Applications of machine learning in operational aspects of academia: a review

Abstract

Educational institutions, propelled by digital transformation and sophisticated machine learning (ML) algorithms, amass plentiful data, facilitating the execution of complicated decision-making tasks previously inconceivable. ML’s pervasive influence extends beyond pedagogy and research, profoundly altering the fabric of academia and reshaping university functionalities. Its deployment in university administration enhances efficacy, efficiency, and operational streamlining across diverse levels. This article conducts a comprehensive review of extant knowledge pertaining to the diverse applications of ML in non-teaching domains within academic settings, delineating avenues for future research. The recognized findings furnish a robust foundation for the further exploration and refinement of ML applications, particularly within the administrative and operational realms of academia. A consequential outcome of this transformative integration is the mitigation of teachers’ administrative burdens. In practical terms, this liberation affords educators the opportunity to redirect their time and energy towards their primary responsibilities of educating and fostering the intellectual development of their students.