Empirical Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments
Psychotherapy is a primary treatment for many mental health conditions, yet the interplay among therapist behaviors, client responses, and the therapeutic relationship is difficult to study at scale, as process research has relied on labor-intensive human coding. We develop and validate a computational framework for modeling therapist-client interaction, using large language models (LLMs) to measure therapist behaviors (empathy, exploration), relational quality (rapport), and client outcomes (self-disclosure, self-directed and outward-directed negative emotion). After validating model-generated scores against human annotations (ICC = 0.45-0.81; rapport 0.81, self-disclosure 0.78), we apply these measures to roughly 2,000 hours of transcripts from the Alexander Street corpus and use Structural Equation Modeling to estimate moment-to-moment relationships among therapist behaviors, rapport, and subsequent client responses, controlling for prior client state and context. Therapist empathy and exploration directly predict increased client disclosure and shifts in emotional expression; empathy is more strongly associated with self-directed than outward-directed negative emotion, suggesting greater acknowledgment of internal distress, while exploration increases disclosure and emotional elaboration. Rapport does not directly amplify disclosure or emotional intensity but instead moderates the associations between therapist behaviors and client affect, potentially contributing to reductions in internal distress. These results show that LLM-based measurement combined with structural modeling can capture core therapeutic processes at scale, with empathy and exploration acting directly and rapport as a contextual moderator, providing a foundation for precision modeling of psychotherapy and for scalable therapist training and AI-supported clinical education.