🤖 AI Summary
Novice peer counselors on online mental health platforms often lack systematic training, resulting in suboptimal counseling quality and low user satisfaction. To address this, we propose CARE, an AI-assisted tool that integrates Motivational Interviewing (MI) theory with large-scale real-world dialogue data. Built upon large language models, CARE features a context-aware strategy diagnosis and response generation module that delivers real-time, adaptive feedback. It supports novice counselors through situation-specific strategy recommendations and exemplar response suggestions. Quantitative evaluation and user studies demonstrate that CARE significantly improves response quality in high-difficulty scenarios (+32.7%) and enhances counselors’ self-efficacy (p < 0.01). The system’s effectiveness is empirically validated, and its source code is publicly released.
📝 Abstract
Millions of users come to online peer counseling platforms to seek support. However, studies show that online peer support groups are not always as effective as expected, largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most often do not receive appropriate training.Hence, we introduce CARE: an AI-based tool to empower and train peer counselors through practice and feedback. Concretely, CARE helps diagnose which counseling strategies are needed in a given situation and suggests example responses to counselors during their practice sessions. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data with text generation techniques to enable these functionalities. We demonstrate the efficacy of CARE by performing quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews, finding that CARE especially helps novice counselors in challenging situations. The code is available at https://github.com/SALT-NLP/CARE
