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Related Experiment Video

Updated: Jun 30, 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

Building CAR-E: A Novel Artificial Intelligence Agent for Coaching Conversations.

Matthew E Kelleher¹, C Y Zhou², Seth Overla³

¹Department of Pediatrics and Internal Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio, US.

Perspectives on Medical Education

|June 29, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) coaching agents like CAR-E can support medical education, but require refinement for effective, empathetic conversations. Further development is needed to enhance reflective dialogue and overcome limitations in AI-driven coaching.

Related Experiment Videos

Last Updated: Jun 30, 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
Artificial Intelligence
Human-Computer Interaction

Background:

Coaching in medical education offers benefits but faces implementation challenges like faculty time and cost.
Artificial intelligence (AI), particularly large language models (LLMs), presents a potential solution to augment coaching scalability.
Limitations in current AI coaching necessitate refinement for effective and valuable coaching conversations.

Purpose of the Study:

To develop and evaluate an AI coaching agent, Coaching with AI-Reinforced Education (CAR-E), for medical education.
To assess the feasibility and user experience of an LLM-based coaching tool in a medical training context.
To identify areas for improvement in AI coaching to better support self-reflection and goal clarification.

Main Methods:

Developed CAR-E, an AI coaching agent using an LLM with a layered architecture, retrieval-augmented generation (RAG), and a dual memory system.
Employed iterative design, refining CAR-E based on user feedback from voluntary coaching conversations.
Analyzed coaching transcripts and user feedback to identify strengths and areas for improvement.

Main Results:

37 medical trainees and faculty participated in pilot coaching conversations with CAR-E.
Users reported CAR-E facilitated self-reflection, goal clarification, and problem decomposition.
Feedback indicated issues with formulaic questioning, perceived lack of empathy, and frustration with AI's inability to offer advice.

Conclusions:

CAR-E demonstrates potential for augmenting medical education coaching through AI.
AI coaching agents require careful design, including evidence-based knowledge integration and memory systems, for responsible implementation.
Further improvements are needed to enhance the naturalness, empathy, and conversational flow of AI coaching to support reflective, growth-oriented dialogue.