Enhancing Education Quality and Student Knowledge in Higher Education Using Machine Learning to Meet Societal Needs

Abstract

Machine learning (ML) is increasingly recognized for its potential to revolutionize higher education by addressing critical societal needs and enhancing educational quality (2023). This essay explores how ML applications can improve student knowledge, personalize learning experiences, and streamline administrative processes to better meet the evolving demands of society (Yurii Nykon, 2024). By analysing current research and trends, the essay identifies key areas where ML can be effectively implemented, such as predicting student performance, automating assessment, and providing personalized feedback (2023). The integration of ML in higher education not only improves educational outcomes but also prepares students with the skills and knowledge necessary to contribute to a rapidly changing world (Eric Klein Assistant Provost, Doctoral Research and Student Success, 2025). However, the essay also acknowledges the ethical and practical challenges associated with ML implementation, including data privacy, algorithmic bias, and the need for human oversight (2023). By addressing these challenges and promoting responsible use, higher education institutions can leverage ML to create more equitable, effective, and relevant learning environments, ultimately fostering a more knowledgeable and capable citizenry (2023). This essay argues that thoughtful and strategic deployment of ML can significantly enhance the quality of education and better align it with societal needs (2023).