@inproceedings{chen-etal-2026-empirically,
title = "Empirically Grounded Behavioral Adaptivity for {LLM}-Based Virtual Patients in Psychotherapy Training",
author = "Chen, Angela and
Jin, Siwei and
Bao, Catherine and
Wang, Canwen and
Wang, Yi-Chia and
Kraut, Robert E. and
Wu, Tongshuang and
Zhu, Haiyi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1349/",
pages = "28644--28665",
ISBN = "979-8-89176-462-0",
abstract = "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 in response to trainee skill, from guarded, through moderate openness, to full disclosure. The AVP is informed by a structural equation model fit to nearly 2000 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."
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<abstract>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 in response to trainee skill, from guarded, through moderate openness, to full disclosure. The AVP is informed by a structural equation model fit to nearly 2000 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.</abstract>
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%0 Conference Proceedings
%T Empirically Grounded Behavioral Adaptivity for LLM-Based Virtual Patients in Psychotherapy Training
%A Chen, Angela
%A Jin, Siwei
%A Bao, Catherine
%A Wang, Canwen
%A Wang, Yi-Chia
%A Kraut, Robert E.
%A Wu, Tongshuang
%A Zhu, Haiyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-empirically
%X 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 in response to trainee skill, from guarded, through moderate openness, to full disclosure. The AVP is informed by a structural equation model fit to nearly 2000 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.
%U https://aclanthology.org/2026.findings-emnlp.1349/
%P 28644-28665
Markdown (Informal)
[Empirically Grounded Behavioral Adaptivity for LLM-Based Virtual Patients in Psychotherapy Training](https://aclanthology.org/2026.findings-emnlp.1349/) (Chen et al., Findings 2026)
ACL
- Angela Chen, Siwei Jin, Catherine Bao, Canwen Wang, Yi-Chia Wang, Robert E. Kraut, Tongshuang Wu, and Haiyi Zhu. 2026. Empirically Grounded Behavioral Adaptivity for LLM-Based Virtual Patients in Psychotherapy Training. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28644–28665, Budapest, Hungary. Association for Computational Linguistics.