The need for mental health care continues to outpace the supply of trained psychotherapists, while psychotherapy training remains constrained by limited supervision time and scarce opportunities for repeated, feedback-rich practice in realistic scenarios. Simulation-based training can mitigate these constraints, but actor-based standardized patients are costly and difficult to scale, and many clinically challenging moments are ethically and logistically hard to practice with real clients. Large language models (LLMs) make interactive virtual patients increasingly feasible as a complement to conventional training; however, early systems are often prompt-driven rather than empirically grounded, exhibit limited psychologically meaningful state and longitudinal change, and predominantly focus on one-on-one sessions—leaving multi-party modalities such as couples therapy under-supported.

This thesis advances an empirical and design-oriented framework for building more realistic and pedagogically effective virtual patients grounded in psychotherapy process theory and real clinical data. Chapter 2 develops scalable, LLM-based measurement of therapist behaviors and client responses from large psychotherapy transcript corpora and uses structural equation modeling to estimate process-level dynamics linking therapist micro-skills and relational factors to subsequent client disclosure and emotional expression. Chapter 3 translates these empirically informed requirements into a multimodal, multi-agent couples therapy simulator that represents stage-structured sessions and recurrent interaction cycles such as demand–withdraw, enabling trainees to practice timing- and wording-sensitive interventions in high-conflict moments; an evaluation with licensed therapists examines realism and training relevance. Chapter 4 proposes an adaptive virtual patient architecture in which an LLM agent maintains latent psychological states that update in real time according to SEM-derived interpersonal dynamics conditioned on detected therapist behaviors, together with a plan for evaluating psychological fidelity.

By integrating theory-grounded measurement, empirical causal modeling, and interactive system design, this work lays a pathway for scalable psychotherapy training tools that make the consequences of therapist choices visible, support deliberate practice, and responsibly expand access to high-quality skills development.