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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

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Artificial Intelligence-Powered Simulation for Telehealth Communication Training: Bridging Traditional Simulation and

Farrukh Nadeem Jafri¹, Michelle Elsener, Anshul Kumar

¹WPH Cares Transitional Care and Population Health, White Plains Hospital, Montefiore Health, White Plains, New York, NY (F.N.J., M.E.); Administration Department, Massachusetts General Hospital Institute of Health Professions, Boston, MA (A.K., S.K.-E., R.A.E.).

Simulation in Healthcare : Journal of the Society for Simulation in Healthcare

|April 30, 2026

Summary

This study introduces a 5-phase blueprint for integrating artificial intelligence (AI) simulation into healthcare quality improvement (QI) to enhance communication skills training. AI-driven microdebriefing with rubric feedback proved effective for scalable, cost-efficient learning.

Keywords:

artificial intelligence interprofessional communication patient simulation quality improvement transitional care

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Last Updated: May 2, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Healthcare Education Technology
Artificial Intelligence in Medicine
Simulation-Based Training

Background:

Traditional communication training is resource-intensive and difficult to scale.
Artificial intelligence (AI) and large language models offer potential solutions for healthcare education.
A methodological blueprint for integrating AI simulation into quality improvement (QI) frameworks is lacking.

Purpose of the Study:

To present a 5-phase methodological blueprint for implementing AI-powered simulation training.
To provide evaluatory evidence for enhancing transitional care communication skills using AI.
To integrate AI simulation within operational QI frameworks.

Main Methods:

A 5-phase framework was developed, integrating AI simulation with QI and educational principles.
Phases included content validation, simulation development (cognitive load theory), platform selection, structured implementation, and outcome measurement (statistical process control).
The framework was grounded in the Donabedian model.

Main Results:

AI systems struggled with traditional debriefing methods (advocacy-inquiry, plus-delta).
Microdebriefing with rubric-focused feedback was identified as optimal for AI-mediated learning.
AI demonstrated strengths in consistent, structured feedback delivery within its limitations.

Conclusions:

The 5-phase blueprint offers a scalable, cost-effective approach for AI-powered communication training in healthcare.
Embedding AI simulation within QI infrastructure enhances communication skills systematically and maintains educational rigor.
AI serves as a scalable alternative to traditional training for foundational communication skills, benefiting simulation science and telehealth education.