The Effects of Generative AI on Design Fixation and Divergent Thinking

要旨

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants' chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI's suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows.

著者
Samangi Wadinambiarachchi
University of Melbourne, Melbourne, VIC, Australia
Ryan M.. Kelly
University of Melbourne, Melbourne, VIC, Australia
Saumya Pareek
University of Melbourne, Melbourne, Victoria, Australia
Qiushi Zhou
University of Melbourne, Melbourne, Victoria, Australia
Eduardo Velloso
University of Melbourne, Melbourne, Victoria, Australia
論文URL

https://doi.org/10.1145/3613904.3642919

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Generative AI for Design

316C
5 件の発表
2024-05-15 20:00:00
2024-05-15 21:20:00