Artificial intelligence in education: A systematic literature review of machine learning approaches in student career prediction

Abstract

This paper presents a systematic literature review of using Machine Learning (ML) techniques in higher education career recommendation. Despite the growing interest in leveraging AI for personalized academic guidance, no previous reviews have synthesized the diverse methodologies in this field. To fill this gap, we analyzed 38 studies selected from an initial pool of 1296, using a combination of search phrases and digital libraries. Our findings reveal that Random Forest, Support Vector Machines (SVM), and Neural Networks are the most frequently employed models to improve the accuracy and personalization of career recommendations in higher education. These systems typically use academic performance, personal interests, and demographic data as the primary data types. The review also highlights key validation metrics like precision, recall, and F1-score, which reflect the effectiveness of these models. However, limitations were identified, such as the lack of access to open datasets and the scarcity of studies with longitudinal data that evaluate the long-term impact of recommendations. This systematic literature review provides a solid foundation for improving career recommendation systems using advanced ML techniques. It highlights the potential to revolutionize career guidance while also addressing ethical considerations and the necessity of integrating with traditional counseling approaches.