Generative AI : A Problematic Illustration of the Intersections of Race, Gender and Class

ABSTRACT This commentary investigates how generative AI tools such as DALL‐E can create imagery which (re)produce racist, gendered and classist representations of peoples. Drawing on prompts entered across three time periods into DALL‐E, I employ algorithmic coloniality as a conceptual framework, together with critical visual analysis, critical race semiotics and intersectionality to examine the images created. This analysis shows that, despite advances in photorealism, DALL‐E persistently reproduces the same racialised and gendered tropes in its depictions of Black American women. I argue that these images are not merely aesthetic by‐products, but socio‐technical artefacts shaped by historically racist training data. Moreover, I suggest that enhanced photorealism may amplify, rather than mitigate, such stereotypes. Building on these findings, I argue that geography educators need to cultivate forms of critical AI literacy that extend beyond refining prompts to interrogate the algorithmic coloniality embedded within these systems. I conclude by proposing practical and collective strategies to support geography educators engaging with these tools.