Making the Making Visible: How Process Evidence and Individual Differences Affect People's Creativity Judgments of Text-to-Image Generative AI

Generative AI tools for image creation are now mainstream, yet we know little about when and why observers judge them as “creative”. Previous human-robot interaction research suggests that revealing the creation process can raise perceived machine creativity and points that observer differences may moderate this effect. We take these observations from physical robots to the bigger domain of virtual text-to-image diffusion systems by manipulating perceptual evidence (PE), i.e., interface-visible cues about the generation process. We report two preregistered online experiments looking into PE and observer individual differences. Study 1 (N=298) used a within-subjects manipulation comparing Product (final image only) to Product+Process (adding a short animation of the denoising process). Study 2 (N=295) added a between-subjects tutorial (diffusion vs. control) in a 2 × 2 mixed design. The tutorial briefly explained how diffusion models generate images, intended to raise system-specific literacy. Contrary to previous work, confirmatory analyses found no average effect of showing Process on creativity, and no tutorial effect. Exploratory analyses revealed that general AI literacy moderated the PE contrast, i.e., at lower literacy, observing process tended to lower creativity ratings; at higher literacy, it tended to raise them. Moreover, attitudes toward AI and art interest were positively associated with creativity ratings. Thematic analysis of open-ended responses indicated potential reasons for the lack of overall PE effect. Taken together, these converging quantitative and qualitative findings indicate that individual differences systematically shape creativity judgments of text-to-image GenAI and, in our setting, exert stronger and more reliable influence than PE alone. For design, this implies that process visualizations could help some audiences more than others. Interfaces that adapt to literacy and attitudes, or that pair process views with contextual explanation calibrated to user background, could be more likely to shift judgments than one-size-fits-all depictions of generation.