Enhancing Mental Health Support Through Human-AI Collaboration: Toward Secure and Empathetic AI-Enabled Chatbots

Access to mental health support remains limited, particularly in marginalized communities facing structural and cultural barriers. This paper examines the potential of AI-enabled chatbots as scalable solutions by evaluating advanced large language models (LLMs) such as GPT-4, Mistral, and Llama v3.1 for their ability to provide empathetic responses in mental health contexts. While these models show promise in generating coherent and structured responses, they struggle to replicate the emotional depth and adaptability of human therapists. Additionally, challenges related to trustworthiness, bias, and privacy persist due to unreliable datasets and limited collaboration with mental health professionals. To address these limitations, we propose a federated learning framework that ensures data privacy, reduces bias, and incorporates continuous clinician validation to enhance response quality. This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy and empathetic mental health support, thereby advancing AI's role in digital mental health care.