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Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality?

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Title: Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality?
Authors: Orwig, William (AUTHOR), Bellaiche, Lucas (AUTHOR), Spooner, Sarah (AUTHOR), Vo, Anh (AUTHOR), Baig, Zia (AUTHOR), Ragnhildstveit, Anya (AUTHOR), Schacter, Daniel L. (AUTHOR), Barr, Nathaniel (AUTHOR), Seli, Paul (AUTHOR)
Source: Creativity Research Journal. Jan-Mar2026, Vol. 38 Issue 1, p242-253. 12p.
Subjects: Creative ability, Divergent thinking, Aesthetics, Individual differences, Art, Computer art, Artificial intelligence
Abstract: As artificial intelligence (AI) advances in the realm of generative art, a critical question emerges: does human creativity matter? That is, do more-creative people produce more-creative AI-assisted artwork? To explore this, we conducted an online, pre-registered study in which we measured individual differences in creativity through two divergent-thinking tasks: The Alternate Uses Task and the Divergent Associations Task. Separately, participants produced creative wordsets for a hypothetical AI-art generator, which we then input into DALL-E to generate images. A group of trained raters independently assessed these images for creativity. Results revealed that both DAT performance and semantic diversity of the wordsets positively associated with the creativity of the AI-assisted images, suggesting that individuals with stronger divergent-thinking skills, and those who generated more-creative wordsets, tended to inspire more-creative AI-assisted artwork. Mediation analyses supported this conclusion by demonstrating a significant pathway between individual creative ability and AI-art creativity, mediated by semantic diversity. However, while our models yielded significant results, the effect sizes were modest, suggesting that the relationship between individual creative ability and AI-assisted creative outputs is relatively small. Taken together, these results suggest that while individual creativity appears to contribute to the quality of AI-assisted artwork, its influence may be relatively limited. [ABSTRACT FROM AUTHOR]
Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality?
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  Data: <searchLink fieldCode="JN" term="%22Creativity+Research+Journal%22">Creativity Research Journal</searchLink>. Jan-Mar2026, Vol. 38 Issue 1, p242-253. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Creative+ability%22">Creative ability</searchLink><br /><searchLink fieldCode="DE" term="%22Divergent+thinking%22">Divergent thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Aesthetics%22">Aesthetics</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+differences%22">Individual differences</searchLink><br /><searchLink fieldCode="DE" term="%22Art%22">Art</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+art%22">Computer art</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
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  Data: As artificial intelligence (AI) advances in the realm of generative art, a critical question emerges: does human creativity matter? That is, do more-creative people produce more-creative AI-assisted artwork? To explore this, we conducted an online, pre-registered study in which we measured individual differences in creativity through two divergent-thinking tasks: The Alternate Uses Task and the Divergent Associations Task. Separately, participants produced creative wordsets for a hypothetical AI-art generator, which we then input into DALL-E to generate images. A group of trained raters independently assessed these images for creativity. Results revealed that both DAT performance and semantic diversity of the wordsets positively associated with the creativity of the AI-assisted images, suggesting that individuals with stronger divergent-thinking skills, and those who generated more-creative wordsets, tended to inspire more-creative AI-assisted artwork. Mediation analyses supported this conclusion by demonstrating a significant pathway between individual creative ability and AI-art creativity, mediated by semantic diversity. However, while our models yielded significant results, the effect sizes were modest, suggesting that the relationship between individual creative ability and AI-assisted creative outputs is relatively small. Taken together, these results suggest that while individual creativity appears to contribute to the quality of AI-assisted artwork, its influence may be relatively limited. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Creativity Research Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10400419.2024.2440691
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      – SubjectFull: Art
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      – SubjectFull: Computer art
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      – SubjectFull: Artificial intelligence
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              Text: Jan-Mar2026
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              Y: 2026
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