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Updated: Jul 17, 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

Simulator development using natural language: clinician-led innovation through artificial intelligence.

Federico Lorenzo Barra^1,2, Serena Ricci^3,4, Edoardo Moro⁵

¹Department of Anesthesiology and Intensive Care Medicine, Azienda Ospedaliero-Universitaria "Maggiore della Carità", Novara, Italy.

Advances in Simulation (London, England)

|July 16, 2026

Summary

Natural Language-Driven Development (NLDD) empowers clinicians to create custom healthcare simulation tools without coding. This approach successfully developed the Open Vent Sim, addressing educational resource gaps.

Keywords:

Artificial Intelligence Educational Technology Healthcare Innovation Mechanical Ventilation Medical Education Natural Language Programming Open Source Software Simulation-Based Learning

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Last Updated: Jul 17, 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:

Medical Education Technology
Artificial Intelligence in Healthcare
Simulation-Based Training

Background:

The
clinician-developer gap
hinders custom digital healthcare simulators due to educators' lack of programming skills.
Natural Language-Driven Development (NLDD) offers a solution by enabling technology creation via conversational AI.

Purpose of the Study:

To implement NLDD for developing Open Vent Sim, a mechanical ventilation simulator for educational use.
To overcome resource limitations in healthcare simulation training.
To assess the feasibility of NLDD in creating specialized educational technology.

Main Methods:

NLDD methodology was employed using Google AI Studio for iterative development.
A multidisciplinary team translated clinical requirements into code via natural language prompts and large language models.
Conversational development cycles facilitated the creation of a web-based application.

Main Results:

Open Vent Sim was developed in approximately 40 hours over two weeks, featuring diverse simulation environments and patient profiles.
The simulator includes dynamic compliance calculations and realistic waveform generation, validated by resident feedback.
The application was successfully implemented in educational settings for skill training and team-based scenarios.

Conclusions:

NLDD democratizes educational technology creation, enabling clinical experts to build sophisticated simulators without traditional programming.
This approach addresses resource constraints while maintaining clinical authenticity in simulation training.
Professional technical oversight is crucial for transitioning NLDD-generated prototypes to production-ready applications.