Deep SAGE

A Strategic Questioning Framework for AI Counseling using Large Language Models and Deep Reinforcement Learning.

Overall research structure
Overall research structure.

Full Description

The growing demand for mental health services highlighted the need for AI-powered chatbots. However, existing counselor chatbots face significant shortcomings, including reliance on predetermined scripts, client-led conversation models, and a lack of implementation details for advanced features, restricting their ability to handle complex emotional contexts, guide scientifically validated counseling processes, and scale effectively.

We propose Deep SAGE, a Strategic AI Guidance Engine (SAGE). Deep SAGE integrates essential counselor characteristics with a scientifically validated counseling process, specifically cognitive behavioral therapy (CBT). Leveraging advanced natural language processing (NLP), large language models (LLMs), and deep reinforcement learning (DRL), fostering deeper self-disclosure and openness through strategic questioning and context-aware responses. Our work bridges the gap between theoretical counseling frameworks and practical, scalable implementations, offering a cost-effective and accessible mental health resource.

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Publications

In Preparation.

Authors: Qi Zhang, Heajun An, Minqian Liu, Xinyi Zhang, Sang Won Lee, Lifu Hwang, Pamela Wisniewski and Jin-Hee Cho

Presentations

ACM CAPWIC 2025 – Student Research Short Talk

Presented "Deep SAGE: A Strategic Questioning Framework for AI Counseling using Large Language Models and Deep Reinforcement Learning" at ACM CAPWIC 2025, March 2025. Slides

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