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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.
Joseph et al. (Mon,) studied this question.