Exploring the potential of AI-assisted self-representations in identity-focused art therapy

Aim: Generative text-to-image technologies offer new opportunities for individuals to visually articulate internal experiences. While traditional artistic self-portraiture has been extensively associated with self-insight, its AI-assisted equivalent remains underexplored. This study investigates the experiential and assessment potential of AI-generated self-representations and assesses their applicability within contemporary digital mental health frameworks. Methods: Five participants (aged 18–58) engaged in a 45-minute image generation session using Midjourney, producing approximately 500 images. This was followed by semi-structured interviews analyzed via interpretative phenomenological analysis (IPA). This study is idiographic and exploratory, drawing on the principles of IPA. Our aim is an in-depth explication of lived experience and shared experiential layers across cases. The findings should therefore be read as hypothesis-generating and as groundwork for future, larger-scale and mixed-methods evaluations and potential telepsychology integrations. Results: Three group experiential themes: (1) images as functional tools (e.g., as sources of comfort or aspirational vision boards); (2) self-reflective space (facilitating spontaneous self-disclosure and novel insight); and (3) modalities of self-definition (symbolic representation and narrative arc). Participants described the process as highly engaging and reported enhanced self-efficacy. Conclusions: AI-assisted image generation presents a flexible and user-centered modality for psychological reflection, with potential to augment art- and narrative-based therapeutic interventions. Ethical measures (e.g., anonymized data handling, withdrawal options) proved viable. Further research should explore larger, diverse samples and examine integration within telepsychology platforms to assess clinical utility.