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Creative sketching partner: an analysis of human-AI co-creativity

Authors:

Safat Siddiqui,

Mary Lou Maher,

Nasrin DehbozorgiAuthors Info & Claims

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages 221 - 230

https://doi.org/10.1145/3377325.3377522

Published: 17 March 2020 Publication History

Abstract

The creative sketching partner (CSP) is a proof of concept intelligent interface to inspire designers while sketching in response to a specified design task. With this interactive system we are studying the effect of an AI model of visual and conceptual similarity for selecting the Al's sketch response as an inspiration to the current state of the user's sketch. Specifically, we are interested in the user's behavior and response to an AI partner when engaged in a design task. By developing deep learning models of the sketches from a large-scale dataset, the user can control the amount of visual and conceptual similarity of the AI response when requesting inspiration from the CSP. We conducted a study with 50 design students to examine the participants' interaction behavior and their self reports. The participants' behavior maps into clusters that are co-related with three types of design creativity: combinatorial, exploratory, and transformational. Our findings demonstrate that the tool can facilitate ideation and overcome design fixation. In addition, analysis suggests that inspiration related to conceptual similarity is more associated with transformational creativity and inspiration related to visual similarity occurs more frequently during the detailed stages of design and is more prevalent with combinatorial creativity.

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Cited By

Wang BZhang KChen ZShen LShen XLiu YBian JShen H(2025)An intelligent font generation system based on stroke inference, mitigating production labor and enhancing design experienceExpert Systems with Applications10.1016/j.eswa.2024.125657263(125657)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125657
Pei YWang LXue C(2024)Human–AI Co-Drawing: Studying Creative Efficacy and Eye Tracking in Observation and CooperationApplied Sciences10.3390/app1418820314:18(8203)Online publication date: 12-Sep-2024
https://doi.org/10.3390/app14188203
Shojaei FShojaei FOsorio Torres JShih P(2024)AI-Generated Art in Art Therapy: Insights from Art Therapists Using a Mixed Methods Approach (Preprint)JMIR Formative Research10.2196/63038Online publication date: 7-Jun-2024
https://doi.org/10.2196/63038
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Index Terms

Creative sketching partner: an analysis of human-AI co-creativity
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Collaborative interaction

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cover image ACM Conferences

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

March 2020

607 pages

ISBN:9781450371186

DOI:10.1145/3377325

General Chairs:
Fabio Paternò,
Nuria Oliver,
Program Chairs:
Cristina Conati,
Lucio Davide Spano,
Nava Tintarev

Copyright © 2020 ACM.

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Publication History

Published: 17 March 2020

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IUI '20: 25th International Conference on Intelligent User Interfaces

March 17 - 20, 2020

Cagliari, Italy

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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30th International Conference on Intelligent User Interfaces

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Cited By

Wang BZhang KChen ZShen LShen XLiu YBian JShen H(2025)An intelligent font generation system based on stroke inference, mitigating production labor and enhancing design experienceExpert Systems with Applications10.1016/j.eswa.2024.125657263(125657)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125657
Pei YWang LXue C(2024)Human–AI Co-Drawing: Studying Creative Efficacy and Eye Tracking in Observation and CooperationApplied Sciences10.3390/app1418820314:18(8203)Online publication date: 12-Sep-2024
https://doi.org/10.3390/app14188203
Shojaei FShojaei FOsorio Torres JShih P(2024)AI-Generated Art in Art Therapy: Insights from Art Therapists Using a Mixed Methods Approach (Preprint)JMIR Formative Research10.2196/63038Online publication date: 7-Jun-2024
https://doi.org/10.2196/63038
Zhang HChen PXie XJiang ZZhou ZSun L(2024)A Hybrid Prototype Method Combining Physical Models and Generative Artificial Intelligence to Support Creativity in Conceptual DesignACM Transactions on Computer-Human Interaction10.1145/368943331:5(1-34)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3689433
Antony VHuang C(2024)ID.8: Co-Creating Visual Stories with Generative AIACM Transactions on Interactive Intelligent Systems10.1145/367227714:3(1-29)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3672277
He JHoude SGonzalez GSilva Moran DRoss SMuller MWeisz J(2024)AI and the Future of Collaborative Work: Group Ideation with an LLM in a Virtual CanvasProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663398(1-14)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3663384.3663398
Muller MWeisz JHoude SRoss S(2024)Drinking Chai with Your (AI) Programming Partner: Value Tensions in the Tokenization of Future Human-AI Collaborative WorkProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663390(1-15)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3663384.3663390
Yoo DJoo J(2024)BI-CST: Behavioral Science-based Creativity Support Tool for Overcoming Design Fixation.Companion Publication of the 2024 ACM Designing Interactive Systems Conference10.1145/3656156.3663704(116-120)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656156.3663704
Takaffoli MLi SMäkelä V(2024)Generative AI in User Experience Design and Research: How Do UX Practitioners, Teams, and Companies Use GenAI in Industry?Proceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660720(1579-1593)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3660720
Ding HFan ZGuehring IGupta GHa WHuan JLiu LOmidvar-Tehrani BWang SZhou HBaeza-Yates RBonchi F(2024)Reasoning and Planning with Large Language Models in Code DevelopmentProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671452(6480-6490)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671452
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