Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training
Journal:
arXiv
Published Date:
May 31, 2025
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.
