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Purpose of Study: The study aims to develop an AI-driven decision support system tailored to the specific needs of higher education institutions. This system is designed to assist administrators, educators, and stakeholders in making informed decisions related to student enrollment, course planning, resource allocation, academic performance, and institutional effectiveness. Methodology: The study employs a comprehensive framework that integrates machine learning algorithms, data analytics, and predictive modeling to process vast amounts of data from diverse sources within higher education. The system incorporates adaptive learning algorithms to personalize student experiences, optimize curricula, and identify at-risk students for intervention. Ethical considerations are embedded in the system design, ensuring fairness, accountability, and transparency. Results: The proposed decision support system demonstrated its potential to enhance decision-making processes by providing actionable insights into student performance, faculty effectiveness, and resource utilization. The system proved effective in supporting both administrative and academic decisions, while prioritizing ethical standards such as fairness and transparency. Implication of Study: The implementation of this AI-based decision support system has significant implications for higher education institutions. It offers the potential to transform decision-making processes, improve institutional effectiveness, and personalize student experiences. Furthermore, it highlights the importance of collaboration between data scientists, educators, and administrators to maintain ethical standards and ensure data integrity and security. Novelty: The novelty of this study lies in its tailored approach to higher education, incorporating adaptive learning and ethical decision-making principles within an AI-based system. This comprehensive model not only supports traditional decision-making processes but also addresses emerging challenges in higher education by leveraging data-driven insights and advanced machine learning techniques.
Paul et al. (Tue,) studied this question.