@inproceedings{lee-etal-2025-adaptive,
title = "Adaptive-{VP}: A Framework for {LLM}-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training",
author = "Lee, Keyeun and
Lee, Seolhee and
Kim, Esther Hehsun and
Ko, Yena and
Eun, Jinsu and
Kim, Dahee and
Cho, Hyewon and
Zhu, Haiyi and
Kraut, Robert E. and
Suh, Eunyoung E. and
Kim, Eun-mee and
Lim, Hajin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.118/",
doi = "10.18653/v1/2025.findings-acl.118",
pages = "2319--2352",
ISBN = "979-8-89176-256-5",
abstract = "Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide valuable experiential learning, they are often costly and inflexible. Virtual patient (VP) systems offer a scalable alternative, but most fail to adapt to the varying communication skills of trainees. In particular, when trainees respond ineffectively, VPs should escalate in hostility or become uncooperative{---}yet this level of adaptive interaction remains largely unsupported. To address this gap, we introduce Adaptive-VP, a VP dialogue generation framework that leverages large language models (LLMs) to dynamically adapt VP behavior based on trainee input. The framework features a pipeline for constructing clinically grounded yet flexible VP scenarios and a modular system for assessing trainee communication and adjusting VP responses in real time, while ensuring learner safety. We validated Adaptive-VP by simulating challenging patient conversations. Automated evaluation using a corpus from practicing nurses showed that our communication skill evaluation mechanism reflected real-world proficiency levels. Expert nurses further confirmed that Adaptive-VP produced more natural and realistic interactions than existing approaches, demonstrating its potential as a scalable and effective tool for nursing communication training."
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<abstract>Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide valuable experiential learning, they are often costly and inflexible. Virtual patient (VP) systems offer a scalable alternative, but most fail to adapt to the varying communication skills of trainees. In particular, when trainees respond ineffectively, VPs should escalate in hostility or become uncooperative—yet this level of adaptive interaction remains largely unsupported. To address this gap, we introduce Adaptive-VP, a VP dialogue generation framework that leverages large language models (LLMs) to dynamically adapt VP behavior based on trainee input. The framework features a pipeline for constructing clinically grounded yet flexible VP scenarios and a modular system for assessing trainee communication and adjusting VP responses in real time, while ensuring learner safety. We validated Adaptive-VP by simulating challenging patient conversations. Automated evaluation using a corpus from practicing nurses showed that our communication skill evaluation mechanism reflected real-world proficiency levels. Expert nurses further confirmed that Adaptive-VP produced more natural and realistic interactions than existing approaches, demonstrating its potential as a scalable and effective tool for nursing communication training.</abstract>
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%0 Conference Proceedings
%T Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training
%A Lee, Keyeun
%A Lee, Seolhee
%A Kim, Esther Hehsun
%A Ko, Yena
%A Eun, Jinsu
%A Kim, Dahee
%A Cho, Hyewon
%A Zhu, Haiyi
%A Kraut, Robert E.
%A Suh, Eunyoung E.
%A Kim, Eun-mee
%A Lim, Hajin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F lee-etal-2025-adaptive
%X Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide valuable experiential learning, they are often costly and inflexible. Virtual patient (VP) systems offer a scalable alternative, but most fail to adapt to the varying communication skills of trainees. In particular, when trainees respond ineffectively, VPs should escalate in hostility or become uncooperative—yet this level of adaptive interaction remains largely unsupported. To address this gap, we introduce Adaptive-VP, a VP dialogue generation framework that leverages large language models (LLMs) to dynamically adapt VP behavior based on trainee input. The framework features a pipeline for constructing clinically grounded yet flexible VP scenarios and a modular system for assessing trainee communication and adjusting VP responses in real time, while ensuring learner safety. We validated Adaptive-VP by simulating challenging patient conversations. Automated evaluation using a corpus from practicing nurses showed that our communication skill evaluation mechanism reflected real-world proficiency levels. Expert nurses further confirmed that Adaptive-VP produced more natural and realistic interactions than existing approaches, demonstrating its potential as a scalable and effective tool for nursing communication training.
%R 10.18653/v1/2025.findings-acl.118
%U https://aclanthology.org/2025.findings-acl.118/
%U https://doi.org/10.18653/v1/2025.findings-acl.118
%P 2319-2352
Markdown (Informal)
[Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training](https://aclanthology.org/2025.findings-acl.118/) (Lee et al., Findings 2025)
ACL
- Keyeun Lee, Seolhee Lee, Esther Hehsun Kim, Yena Ko, Jinsu Eun, Dahee Kim, Hyewon Cho, Haiyi Zhu, Robert E. Kraut, Eunyoung E. Suh, Eun-mee Kim, and Hajin Lim. 2025. Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training. In Findings of the Association for Computational Linguistics: ACL 2025, pages 2319–2352, Vienna, Austria. Association for Computational Linguistics.