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Posted June 29, 2026 Reviewed by Michelle Quirk
Generative artificial intelligence, or “gen-AI,” uses machine learning, neural networks, and other techniques to generate new content—like images, videos, and text—by analyzing patterns and information from massive training datasets. ChatGPT, Gemini, and MidJourney are just a few examples of popular services that use gen-AI.
It has been predicted that soon a majority of online content will be created by gen-AI. The speed and advancement of gen-AI can be impressive and present new opportunities. However, gen-AI also raises ethical issues that must be considered.
One of these ethical issues pertains to body image—how people think and feel about their body. While a lot is known about how “traditional” media imagery (using real humans) affects people’s body image, how imagery created by gen-AI affects body image is currently unclear.
To that end, my colleagues and I conducted an experiment to test the effects of two types of gen-AI imagery—idealized vs. diverse—on body image. Further, we investigated whether disclosure impacts these effects, and what appearance-based individual characteristics might determine who is more affected than others.
Participants were 291 women aged between 18 and 31 years. They completed questionnaires to measure their current body image (i.e., how appreciative and satisfied they felt with their bodies). Afterward, the participants completed the “gen-AI exposure.” Namely, all participants viewed 20 images that we had created using gen-AI, but the type of images they saw differed according to what group they had been randomly assigned to:
In addition, half of the participants in each condition were informed that the images had been created by gen-AI, and the other half of the participants did not receive this message.
After the gen-AI exposure, all participants completed the same questionnaires about their current body image. They also completed questionnaires about their tendencies to compare their appearance to others’ and the extent to which they bought into societal body ideals (“internalization”). Individuals with higher appearance comparisons and internalization tend to be more vulnerable to the effects of media imagery; we wanted to test whether the same was the case when it comes to gen-AI imagery.
Overall, we found that:
To the best of our knowledge, this was the first experiment to test the effects of gen-AI imagery—idealized vs. diverse—on women’s body image, and whether effects differed when participants received a gen-AI disclosure.
Our findings suggest that viewing gen-AI imagery that depicts idealized bodies leads to poorer body image, whereas viewing gen-AI imagery that depicts more diverse bodies leads to improvements in body image. Thus, when it comes to gen-AI imagery of people, it is better to depict diversity compared to idealized bodies.
This is also the first experiment to create imagery that depicts diversity in terms of body functionality, beyond physical activities. In line with the broader research on positive body image, our findings show that promoting a holistic and appreciative focus on body functionality is beneficial for body image.
Beyond these general effects, we found that more “vulnerable” participants were more affected by the gen-AI imagery: those with higher appearance comparison tendencies and higher internalization of beauty ideals. These findings may help to direct future intervention efforts to people who may need them the most.
Disclosure seemed to be beneficial for some aspects of body image that we assessed, but not for all. For example, even when participants who viewed the idealized gen-AI images received a disclosure, they still felt less satisfied with their bodies. It could be that they compared their own bodies with the idealized bodies in the gen-AI imagery, even though they knew they were “fake.” Appearance comparisons often happen automatically, outside of conscious awareness or control.
Last, our findings also suggest that disclosure can be especially beneficial for more vulnerable individuals—similar to the findings for image type. Thus, it may be worthwhile to explore the use of disclosure messages on gen-AI Imagery in the future.
As this is the first study to investigate the effects of idealized and diverse gen-AI imagery on body image, there are many questions that remain to be investigated. As gen-AI continues to rapidly develop and become more integrated into people’s daily lives, the urgency for this research will continue to rise. We hope that this experiment provides inspiration for future research in this area.
References
Alleva, J. M., Türkcan, T., Lin, L., Sloutas, C. L., & Fardouly, J. (2026). 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? Computers in Human Behavior: Artificial Humans, 100339.
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Jessica Alleva, Ph.D., is an assistant professor of psychology at Maastricht University in the Netherlands, and a Visiting Fellow at the Centre for Appearance Research in the U.K.
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The best way to begin something new—in love, work, and life.
Self Tests are all about you. Are you outgoing or introverted? Are you a narcissist? Does perfectionism hold you back? Find out the answers to these questions and more with Psychology Today.
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