI) enable the creation of personal context-aware real-time agents that, for example, can augment user workflows by following their on-screen activities and providing contextual assistance. However, prototyping such experiences is challenging, especially when supporting people with domain-specific tasks using real-time inputs such as speech and screen recordings. While prototyping an LLM-based proactive support agent system, we found that existing prototyping and evaluation methods were insufficient to anticipate the nuanced situational complexity and contextual immediacy required. To overcome these challenges, we explored a novel user-centered prototyping approach that combines counterfactual video replay prompting and hybrid Wizard-of-Oz methods to iteratively design and refine agent behaviors. This paper discusses our prototyping experiences, highlighting successes and limitations, and offers a practical guide and an open-source toolkit for UX designers, HCI researchers, and AI toolmakers to build more user-centered and context-aware multimodal agents.

Generative AI agents are increasingly used in interaction design to facilitate ideation and offer critique, often following their own internal reasoning. These interactions tend to add design ideas and expand the design space. Our work explores an antagonistic role for design agents, prompting designers to engage with stakeholder tension. We built an AI agent inspired by adversarial design theory that enacts constructive conflict. We examine the agent's influence in a between-subjects experiment with 45 design students across three conditions: Self Reflection (unsupported review of the design proposal), Stepwise Guidance (written prompts that walk designers through a constructive-conflict framework), and Interactive Engagement (an AI agent that enacts the constructive-conflict framework interactively by synthesizing stakeholder pushback). The latter two conditions share the framework but differ in whether it is self-enacted or agent-enacted. Results show that, compared with Self Reflection, both the Stepwise Guidance and Interactive Engagement groups reported significantly higher self-reconsideration and made more improvements to their design proposals. Compared with Stepwise Guidance, the antagonistic agent introduced more conflictual perspectives, and participants in the Interactive Engagement condition generated and discarded more ideas. These findings suggest that agent-enacted constructive conflict can turn reconsideration into concrete design actions and deepen engagement with divergent stakeholder perspectives.

The engineering design research community has studied agentic AI systems that use Large Language Model (LLM) agents to automate the engineering design process. However, these systems are prone to some of the same pathologies that plague humans. Just as human designers, LLM design agents can fixate on existing paradigms and fail to explore alternatives when solving design challenges, potentially leading to suboptimal solutions. In this work, we propose (1) a novel Self-Regulation Loop (SRL), in which the Design Agent self-regulates and explicitly monitors its own metacognition, and (2) a novel Co-Regulation Design Agentic Loop (CRDAL), in which a Metacognitive Co-Regulation Agent assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks. In the battery pack design problem examined here, we found that the novel CRDAL system generates designs with better performance, without significantly increasing the computational cost, compared to a plain Ralph Wiggum Loop (RWL) and the metacognitively self-assessing Self-Regulation Loop (SRL). Also, we found that the CRDAL system navigated through the latent design space more effectively than both SRL and RWL. However, the SRL did not generate designs with significantly better performance than RWL, even though it explored a different region of the design space. The proposed system architectures and findings of this work provide practical implications for future development of agentic AI systems for engineering design.

Animated lyric videos transform song lyrics into dynamic visual experiences, offering a powerful medium for artistic expression and audience engagement. However, creating these videos is challenging, requiring expertise in audio, typography, graphic design, and animation, making it inaccessible to novices. To address this challenge, we introduce Visual Lyrics, a proof-of-concept system for generating animated lyric videos controlled with an augmented text editor interface. We examined existing lyric videos to distill a taxonomy and design guidelines, informing the design of Visual Lyrics. Our key insight is a multimodal music analysis pipeline based on the taxonomy and leveraging LLM’s strong natural language understanding and code generation capabilities to synthesize creative and semantically meaningful animations. We collected a dataset of over 300 code-driven creative text animations to serve as inspiration for our LLM-driven pipeline, which we open source. In a user study, Visual Lyrics enabled novices to easily create high-quality animated lyric videos with high ratings of enjoyment, inspiration, and exploration.

Modern vehicles, with advanced AI voice and autonomous navigation features, extend beyond traditional driving but, like any autonomous system, can potentially make mistakes or behave in ways unexpected by users. Although providing real-time explanations can alleviate some confusion, constant information can overwhelm users and potentially cause unnecessary distractions. Some situations may require explanations or corrective vehicle behavior, and thus, recognizing user response to unexpected vehicle behavior is critical. To investigate such user responses, our study focused on collecting and analyzing user behavioral responses to unexpected events while interacting with a fully autonomous vehicle in a driving simulator. We also aimed to address the lack of datasets capturing subtle user responses (facial, spoken language, physiological signals) to in-vehicle events, as existing datasets primarily focus on strong emotional signals in conventional human-driven cars and user response to external road and traffic conditions. Users were exposed to stimuli designed to induce surprise, confusion, and frustration while performing a secondary task on a tablet and interacting with the vehicle through voice commands and in-vehicle displays. We collected a multi-modal dataset with video, audio, and heart rate data and gained insights into subtle user responses that underscored the need for further investigation of nuanced user behaviors. These observations highlight the importance of designing vehicles that recognize and adapt to occupants' behavior, potentially improving their experience.

Compliant mechanisms enable the creation of compact and easy-to-fabricate devices for tangible interaction. This work explores interconnected compliant mechanisms consisting of multiple joints and rigid bodies to transmit and process displacements as signals that result from physical interactions. As these devices are difficult to design due to their vast and complex design space, we developed a graph-based design algorithm and computational tool to help users program and customize such computational functions and procedurally model physical designs. When combined with active materials with actuation and sensing capabilities, these devices can also render and detect haptic interaction. Our design examples demonstrate the tool's capability to respond to relevant HCI concepts, including building modular physical interface toolkits, encrypting tangible interactions, and customizing user augmentation for accessibility. We believe the tool will facilitate the generation of new interfaces with enriched affordance.

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows.

We present BioSpark, a system for analogical innovation designed to act as a creativity partner in reducing the cognitive effort in finding, mapping, and creatively adapting diverse inspirations. While prior approaches have focused on initial stages of finding inspirations, BioSpark uses LLMs embedded in a familiar, visual, Pinterest-like interface to go beyond inspiration to supporting users in identifying the key solution mechanisms, transferring them to the problem domain, considering tradeoffs, and elaborating on details and characteristics. To accomplish this BioSpark introduces several novel contributions, including a tree-of-life enabled approach for generating relevant and diverse inspirations, as well as AI-powered cards including 'Sparks' for analogical transfer; 'Trade-offs' for considering pros and cons; and 'Q&A' for deeper elaboration. We evaluated BioSpark through workshops with professional designers and a controlled user study, finding that using BioSpark led to a greater number of generated ideas; those ideas being rated higher in creative quality; and more diversity in terms of biological inspirations used than a control condition. Our results suggest new avenues for creativity support tools embedding AI in familiar interaction paradigms for designer workflows.

In North America, bus operators are essential but undervalued public servants — the ''human infrastructure'' of public transit. Transit workers face a range of largely invisible health and safety issues that have worsened in recent years. As more attention is directed toward new technologies being commercialized in the sector these operational challenges remain largely unaddressed. Our paper contributes to a turn ''back to labor'' and describes issues bus operators face on the job. Through a diary study of bus operators' working conditions we detail how punctuated moments of workplace violence, inhumane scheduling, and unsafe operational conditions become prolonged infrastructural failure. We outline how CSCW researchers and practitioners can contribute to the design of transit systems that enhance worker dignity and contribute to ongoing efforts to address urgent health and safety concerns.

Divergent thinking in the ideation stage of creative problem-solving demands that individuals explore a broad design space. Yet this exploration rarely follows a neat, linear sequence; problem-solvers constantly shift among searching, creating, and evaluating ideas. Existing interfaces either impose rigid, step-by-step workflows or permit unguided free-form exploration. To strike a balance between flexibility and guidance for augmenting people's efficiency and creativity, we introduce a human-AI collaborative workflow that supports a fluid ideation process. The system surfaces three opt-in aids: (1) high-level schemas to uncover alternative ideas, (2) risk analysis with mitigation suggestions, and (3) steering system-generated suggestions. Users can invoke these supports at any moment, allowing seamless back-and-forth movement among design actions to maintain creative momentum.

Creative ideation relies on exploring diverse stimuli, but the overwhelming abundance of information often makes it difficult to identify valuable insights or reach the `aha' moment. Traditional methods for accessing design stimuli lack organization and fail to support users in discovering promising opportunities within large idea spaces. In this position paper, we explore how AI can be leveraged to structure, organize, and surface relevant stimuli, guiding users in both exploring idea spaces and mapping insights back to their design challenges.

Future high-level autonomous vehicles (AVs) will enable drivers to engage in non-driving-related tasks (NDRTs) during autopilot. Occasionally, an in-vehicle agent may need to notify drivers of important, yet not urgent, information. Through a four-session interruptibility study on a desktop autonomous driving simulator, we investigated how drivers assess their availability to receive notifications by rating moments as good or bad for interruption. Our results suggest drivers fall into four notification availability groups: always available, prioritizing NDRTs, task-content dependent, and mental-state dependent. Using multimodal behavioral data of the participants and vehicle data from the simulation, we trained a proof-of-concept classification model to determine the appropriate timing to send non-emergency notifications to drivers doing NDRTs. Head pose and gaze direction data from the eye tracker were crucial in the predictions. Based on our quantitative modeling and qualitative observation, we discuss the feasibility of notification timing prediction in the real world and design considerations from individual, task, and context perspectives.

Collaborative engineering design is increasingly important for modern engineering practices as projects routinely require collaboration across multiple domains. Reaching shared understanding within the team is a critical factor in constructing a successful and enjoyable collaboration. One way to promote shared understanding is through the use of design artifacts and design representations as boundary objects. Different design representations have unique characteristics that benefit the engineering design process but could also hinder the development of shared understanding. It is important to identify the limitations of the design artifacts to select the suitable design artifact for the situation and mitigate potential adverse effects, including design fixation and miscommunication. Despite previous studies’ findings, there are still unsolved questions regarding the exact effect of the modality of the design representations on the development of team-shared understanding. This work examines three types of commonly used design representations in the engineering design community, namely, textual description, hand sketch and engineering CAD model. Their unique effect on the development of shared understanding is investigated in a collaborative engineering design setting. The results indicate that the modality of the design artifact would affect the development of shared understanding, and using visual representations can yield better team outcomes regardless of the modality complexity, mainly for design structures. This work shows the importance of using the proper design representation in collaborative engineering design tasks, and such a finding is a critical and timely reminder in the current age when team interactions constantly involve text-dominant online communications.

Effective ideation requires both broad exploration of diverse ideas and deep evaluation of their potential. Generative AI can support such processes, but current tools typically emphasize either generating many ideas or supporting in-depth consideration of a few, lacking support for both. Research also highlights risks of over-reliance on LLMs, including shallow exploration and negative creative outcomes. We present FlexMind, an AI-augmented system that scaffolds iterative exploration of ideas, tradeoffs, and mitigations. FlexMind exposes users to a broad set of ideas while enabling a lightweight transition into deeper engagement. In a study comparing ideation with FlexMind to ChatGPT, participants generated higher-quality ideas with FlexMind, due to both broader exposure and deeper engagement with tradeoffs. By scaffolding ideation across breadth, depth, and reflective evaluation, FlexMind empowers users to surface ideas that might otherwise go unnoticed or be prematurely discarded.

The maker movement embodies a resurgence in DIY creation, merging physical craftsmanship and arts with digital technology support. However, mere technological skills and creativity are insufficient for economically and psychologically sustainable practice. By illuminating and smoothing the path from “maker" to “maker entrepreneur," we can help broaden the viability of making as a livelihood. Our research centers on makers who design, produce, and sell physical goods. In this work, we explore the transition to entrepreneurship for these makers and how technology can facilitate this transition online and offline. We present results from interviews with 20 USA-based maker entrepreneurs (i.e., lamps, stickers), six creative service entrepreneurs (i.e., photographers, fabrication), and seven support personnel (i.e., art curator, incubator director). Our findings reveal that many maker entrepreneurs 1) are makers first and entrepreneurs second; 2) struggle with business logistics and learn business skills as they go; and 3) are motivated by non-monetary values. We discuss training and technology-based design implications and opportunities for addressing challenges in developing economically sustainable businesses around making.