Paper Type
Short
Paper Number
1445
Description
We report preliminary findings from a study investigating the potential of AI-generated images to inspire people similarly to user-generated content, such as photographs shared on social media. We conducted an online experiment involving 171 participants who were randomly assigned to one of two groups. In one group, they indicated their level of being inspired by AI-generated images; in the other group, the level of being inspired by photographs. In each group, stimuli were embedded in the theme domains of dinner dishes, room design, and beauty and style. For specific domains, we found that people are more inspired by AI-generated images than by photographs. Based on these results, we propose a follow-up study employing a mixed-methods approach to delve deeper into the domain-specific variations of AI as an inspirational technology, contributing to the broader discourse on human-AI collaboration across Information Systems and related fields.
Recommended Citation
Wyszynski, Marc; Weber, Sebastian; and Niehaves, Björn, "Investigating the Inspirational Power of Generative AI" (2024). PACIS 2024 Proceedings. 20.
https://aisel.aisnet.org/pacis2024/track13_hcinteract/track13_hcinteract/20
Investigating the Inspirational Power of Generative AI
We report preliminary findings from a study investigating the potential of AI-generated images to inspire people similarly to user-generated content, such as photographs shared on social media. We conducted an online experiment involving 171 participants who were randomly assigned to one of two groups. In one group, they indicated their level of being inspired by AI-generated images; in the other group, the level of being inspired by photographs. In each group, stimuli were embedded in the theme domains of dinner dishes, room design, and beauty and style. For specific domains, we found that people are more inspired by AI-generated images than by photographs. Based on these results, we propose a follow-up study employing a mixed-methods approach to delve deeper into the domain-specific variations of AI as an inspirational technology, contributing to the broader discourse on human-AI collaboration across Information Systems and related fields.
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