Litcius/Paper detail

IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own

X. X. Xu, Rosaleen Xiong, Boyang Wang, David B. Min, Steven P. Dow

2021Proceedings of the ACM on Human-Computer Interaction21 citationsDOIOpen Access PDF

Abstract

Creating truly original ideas requires extensive knowledge of existing ideas. Navigating prior examples can help people to understand what has already been done and to assess the quality of their own ideas through comparison. The creativity literature has suggested that the conceptual distance between a proposed solution and a potential inspiration can influence one's thinking. However, less is known about how creators might use data about conceptual distance when exploring a large repository of ideas. To investigate this, we created a novel tool for exploring examples called IdeateRelate that visualizes 600+ COVID-related ideas, organized by their similarity to a new idea. In an experiment that compared the IdeateRelate visualization to a simple list of examples, we found that users in the Viz condition leveraged both semantic and categorical similarity, curated a more similar set of examples, and adopted more language from examples into their iterated ideas (without negatively affecting the overall novelty). We discuss implications for creating adaptive interfaces that provide creative inspiration in response to designers' ideas throughout an iterative design process.

Topics & Concepts

Computer scienceCreativityRelation (database)Set (abstract data type)NoveltySimilarity (geometry)VisualizationProcess (computing)Categorical variableQuality (philosophy)Human–computer interactionData scienceEpistemologyArtificial intelligencePsychologyData miningProgramming languageImage (mathematics)Social psychologyMachine learningPhilosophyDesign Education and PracticeInnovative Human-Technology InteractionCreativity in Education and Neuroscience
IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own | Litcius