The effects of exposure to imagery created with generative artificial intelligence (gen-AI) on young women's body image: Do image type and disclosure matter?

It has been predicted that soon a majority of online content will be created with generative artificial intelligence, or gen-AI . Yet, there is currently limited knowledge about how exposure to content created by gen-AI may impact users’ body image. This study investigated the effects of exposure to gen-AI imagery depicting (i) the fit ideal; (ii) functionality and body diversity; or (iii) neutral, non-body-related objects, on young women’s body image, and whether these effects would differ when participants were informed that the imagery was created by gen-AI. Participants were 291 women between 18 and 31 years old ( M age = 25.67, SD = 3.04) from the United States, recruited through Prolific. Controlling for pretest levels of the respective outcome, the analyses showed that the fit ideal condition reported lower posttest state functionality appreciation, body appreciation, and appearance satisfaction compared to the functionality diversity condition. Differences between these two conditions and the control condition were nonsignificant, except for significantly lower posttest state body appreciation in the fit ideal condition. The analyses also revealed that participants who received a disclosure— regardless of what type of imagery they saw—reported higher posttest state functionality appreciation; however, there were no differences in body appreciation and appearance satisfaction. Moderation analyses for trait appearance comparisons and appearance-ideal internalization showed that fit ideal and functionality diversity imagery were especially impactful for more ‘vulnerable’ individuals—generally, those with higher levels of these moderators. These results suggest that exposure to functionality diversity gen-AI imagery can improve body image, particularly for those with high levels of trait appearance comparisons and internalization.