Unique Presentation Identifier:
14
Faculty Advisor
Dr. Robin Ghosh
Document Type
Poster
Location
Face-to-face
Start Date
9-4-2026 1:00 PM
End Date
9-4-2026 3:00 PM
Abstract
Mental health challenges such as anxiety and depression are widespread, yet often remain unaddressed due to stigma, financial barriers, limited access to professionals, or personal reluctance. To address this gap, we present an accessible AI-powered chatbot that provides preliminary conversational support with empathetic, context-aware responses. The chatbot is trained and evaluated on two publicly available counseling conversation datasets from Huggingface, enabling it to learn and maintain therapeutic dialogue patterns. We compare three advanced LLMs, such as Llama3.1, Mistral 7b, and Qwen3, evaluating them on relevance, empathy, conciseness, and contextual understanding, and all models demonstrate high response quality. Incorporation of a Retrieval-Augmented Generation (RAG) pipeline with Facebook AI Similarity Search (FAISS) enhances factual accuracy and contextual relevance while reducing hallucinations. A key innovation is the integration of agentic AI, empowering the chatbot to recognize extreme distress and, with user consent, assist in scheduling appointments with mental health professionals. Overall, our system demonstrates the potential to deliver reliable, empathetic, and actionable conversational AI support for individuals experiencing early-stage mental health distress.
Recommended Citation
Wajiha, Shaira, "Proactive Mental Health Assistance via Agentic LLM Chatbots with Retrieval-Augmented Generation" (2026). ATU Scholars Symposium. 44.
https://orc.library.atu.edu/atu_rs/2026/2026/44
Included in
Proactive Mental Health Assistance via Agentic LLM Chatbots with Retrieval-Augmented Generation
Face-to-face
Mental health challenges such as anxiety and depression are widespread, yet often remain unaddressed due to stigma, financial barriers, limited access to professionals, or personal reluctance. To address this gap, we present an accessible AI-powered chatbot that provides preliminary conversational support with empathetic, context-aware responses. The chatbot is trained and evaluated on two publicly available counseling conversation datasets from Huggingface, enabling it to learn and maintain therapeutic dialogue patterns. We compare three advanced LLMs, such as Llama3.1, Mistral 7b, and Qwen3, evaluating them on relevance, empathy, conciseness, and contextual understanding, and all models demonstrate high response quality. Incorporation of a Retrieval-Augmented Generation (RAG) pipeline with Facebook AI Similarity Search (FAISS) enhances factual accuracy and contextual relevance while reducing hallucinations. A key innovation is the integration of agentic AI, empowering the chatbot to recognize extreme distress and, with user consent, assist in scheduling appointments with mental health professionals. Overall, our system demonstrates the potential to deliver reliable, empathetic, and actionable conversational AI support for individuals experiencing early-stage mental health distress.