loading . . . Using Generative AI to Co-Design Digital Mental Health Interventions With Adolescents in Rural South Africa: Qualitative Thematic Analysis of Participatory Workshops Background: Digital mental health interventions (DMHIs) offer a scalable approach to address adolescent depression and anxiety. User-centred co-production can optimize acceptability and engagement, but it is often resource-intensive. Advances in generative artificial intelligence (AI) create new opportunities for involving adolescents in co-design, yet research on its feasibility and acceptability, particularly in low-resource settings, remain underexplored. Objective: This study explored adolescents’ experiences and perspectives of using generative AI to co-design stories, images, and music for the Kuamsha app, a gamified digital mental health intervention that teaches Behavioural Activation through interactive narratives and peer support. Methods: Two participatory workshops and focus group discussions were conducted with 23 adolescents (aged 15-19 years) in rural Mpumalanga, South Africa. Participants were guided to use of three generative AI tools—ChatGPT (text-to-story), MidJourney (text-to-image), and Soundful (music generation)—to create digital content. Data were audio-recorded, translated, transcribed and triangulated with facilitator’s observation notes. Thematic Analysis was used to explore key themes. Results: Almost all participants (22/23, 96%) had no prior exposure to generative AI. The majority (20/23, 87%) described the creative process as enjoyable and engaging, with most (21/23, 91%) reporting that creating music improved their mood. Adolescents expressed autonomy and ownership of the process, with over half (14/23, 61%) personalizing outputs to reflect their identities and aspirations. All participants (23/23, 100%) preferred AI-generated images over the cartoon-like illustrations of the Kuamsha app, and most (19/23, 83%) preferred AI-generated music. Story preferences were more mixed, with about a quarter of participants (6/23, 26%) recalling that Kuamsha’s narratives contained embedded lessons which were not integrated into the ChatGPT outputs. Most adolescents (18/23, 78%) required support with prompt construction, and more than half (13/23, 57%) noted cultural biases in AI outputs, particularly in images. Most participants (17/23, 74%) expressed interest in using AI for schoolwork and creative projects, while a minority (6/23, 26%) preferred to limit use to personal applications. Concerns about fairness and the displacement of human creativity were also raised. Conclusions: Generative AI shows promise for enhancing adolescent engagement in the co-production of DMHIs and enabling culturally relevant and personalized content. However, reliance on human support and persistent algorithmic biases remain limitations. Further research should explore integration of therapeutic principles into AI-generated media and strategies to mitigate bias. http://dlvr.it/TPflQl