Hybrid Retrieval Augmented Models for Educational Knowledge Discovery

Abstract

Hybrid Retrieval-Augmented Models for educational knowledge discovery combine deep learning architectures with multiple retrieval mechanisms to enhance the discovery, organization, and application of academic knowledge. These systems integrate dense and sparse retrieval techniques, Large Language Models (LLMs), knowledge graphs, and curriculum-aware databases to improve accuracy, relevance, and contextual understanding in educational environments. Unlike traditional retrieval systems that rely on a single retrieval strategy, hybrid models combine semantic retrieval with symbolic and structured knowledge sources, enabling richer and more reliable educational outputs. The integration of Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) validation, and Retrieval-Augmented Generation (RAG) further enhances transparency and trustworthiness. This study explores hybrid retrieval-augmented models for educational knowledge discovery through a qualitative literature review. Findings indicate improved knowledge accuracy, better curriculum alignment, enhanced semantic reasoning, and increased learner engagement. Despite challenges such as system complexity, computational cost, and data integration difficulties, hybrid retrieval systems provide a scalable foundation for intelligent educational knowledge discovery