Paper Title
Applications of Retrieval-Augmented Generation Systems in Higher Education: A Review

Abstract
This paper presents a thorough review of the emerging field of Retrieval Augmented Generations (RAGs), with focus on its applications in higher education domain. RAGs’ exceptional properties, inherited from the strengths of Large Language Models (LLMs) with Information Retrieval (IR) models, bring novelty and creativity to its applications in different activities in higher education including teaching and learning, student services and administration, and quality and institutional effectiveness. This survey to the literature reveals the increasing utilization of RAG in education, driven by to its pivotal role in advancing the provided services and enhancing students learning experience. It identifies critical gaps in the current research and outlines promising future directions with transformative potential for higher education, offering valuable guidance for researchers aiming to advance this emerging field. Keywords - Higher education, Large Language Model, Retrieval Augmented Generation, Information Retrieval.