Enhanced Retrieval-Augmented Generation for Student Mental Health Support Using Generative Artificial Intelligence

Generative AI, combined with Retrieval-Augmented Generation (RAG), enhances large language models (LLMs) by integrating dynamic information retrieval, significantly improving response accuracy and contextual relevance.This study explores the implementation of Generative AI-powered RAG in student mental health support systems, demonstrating how it mitigates hallucinations, incorporates real-time knowledge updates, and personalizes assistance.The proposed system integrates retrieval and generation components, leveraging vector-based search mechanisms to access domain-specific mental health knowledge.Comparative analysis with traditional LLMs highlights RAG's superior accuracy, reduced misinformation, and improved response reliability.Experimental evaluation using context precision, hit rate, faithfulness, and user satisfaction metrics validates the system's effectiveness.This research underscores the transformative potential of Generative AI-driven RAG in delivering scalable, evidence-based mental health support for students.