Adaptive Recommendation Algorithms for Generative Art Therapy Content in Adolescent Trauma Interventions

Adolescents exposed to family dysfunction, violence, and school bullying often show trauma-related symptoms and disengagement from class, and current digital interventions rely on static templates that do not handle trauma safety and individual differences well. This study designs an adaptive recommendation framework for generative art therapy that integrates content generation and tagging, a constrained contextual bandit policy, and behavioral and scale-based evaluation, and manages image, music, and narrative micro-tasks with structured labels of goal type, difficulty, and arousal. A school-based trial compares the adaptive policy with a static rule-based sequence. Results indicate higher task completion, deeper sessions, and higher next-day login in the adaptive group, together with larger improvements in emotion-regulation difficulty, self-efficacy, and trauma symptom scores and no increase in the proportion of “trigger/strong discomfort” sessions. The findings suggest that trauma-informed online learning policies can enhance engagement and psychological benefit in adolescent digital art therapy without an observable rise in safety incidents.