🤖 AI Summary
Motivational Interviewing (MI) training for counselors suffers from infrequent, coarse-grained, and contextually detached feedback. Method: This study introduces the first LLM-driven framework for MI training that integrates high-fidelity simulated patients with discourse-level, real-time visual feedback. It employs large language models to generate realistic, responsive patient avatars; combines dialogue state modeling with MI behavior coding to produce empathic, pedagogically grounded turn-by-turn feedback; and delivers performance analytics via an interactive dashboard. Contribution/Results: Evaluated with professional and trainee counselors, the system significantly improves feedback timeliness (average latency <3 seconds) and reflective depth (42% improvement in reflection journal quality), achieving a user satisfaction score of 4.7/5.0. Its modular architecture supports generalization to other social skill training domains, establishing a novel AI-augmented paradigm for clinical psychology education.
📝 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.