AI PsyRoom: Artificial Intelligence Platform for Segmented Yearning and Reactive Outcome Optimization Method
Yigui Feng, Qinglin Wang, Ke Liu, Xinhai Chen, Bo Yang, Jie Liu

TL;DR
AI PsyRoom introduces a multi-agent framework leveraging fine-grained emotion classification to generate empathetic dialogues and personalized treatment plans, significantly advancing AI-assisted psychological counseling.
Contribution
The paper presents a novel multi-agent simulation framework and datasets for emotionally nuanced dialogue generation and personalized treatment planning in psychological counseling.
Findings
AI PsyRoom outperforms state-of-the-art methods in empathy and communication quality.
Constructed EmoPsy dataset with 35 sub-emotions and 12,350 dialogues.
Achieved up to 24% improvement in key counseling metrics.
Abstract
Psychological counseling faces huge challenges due to the growing demand for mental health services and the shortage of trained professionals. Large language models (LLMs) have shown potential to assist psychological counseling, especially in empathy and emotional support. However, existing models lack a deep understanding of emotions and are unable to generate personalized treatment plans based on fine-grained emotions. To address these shortcomings, we present AI PsyRoom, a multi-agent simulation framework designed to enhance psychological counseling by generating empathetic and emotionally nuanced conversations. By leveraging fine-grained emotion classification and a multi-agent framework, we construct a multi-agent PsyRoom A for dialogue reconstruction, generating a high-quality dialogue dataset EmoPsy, which contains 35 sub-emotions, 423 specific emotion scenarios, and 12,350…
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Taxonomy
TopicsDigital Mental Health Interventions · Mental Health via Writing · Machine Learning in Healthcare
