Psychological Therapy in the Age of Large Language Models: Framework for Therapist-Delivered and AI-Supported Functions.

Large language models are increasingly used within and alongside therapy. As large language models perform more therapy functions, questions arise about what the future might hold for therapists and what they will do. We argue that the enduring therapist role in the age of AI-assisted care is currently best understood through relational, adaptive, and accountability functions. These functions include therapeutic challenge, use of the therapeutic relationship as a mechanism of change, rupture detection and repair, bearing witness to suffering, calibration of pace and treatment burden, and clinical judgment under uncertainty across the broader care pathway. Drawing on psychotherapy theory, digital mental health research, the declarative-procedural-reflective model by Bennett-Levy, and our clinical experience, we propose a clinically informed, hypothesis-generating, relational-adaptive-accountability framework. This framework is intended to support further empirical testing and may have implications for workforce development, supervision, training, and service design.