This research develops a framework for analyzing problem-solving therapy dialogues using large language models, laying groundwork for potential automation in mental healthcare.

• Analyzes how LLMs can interpret therapy session structures and outcomes
• Creates a systematic approach to understanding therapy conversations
• Bridges the gap between conversational AI and practical therapeutic applications

This work has significant implications for mental healthcare, potentially expanding access to therapeutic resources through technology while maintaining clinical effectiveness.

From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis