Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations

TL;DR

This paper introduces an AI-powered training system for counselors that uses simulated patients and detailed performance visualizations to enhance empathy and motivational interviewing skills, showing high usability and potential for broader social skills training.

Contribution

The paper presents a novel system integrating large language models and visual feedback to improve counselor training in motivational interviewing, with demonstrated effectiveness and usability.

Findings

High user satisfaction and usability in trials

Effective feedback improves counseling skills

Potential for application to other social skills training

Abstract

Learning therapeutic counseling involves significant role-play experience with mock patients, with current manual training methods providing only intermittent granular feedback. We seek to accelerate and optimize counselor training by providing frequent, detailed feedback to trainees as they interact with a simulated patient. Our first application domain involves training motivational interviewing skills for counselors. Motivational interviewing is a collaborative counseling style in which patients are guided to talk about changing their behavior, with empathetic counseling an essential ingredient. We developed and evaluated an LLM-powered training system that features a simulated patient and visualizations of turn-by-turn performance feedback tailored to the needs of counselors learning motivational interviewing. We conducted an evaluation study with professional and student counselors, demonstrating high usability and satisfaction with the system. We present design implications for the development of automated systems that train users in counseling skills and their generalizability to other types of social skills training.