Generative AI Mental Health Chatbots: A Scoping Review of Intervention Design and User Experience

Generative AI (GenAI) mental health chatbots offer scalable, on-demand support with the potential to address persistent gaps in mental healthcare access. Yet evidence on how these interventions are designed and how users experience them remains fragmented. To our knowledge, this scoping review represents the first systematic and integrated mapping of conversational agent features, intervention design characteristics and user experience (UX) outcomes in purpose-built GenAI mental health chatbot interventions. A systematic search of seven databases identified 1899 articles, from which 21 studies across 11 countries were included. Most interventions were early-stage, cognitive behavioural therapy-based, and delivered through non-embodied text chatbots. Target conditions included depression, anxiety, dementia, eating disorders, and post-traumatic stress disorder. UX outcomes indicated moderate-to-high usability, therapeutic alliance, and user satisfaction, driven by convenience, personalisation, and perceived empathy. However, engagement commonly declined over time, attributed to limited interactivity, and erosion of trust following inaccurate or contextually misaligned outputs. Design approaches including multi-modal interaction, domain knowledge grounding, structured delivery formats, and co-design processes emerged as important influences on UX outcomes. Future work should prioritise efficacy trials that incorporate standardised UX outcome measures and researchers should adopt co-design approaches with diverse end-users to enable equitable, human-centred interventions.