Picture This: AI-Assisted Image Generation as a Resource for Problem Construction in Creative Problem-Solving

Rafner, Janet; Zana, Blanka; Dalsgaard, Peter; Biskjær, Michael Mose; Sherson, Jacob

doi:10.1145/3591196.3596823

Cited by 4 publications

(2 citation statements)

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“…Lewis points out that it is challenging to distinguish between inspiration and copying when utilizing generative AI and reflects on concerns such as "transparency of attribution", "ethical considerations", and the clarity of the "creation process". Rafner et al [48] conducted an in-the-wild study to examine the effects of AI-assisted image generation on creative problemsolving tasks, aiming to investigate the effects of generative AI on problem identification and problem construction. They developed a human-AI co-creative technology that combines a GAN and stable diffusion model to support AI-assisted image generation.…”

Section: The Emergent Role Of Ai In Creativity Supportmentioning

confidence: 99%

“…They developed a human-AI co-creative technology that combines a GAN and stable diffusion model to support AI-assisted image generation. They found that this intervention enabled participants to facilitate idea expansion and prompt engineering, suggesting that AI can "aid users in generating new ideas and refining their initial problem representations" [48].…”

Section: The Emergent Role Of Ai In Creativity Supportmentioning

confidence: 99%

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The Effects of Generative AI on Design Fixation and Divergent Thinking

Wadinambiarachchi,

Kelly,

Pareek

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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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. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.

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Section: The Emergent Role Of Ai In Creativity Supportmentioning

confidence: 99%

Section: The Emergent Role Of Ai In Creativity Supportmentioning

confidence: 99%