AIDE: Leveraging Retrieval-Augmented Generation for Context-Aware Educational Data Retrieval and Dialogue
Manoj Adhikari, Puskar Joshi, Gabriel Vieira Ramos, Ahmad Al Doulat, Shehenaz Shaik · 2025
This study introduces AIDE (Artificial Intelligence for Data Extraction and Dialogue in Education), an innovative platform designed to enhance data retrieval and presentation within academic institutions. AIDE combines large language models with advanced information retrieval systems to deliver precise and contextually relevant responses to user inquiries, facilitated by Retrieval-Augmented Generation (RAG) architecture. The platform's adaptability allows seamless customization across diverse educational settings, thereby improving communication, information accessibility, and user engagement. AIDE's implementation at East Tennessee State University (ETSU) serves as a prototype, demonstrating its practical application and effectiveness. Quantitative evaluations revealed an 85% retrieval accuracy with an average response time of 2.8 seconds, underscoring the system's efficiency and reliability. Key contributions of this research include the development of a scalable AI-driven platform for educational data retrieval, the implementation of domain-specific adaptations to meet institutional needs, and the enhancement of inclusivity through features that accommodate diverse user requirements. These findings confirm AIDE's ability to address prevalent challenges in educational data retrieval, offering a robust and usercentric solution for the academic community.