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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Evaluating real-time voice AI-based virtual patients for authentic communication training.

Wei Hu1, Yi Zuo2, Qifeng Wan3

  • 1Pediatric Department, People's Hospital of Xiangxi Autonomous Prefecture, Ji Shou, China.

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|March 19, 2026
PubMed
Summary
This summary is machine-generated.

A new real-time voice virtual patient (RT-VP) using generative AI shows promise for clinical communication training. This AI-powered tool achieved communication performance comparable to standardized patients (SPs), offering a scalable solution for training.

Keywords:
Communication skills trainingGenerative artificial intelligencelarge language modelmedical educationvirtual patient

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Communication Skills Training

Background:

  • Effective clinical communication training is crucial but limited by resource-intensive standardized patients (SPs) and less interactive virtual patients (VPs).
  • Generative artificial intelligence (AI) and real-time voice models offer potential for more natural and synchronous dialogue in virtual simulations.

Purpose of the Study:

  • To assess if a real-time voice-based virtual patient (RT-VP) can match standardized patient (SP) training outcomes in communication performance, realism, and self-efficacy.
  • To address the global need for scalable and authentic communication training methods.

Main Methods:

  • A randomized controlled study involving 134 residents compared RT-VP, SP, and peer role-play (PR) training modalities.
  • All participants received identical SPIKES-based communication instruction.
  • Outcomes measured included communication performance (SPIKES scores), self-efficacy, and perceived realism.

Main Results:

  • SP training yielded the highest SPIKES scores, followed closely by RT-VP, with both significantly outperforming PR (p < .001).
  • The difference in communication performance between SP and RT-VP was not statistically significant (p = .06).
  • Perceived realism favored SPs over RT-VPs, though RT-VPs demonstrated comparable linguistic realism, while SPs excelled in contextual, emotional, and engagement realism.

Conclusions:

  • Real-time voice generative AI virtual patients (RT-VPs) offer a scalable and consistent approach to communication training, approximating SP-level performance and authenticity.
  • RT-VP simulation presents a viable method to enhance access to emotionally authentic communication training globally.