Creative Blends of Visual ConceptsVisual blends combine elements from two distinct visual concepts into a single, integrated image, with the goal of conveying ideas through imaginative and often thought-provoking visuals. Communicating abstract concepts through visual blends poses a series of conceptual and technical challenges. To address these challenges, we introduce Creative Blends, an AI-assisted design system that leverages metaphors to visually symbolize abstract concepts by blending disparate objects. Our method harnesses commonsense knowledge bases and large language models to align designers’ conceptual intent with expressive concrete objects. Additionally, we employ generative text-to-image techniques to blend visual elements through their overlapping attributes. A user study (N=24) demonstrated that our approach reduces participants’ cognitive load, fosters creativity, and enhances the metaphorical richness of visual blend ideation. We explore the potential of our method to expand visual blends to include multiple object blending and discuss the insights gained from designing with generative AI.2025ZSZhida Sun et al.Shenzhen University, CSSEGenerative AI (Text, Image, Music, Video)AI-Assisted Creative WritingGraphic Design & Typography ToolsCHI
Blue Ceramics: Co-designing Morphing Ceramics for Seagrass Meadow Restoration Seagrass meadows are twice as effective as forests at capturing and storing carbon, but human activities have caused them to gradually disappear over the last few decades. We take a nature-centered design approach on contextual inquiry and collaborative designs methods to consolidate knowledge from marine and material sciences to industrial design. This pictorial documents a dialogue between designers and scientists to co-create an ecological intervention using digital fabrication to manufacture morphing ceramics for seagrass meadow restoration.2022RARachel Ann Arredondo et al.Ecological Design & Green ComputingFood Culture & Food InteractionC&C
Scaling Creative Inspiration with Fine-Grained Functional Aspects of Product IdeasLarge repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.2022THTom Hope et al.Allen Institute , University of WashingtonGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsCHI
A Promise Is A Promise: The Effect of Commitment Devices on Computer Security IntentionsCommitment devices are a technique from behavioral economics that have been shown to mitigate the effects of present bias---the tendency to discount future risks and gains in favor of immediate gratifications. In this paper, we explore the feasibility of using commitment devices to nudge users towards complying with varying online security mitigations. Using two online experiments, with over 1,000 participants total, we offered participants the option to be reminded or to schedule security tasks in the future. We find that both reminders and commitment nudges can increase users' intentions to install security updates and enable two-factor authentication, but not to configure automatic backups. Using qualitative data, we gain insights into the reasons for postponement and how to improve future nudges. We posit that current nudges may not live up to their full potential, as the timing options offered to users may be too rigid.2019AFAlisa Frik et al.International Computer Science Institute & University of California, BerkeleyPrivacy by Design & User ControlPasswords & AuthenticationPrivacy Perception & Decision-MakingCHI
Digital Konditorei: Programmable Taste Structures using a Modular MoldDigital Gastronomy (DG) is a culinary concept that enhances traditional cooking with new HCI capabilities, rather than replacing the chef with an autonomous machine. Preliminary projects demonstrate implementation of DG via the deployment of digital instruments in a kitchen. Here we contribute an alternative solution, demonstrating the use of a modular (silicone) mold and a genetic mold-arrangement algorithm to achieve a variety of shape permutations for a recipe, allowing the control of taste structures in the dish. The mold overcomes the slow production time of 3D food printing, while allowing for a high degree of flexibility in the numerous shapes produced. This flexibility enables us to satisfy chefs’ and diners’ diverse requirements. We present the mold’s logic, arithmetic, design and special parts, the evolutionary algorithm, and a recipe, exploiting a new digital cooking concept of programmable edible taste structures and taste patterns to enrich user interaction with a given recipe.2018AZAmit Zoran et al.Hebrew UniversityCustomizable & Personalized ObjectsFood Culture & Food InteractionCHI
SOLVENT: A Mixed Initiative System for Finding Analogies between Research PapersScientific discoveries are often driven by finding analogies in distant domains, but the growing number of papers makes it difficult to find relevant ideas in a single discipline, let alone distant analogies in other domains. To provide computational support for finding analogies across domains, we introduce SOLVENT, a mixed-initiative system where humans annotate aspects of research papers that denote their background (the high-level problems being addressed), purpose (the specific problems being addressed), mechanism (how they achieved their purpose), and findings (what they learned/achieved), and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers. We demonstrate that this system finds more analogies than baseline information-retrieval approaches; that annotators and annotations can generalize beyond domain; and that the resulting analogies found are useful to experts. These results demonstrate a novel path towards computationally supported knowledge sharing in research communities.2018JCJoel Chan et al.Research MethodsCSCW
Analogy Mining for Specific Design NeedsFinding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute an analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.2018KGKarni Gilon et al.Hebrew University of JerusalemMental Health Apps & Online Support CommunitiesCrowdsourcing Task Design & Quality ControlCHI