AI-DRIVEN DECISION-MAKING IN EDUCATIONAL MANAGEMENT: CHALLENGES AND OPPORTUNITIES IN EDUCATION MANAGEMENT
Keywords:
AI, AI-driven Decision-Making, Challenges, Opportunities for Educational ManagementAbstract
The paper examined the concept of AI-driven decision-making in educational management by exploring the conceptual foundations of Artificial Intelligence, educational management, and AI-enabled decision processes. This paper also highlighted critical challenges associated with AI adoption, including ethical concerns, algorithmic bias, data privacy and security, inadequate digital infrastructure, resistance to organizational change, limited AI competencies among educational managers, and regulatory issues. It further examined the opportunities presented by AI, including improved strategic planning, enhanced student performance monitoring, efficient resource management, personalized learning support, and strengthened institutional accountability. The paper argued that while AI has considerable potential to improve educational management, its successful implementation depends on responsible governance, human oversight, institutional readiness, and adherence to ethical principles. The paper concluded that the successful adoption of AI-driven decision-making in educational management depends on balancing technological innovation with ethical principles, professional expertise, and human-centered leadership. Therefore, the paper suggested that when AI-enabled decision processes are implemented responsibly, the potential to enhance institutional effectiveness and contribute to the delivery of quality, equitable, and sustainable education would be achieved.
References
Bush, T. (2020). Theories of educational leadership and management (5th ed.). Sage.
Davis, F. D. (1989). Perceived usefulness, ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
European Commission. (2021). Proposal for a regulation laid down harmonized rules on artificial intelligence (A Intelligence Act). Publications Office of the European Union.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilizing learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite: The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50.
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1–38. https://doi.org/10.1145/3583557
Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
OECD. (2021). Digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing.
OpenAI. (2023). GPT-4 technical report. https://doi.org/10.48550/arXiv.2303.08774
Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths, and total nonsense: Profiting from evidence-based management. Harvard Business School Press.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254). Association for Computing Machinery. https://doi.org/10.1145/2330601.2330661
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 STUDIES IN EDUCATION
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.