[2511.16814] Stable diffusion models reveal a persisting human and AI gap in visual creativity

Computer Science > Artificial Intelligence arXiv:2511.16814 (cs) [Submitted on 20 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Stable diffusion models reveal a persisting human and AI gap in visual creativity Authors:Silvia Rondini, Claudia Alvarez-Martin, Paula Angermair-Barkai, Olivier Penacchio, M. Paz, Matthew Pelowski, Dan Dediu, Antoni Rodriguez-Fornells, Xim Cerda-Company View a PDF of the paper titled Stable diffusion models reveal a persisting human and AI gap in visual creativity, by Silvia Rondini and 8 other authors View PDF Abstract:While recent research suggests Large Language Models match human creative performance in divergent thinking tasks, visual creativity remains underexplored. This study compared image generation in human participants (Visual Artists and Non Artists) and using an image generation AI model (two prompting conditions with varying human input: high for Human Inspired, low for Self Guided). Human raters (N=255) and GPT4o evaluated the creativity of the resulting images. We found a clear creativity gradient, with Visual Artists being the most creative, followed by Non Artists, then Human Inspired generative AI, and finally Self Guided generative AI. Increased human guidance strongly improved GenAI's creative output, bringing its productions close to those of Non Artists. Notably, human and AI raters also showed vastly different creativity judgment patterns. These results suggest that, in contrast to language centered ta...