ONVERSATIONAL AI FOR MENTAL HEALTH SUPPORT

Mental health support remains difficult to access for many individuals because of cost, stigma, limited availability of professionals, geographical barriers, and hesitation in discussing personal concerns.Conversational Artificial Intelligence (AI), particularly systems based on Natural Language Processing (NLP) and Large Language Models (LLMs), offers a potential supplementary channel through which users can communicate in natural language and receive basic emotional or informational support.However, mental health is a sensitive application area in which fluent responses alone are not sufficient: reliability, contextual understanding, empathy, privacy, safety, bias, emotional dependency, and appropriate referral to human professionals are important concerns.This paper reviews the development and application of conversational AI for mental health support and synthesizes the gaps identified in the reviewed literature.Based on these gaps, a conceptual framework is proposed that combines user-input analysis, conversational context, LLM-based response generation, safety validation, and supportive response or professional referral.The framework is evaluated conceptually using eight dimensions: response relevance, empathy, context awareness, reliability, safety, privacy, usability, and transparency.The paper emphasizes that conversational AI should function as a supportive technological tool rather than a replacement for qualified mental health professionals.