An Advanced Retrieval-Augmented Generative AI Framework for Personalized Student Mental Health Support
Keywords:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Student Mental Health Support, Vector Database, AI for Mental Health, Context-Aware AI.Abstract
This research article presents a Retrieval-Augmented Generation (RAG) framework designed to improve the effectiveness of large language models (LLMs) in supporting student mental health. By combining generative AI with real-time information retrieval, the proposed system delivers accurate, personalized, and context-aware responses tailored to students’ mental health needs. Unlike traditional LLMs that rely solely on pre-trained data, our RAG approach integrates a vector-based search over a domain-specific knowledge base comprising academic literature, therapeutic guidelines, and counseling transcripts. Comparative evaluations with conventional LLMs highlight RAG’s advantages in accuracy, relevance, and user satisfaction. This research work demonstrates how intelligent retrieval combined with generation mechanisms can significantly enhance the delivery of scalable, evidence-based mental health interventions for students.
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