A Systematic Literature Review: Improving University Guidance with an AI-Powered Automatic Recommendation System

Abstract The integration of artificial intelligence (AI) into automatic recommendation systems for improving university guidance represents a major advancement in educational support services. Effective guidance helps students navigate their academic journey, make informed decisions about courses and career paths, and achieve their personal and professional goals. AI-powered systems offer personalized recommendations that can transform how students plan their future by delivering precise, tailored advice. Following PRISMA guidelines, we conducted a systematic literature review, analyzing 28 relevant studies from a pool of 451 articles published between 2013 and 2024. This review aimed to explore the influence of recommendation systems on student guidance, the development methods employed, the types of data used, and the evaluation metrics applied. Our findings highlight that machine learning and hybrid approaches improve the understanding of student needs and behaviors, enabling more accurate and relevant recommendations. Furthermore, these systems can contribute to lower dropout rates and enhanced student success. The most commonly used data types include academic data, personal and preference data, demographic data, family and social background data, and skills and competency data.