Can LLMs improve counselor training simulations?
Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations
This paper presents SimPatient, an LLM-powered training system for counselors learning motivational interviewing (MI). It uses a multi-agent architecture where different LLMs handle simulated patient responses, behavior coding, dynamic cognitive factor modeling, and post-session feedback generation.
Key points for LLM-based multi-agent systems include: specialized prompting for different agent roles, chain-of-thought prompting for generating explanations, and challenges in simulating resistant patient behaviors and nuanced personas with LLMs. The dynamic cognitive model driven by a dedicated LLM agent offers valuable insights into training by simulating the impact of counselor actions on patient internal states. The system shows promise as an effective training tool, improving MI self-efficacy and garnering positive user feedback. However, further research is needed to address LLM limitations, including positivity bias and persona fidelity, and to incorporate more robust validation methods.