Digital Transformational Leadership In Higher Education For Navigating Institutional Change In The Era Of Artificial Intelligence
Keywords:
Digital Leadership, Artificial Intelligence, Higher Education, Institutional Transformation, Educational InnovationAbstract
The rapid development of artificial intelligence (AI) has significantly transformed higher education institutions worldwide. Universities are required to adapt to technological changes that influence teaching, research, and administrative systems. Digital transformational leadership has become a strategic approach that enables institutional leaders to manage digital transformation while maintaining academic quality and organizational sustainability. This study aims to explore the role of digital transformational leadership in supporting institutional change within higher education in the era of artificial intelligence. The study employs a qualitative literature review method by analyzing scholarly publications related to digital leadership, AI adoption, and higher education transformation. The findings indicate that digital transformational leadership contributes to effective institutional adaptation through data-driven decision-making, innovation in teaching and learning, and the development of digital competencies among academic communities. Leadership that integrates technological awareness with transformational values enables universities to respond to technological disruption and maintain institutional resilience. The study highlights that successful digital transformation in higher education depends on visionary leadership capable of integrating artificial intelligence technologies with institutional strategies and ethical governance.
References
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. MIT Press.
Bates, A. W. (2019). Teaching in a digital age: Guidelines for designing teaching and learning (2nd ed.). BCcampus.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159. https://doi.org/10.1145/3287560.3287598
Bond, M., Zawacki-Richter, O., & Nichols, M. (2020). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 51(1), 12–63. https://doi.org/10.1111/bjet.12730
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
https://doi.org/10.1191/1478088706qp063oa
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
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
Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6
European Commission. (2021). Ethics guidelines for trustworthy artificial intelligence. Publications Office of the European Union.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Goodyear, P. (2015). Teaching as design. HERDSA Review of Higher Education, 2, 27–50.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Research and Practice in Technology Enhanced Learning, 15(21). https://doi.org/10.1186/s41039-020-00132-x
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Kane, G. C., Phillips, A. N., Copulsky, J., & Andrus, G. (2019). How digital leadership is evolving in organizations. MIT Sloan Management Review, 60(3), 1–5.
Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: A critical literature review. Learning, Media and Technology, 39(1), 6–36. https://doi.org/10.1080/17439884.2013.770404
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing.
Redecker, C., & Punie, Y. (2017). European framework for the digital competence of educators. European Commission Joint Research Centre. https://doi.org/10.2760/159770
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Salmon, G. (2019). May the fourth be with you: Creating education 4.0. Journal of Learning for Development, 6(2), 95–115.
Selwyn, N. (2019). Should robots replace teachers? Artificial intelligence and the future of education. Cambridge, UK: Polity Press.
Selwyn, N. (2022). Education and technology: Key issues and debates. Bloomsbury Academic.
Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254. https://doi.org/10.1145/2330601.2330661
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
https://doi.org/10.1016/j.jbusres.2019.07.039
Stahl, B. C., Wright, D., & Ikonen, V. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31(3), 504–526. https://doi.org/10.1007/s40593-020-00239-1
Tsai, Y. S., Moreno-Marcos, P. M., Tammets, K., Kollom, K., & Gašević, D. (2020). Shedding light on learning analytics adoption in higher education. The Internet and Higher Education, 46. https://doi.org/10.1016/j.iheduc.2020.100758
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Woolf, B. P. (2020). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(39). https://doi.org/10.1186/s41239-019-0171-0
