Artificial Intelligence-Assisted Strategic Decision-Making for Sustainable University Governance
Downloads
Artificial Intelligence (AI) has emerged as a transformative technology that enhances organizational decision-making by enabling institutions to analyze complex data, generate predictive insights, and support evidence-based governance. In higher education, AI offers significant opportunities to strengthen strategic planning, resource optimization, and institutional sustainability. This study examined the role of Artificial Intelligence-assisted strategic decision-making in promoting sustainable university governance among selected State Universities and Colleges (SUCs) in the Philippines. Specifically, it determined the level of implementation of AI-assisted strategic decision-making, assessed the level of sustainable university governance, examined the significant relationship between the two variables, and developed an Artificial Intelligence-Assisted Strategic Decision-Making Framework for sustainable university governance. The study employed a quantitative descriptive-correlational research design using a researcher-developed questionnaire administered to university administrators involved in institutional planning and governance. Data were analyzed using mean, standard deviation, and Pearson Product-Moment Correlation. The findings revealed that universities demonstrated a very high level of implementation of Artificial Intelligence-assisted strategic decision-making, with an overall mean of 4.21 (SD = 0.59). Among the dimensions, AI-driven data analytics obtained the highest rating, followed by intelligent resource management, while predictive decision support and automated strategic planning were rated high. Likewise, sustainable AI-driven data analytics university governance was assessed at a very high level with an overall mean of 4.27 (SD = 0.56). Transparency and accountability received the highest rating, followed by strategic leadership, operational efficiency, and institutional sustainability. Correlation analysis further revealed a strong positive and statistically significant relationship between Artificial Intelligence-assisted strategic decision-making and sustainable university governance (r = .821, p < .001), indicating that greater implementation of AI technologies is associated with stronger governance practices. Based on these findings, the study developed an Artificial Intelligence-Assisted Strategic Decision-Making Framework that integrates institutional data sources, AI technologies, intelligent decision support systems, and governance processes to facilitate evidence-based strategic planning, resource optimization, policy formulation, and institutional sustainability. The study concludes that Artificial Intelligence serves as a strategic enabler that enhances governance effectiveness by improving transparency, accountability, leadership, operational efficiency, and long-term institutional resilience. It is recommended that higher education institutions strengthen AI integration in governance, establish comprehensive AI governance policies, invest in digital infrastructure and capacity building, and pilot-test the proposed framework to support sustainable university governance and digital transformation initiatives.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... Schafer, B. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI. British Journal of Educational Technology, 50(6), 2824–2838.
OECD. (2021). Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of Artificial Intelligence. California Management Review, 61(4), 66–83.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), Article 39.
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of Artificial Intelligence. Business Horizons, 63(1), 37–50.
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Agarwal, R., Gans, J., & Goldfarb, A. (2022). The simple economics of artificial intelligence. Harvard Business Review Press.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Ifenthaler, D., & Schumacher, C. (2023). Artificial Intelligence in higher education: Implications for teaching, learning, and institutional management. Springer.
Bond, M., Zawacki-Richter, O., & Nichols, M. (2024). Artificial intelligence in higher education: Emerging trends and future directions. Educational Technology Research and Development, 72(1), 1–24.
Power, D. J., & Sharda, R. (2020). Decision support systems. In Business intelligence, analytics, and data science. Springer.
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C.-E. (2020). Influence of artificial intelligence on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Leal Filho, W., Salvia, A. L., Frankenberger, F., & others. (2023). Sustainable development in higher education institutions: Global trends and future directions. International Journal of Sustainability in Higher Education, 24(5), 1035–1055.
Mhlanga, D. (2023). Artificial intelligence in higher education: Applications, opportunities, and challenges. Education and Information Technologies, 28(9), 11559–11582. https://doi.org/10.1007/s10639-023-11602-2
Bharadiya, J. P. (2023). Artificial intelligence and strategic leadership: Opportunities and challenges for organizational decision-making. International Journal of Innovative Research in Computer Science & Technology, 11(4), 1–9.
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
J. C. O. Mamaril et al., "Predicting College Students’ Academic Performance Using Machine Learning," 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET), Cairo, Egypt, 2026, pp. 1-6, doi: 10.1109/ICAISET66439.2026.11541299.
