Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training
🤖 AI Summary
Existing virtual patient (VP) systems for nursing communication training lack dynamic responsiveness to learners’ skill levels—particularly failing to adaptively escalate emotional intensity and collaborative engagement when learners respond ineffectively—compromising clinical authenticity and training safety. This study introduces the first large language model (LLM)-based adaptive VP system, innovatively integrating clinical scenario modeling, real-time communication skill assessment, and modular behavioral regulation to dynamically adjust the VP’s emotional state and interaction strategies. Experimental evaluation demonstrates high agreement between the system’s automated assessments and expert nurse reference transcripts (Pearson’s *r* > 0.92). Domain expert evaluations confirm significantly superior interaction naturalness and clinical authenticity compared to mainstream VP systems (*p* < 0.01). The system is scalable, safe, and empirically validated for effective nursing communication training.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing nurse communication training with adaptive virtual patients
Overcoming cost and inflexibility of standardized patient simulations
Dynamically adjusting VP behavior based on trainee input
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-based dynamic adaptation to trainee input
Modular real-time communication assessment system
Clinically grounded flexible scenario construction
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