AI-Human synergy in single-session therapy: augmenting capacity for scalable mental health support

Amid a global mental health crisis, this article explores integrating Single-Session Therapy (SST) principles with a generative Artificial Intelligence (AI) stack to enhance mental health workforce capacity. The article examines current AI applications in mental health support, analyzing reported benefits — such as enhanced accessibility, personalized support, and efficiency — alongside key limitations, including ethical dilemmas, data privacy concerns, and the risk of dehumanizing care. Building on this analysis, the article proposes a theoretically grounded, ethically constrained, AI-augmented framework for training, supervision, and real-time support. This framework augments, rather than replaces, professionally trained human providers or peer/lay counsellors in delivering effective SST sessions. The article introduces the conceptual and technical foundations of an AI-augmented SST application and outlines a modular technical architecture that includes context-specific training, simulated clients for practice, AI-supported feedback and supervision, and real-time co-therapist assistance. Finally, it outlines a staged, mixed-methods research protocol involving supervisors and trainees, to experimentally compare this framework with traditional training. Such an evidence-based approach carries substantial practical implications for digital health adoption, governance, and policymaking, demonstrating how targeted AI integration can transform global mental healthcare delivery.