Artificial Intelligence in Education: A Systematic Review of Machine Learning for Predicting Student Performance

Abstract

Artificial Intelligence is increasingly being employed in education, specifically through machine learning techniques, to improve the quality of education and refine teaching and learning methods. Despite its positive impacts on education quality and social life, machine learning technology poses ethical and practical concerns, especially in predicting student performance. To address these concerns, this study conducts a systematic literature review on machine learning technology for predicting student performance, analysing 51 relevant articles from Scopus and Science Direct databases between 2019 and 2023 using the PRISMA method. The findings reveal that the primary motivation for employing machine learning in educational institutions is to improve predictive accuracy, identify early interventions, and optimise decision-making processes. Supervised machine learning approaches such as Decision Trees, Linear Models, and Neural Networks are commonly used. However, machine learning techniques encounter challenges such as overfitting, scalability, and generalizability, which may impact education practices' fairness, accountability, and transparency. The study provides valuable insights into the benefits of machine learning, ethical considerations, and practical recommendations to guide stakeholders, including educators, researchers, policymakers, and administrators, in navigating the convergence of artificial intelligence and education. These insights emphasise the critical need for equitable model implementation, data collection, and decision-making to mitigate bias in real-world educational settings.