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Artificial intelligence has transformed the landscape of sexual harm, particularly towards women and girls, by enabling the rapid creation of synthetic hypersexualised material, including peer-generated modification images and deepfakes, falling under the category of child sexual abuse material (AI-CSAM). While research has focused primarily on adult offenders, far less attention has been given to the growing involvement of children and young people in producing AI-generated sexual imagery of peers. This review synthesises behavioural, criminological, feminist and sociotechnical literature to develop a theoretical framework explaining peer-on-peer AI-CSAM as a form of technology-facilitated gender-based violence (TFGBV), drawing on moral disengagement theory, social learning theory, information gap theory and feminist analyses of image-based abuse. The review situates AI-CSAM within a multi-level safeguarding ecology spanning the family, the school and the national curriculum. It argues that weaknesses at any one level undermine the protective capacity of the wider safeguarding system and advances an Ecological Safeguarding Model to explain how coordinated intervention across these domains can strengthen prevention. By identifying gaps in current safeguarding practice and highlighting implications for policy, education and future research, this article provides a conceptual foundation for trauma-informed, gender-aware and developmentally appropriate responses to AI-facilitated sexual harm among children and young people.
Danielle Hotz (Sun,) studied this question.