arXiv subjects

Siwei Jin

Publications and source records attributed to Siwei Jin.

3 recordsLinked to original sources

arXiv2026

The Empirically Grounded Adaptive Virtual Patient for Psychotherapy Training: Disclosure That Responds to Therapist Micro-Skills

Simulated patients offer a scalable way to train psychotherapy micro-skills such as empathic responding and exploratory probing, but current systems either follow fixed scripts or rely on LLMs that drift unpredictably over long sessions. We present the Adaptive Virtual Patient (AVP), which adapts its disclosure behavior -- from guarded, through moderate openness, to full disclosure -- in response to trainee skill. The AVP is grounded in a structural equation model fit to nearly 2{,}000 hours of real-world psychotherapy transcripts, which quantifies how therapist empathy and exploration shift a patient's openness over time. An LLM generates the AVP's utterances conditioned on a disclosure level that the dynamics module updates each turn. In an evaluation with 20 clinicians and trainees over 80 sessions (1{,}033 turns), the AVP's disclosure rises in response to therapist empathy and exploration, while a prompt-only baseline stays flat; ablations confirm that the empirically motivated parameterization outperforms alternatives, with exploration carrying most of the adaptive signal.

cs.CY↗

arXiv2026

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.

cs.CY↗

arXiv2026

Simulating Couple Conflict: Designing A Multi-Agent System for Therapy Training and Practice

Couples therapy requires managing complex, evolving emotional dynamics between partners, but traditional training methods for therapists, like role-play, lack realism, consistency, and control. We present a multi-modal simulation that models therapy as a controlled, multi-agent dynamical system with structured interaction stages. Therapists practice with a pair of client-agents who go through six evolving stages that respond to therapist actions. This simulation enables practice with demand-withdraw conflict patterns in a closed-loop environment. The simulation uses a sense-plan-act architecture: it detects the therapist's input, updates agents' interaction states based on psychotherapy theory and transcript analysis, and generates realistic verbal and emotional responses. In an experiment with 21 licensed U.S. therapists, participants more accurately identified state transitions and rated the system as more realistic and responsive than a prompt-based baseline, demonstrating the value of stateful, interpretable simulation for therapist training.

cs.CY↗