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This essay explores the pedagogical use of images generated by artificial intelligence (AI) to investigate the politics of facial representation. Through a media archaeological and visual culture lens, the essay outlines a classroom assignment, Facial Subjects , in which students generate photographic portraits using generative AI to critically examine how identity categories such as race, gender and class are constructed, mediated and encoded by machine learning (ML) systems. By comparing AI-generated portraits to archival and real-word photographic portraits, students engage in visual critique while developing media literacy in the analysis of images. The essay argues that teaching with AI-generated images provides new resources for critical media pedagogy and that comparing those images with archival and real-world portraits equips students with conceptual tools for thinking about questions of aesthetics, normativity, and bias central to film and media studies today. Through this exercise, teaching with AI becomes a method of inquiring into the ideological underpinnings of contemporary image systems while fostering experimental and critical approaches to media pedagogy.
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Lisa Åkervall (2026) studied this question.
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