Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training

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.

@article{lee2025_2506.00386,
  title={ Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training },
  author={ Keyeun Lee and Seolhee Lee and Esther Hehsun Kim and Yena Ko and Jinsu Eun and Dahee Kim and Hyewon Cho and Haiyi Zhu and Robert E. Kraut and Eunyoung Suh and Eun-mee Kim and Hajin Lim },
  journal={arXiv preprint arXiv:2506.00386},
  year={ 2025 }
}