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@article{190339,
        author = {Shravani Jadhav and Anupama Tilak and Om Jadhav and Rutuja Jadhav and Dr. Bharati P. Vasgi and Prof. Punam Chavan},
        title = {From Rule-Based Agents to Agentic AI: A Comprehensive Survey of Mental Health Chatbots},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1824-1828},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190339},
        abstract = {Conversational agents have gained increasing attention in mental health care as scalable, cost-effective, and stigma-free support tools. Evolving from rule-based systems to advanced dialogue models powered by large language technologies, these chatbots are now applied across diverse domains, including general mental health awareness, student support, caregiver assistance, and disorder-specific interventions. Despite their promise, many existing systems lack flexibility, personalization, and the ability to effectively address the needs of heterogeneous user groups particularly caregivers supporting children with early-onset mental health conditions. Recent advances in agentic AI, enabling autonomous reasoning, context-aware interaction, and adaptive decision-making, offer new opportunities to address these limitations. In parallel, retrieval-augmented generation (RAG) is being explored to improve reliability, trustworthiness, and user-centered interaction. This paper surveys current mental health chatbot solutions, categorizing them into general-purpose, student focused, caregiver-oriented, and disorder specific frameworks, and discusses open challenges and future directions for agentic AI–driven chatbot design.},
        keywords = {Agentic AI, Human-AI collaboration, mental chatbot, Retrieval Augmented Generation (RAG), Large Language Models (LLMs)},
        month = {January},
        }