Generative AI mental health chatbots: a scoping review of intervention design and user experience

Lotenna Olisaeloka, Chris G. Richardson, Angel Y Wang, Richard J. Munthali, Daniel V. Vigo

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 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 published between 2023 and 2025 were included. Most interventions were early-stage, cognitive behavioral 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, personalization, 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 such as domain knowledge grounding, adaptive tailoring, multimodal interaction, and structured delivery formats emerged as important features in relation to reported UX outcomes. Future work should prioritize efficacy trials, and incorporate standardized UX and safety evaluation. Researchers should also adopt inclusive co-design approaches that enable ethical, human-centered interventions.