Education Innovation and Change Leadership in an Emerging Artificial Intelligence Society

Dear Colleagues,

Artificial intelligence is reshaping society, industry, and education at an accelerating rate; in response to this, education system leadership has been calling for university and school systems to accelerate efforts to update themselves (Khan, 2023). In the last five years, for example, the world’s top ten producing countries (G10) have allocated nearly one trillion dollars via their national strategic plans to artificial intelligence (Kowch, 2025). Less than 5% of these policymakers hail from education-related disciplines, whereas most funding has been allocated to foundational AI (science) university activity. Teacher preparation programs, as a result, fall behind as concerns AI integration compared to other sectors (Weiner, 2024), and universities struggle to develop AI-literate leaders (Aoun, 2017).

Given this background, the aim of this Special Issue is to explore the intersection of artificial intelligence (AI), educational innovation, and change leadership within K012 and higher education ecosystems. As AI technologies—ranging from generative AI to learning analytics—are increasingly embedded in teaching, assessment, and administration systems, the need for adaptive and forward-facing leadership has never been greater. Despite burgeoning research on AI applications in education, there is limited scholarly engagement with how educational organizations, leaders, and policy makers respond to, and shape, AI-driven change. Thus, this Special Issue will critically examine the theoretical, empirical, policy-related, and social dimensions of leading educational transformation in the AI era.

Aligned with the aims of MDPI’s Social Sciences journal, this Issue emphasizes interdisciplinary approaches to social, organizational, leadership, policy, teaching, learning, governance, and community change leadership within AI-integrating contexts. AI’s integration in universities, for example, presents significant implications for research processes (Ifargan et al., 2024), for teaching and learning (Benavides-Prado et al., 2020), for faculty service (Shin et al., 2024), and even for libraries, emerging as curators of verified information (Barman et al., 2024). Absent from this discourse is the perspective of leaders and leadership scholars in this context.

We welcome research contributions spanning disciplines, education systems, and communities involved with education institutions. Contributions may contain qualitative, conceptual, or review methodologies.

Topics include, but are not limited to, the following:

• Leading to address AI-related fears in educational and social systems;
• Leading changes in the research process and in products with AI;
• Innovation policy design, analysis or development—implications for AI education systems;
• Leading change in teaching, learning, and innovation in AI contexts;
• Social entrepreneurial/social innovation perspectives on K-12- or university-integrated AI;
• Educational institutions and AI leadership—perspectives from the information technology domain;
• The implications and impacts of AI in education policy and governance;
• Leadership development and lifelong learning for AI educators—perils and opportunities;
• The community impacts of AI on universities—challenges and opportunities;
• Education system governance and AI—constraints and enablers.

Aoun, J. (2017). Robot-proof higher education in the age of artificial intelligence. MIT Press. https://doi.org/10.7551/mitpress/11456.001.0001.

Barman, D., Guo, Z., & Conlan, O. (2024). The dark side of language models: Exploring the potential of LLMs in multimedia disinformation generation and dissemination. Machine Learning with Applications, 16,1–17, Article 100545. https://doi.org/10.1016/j.mlwa.2024.100545

Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2020). Towards knowledgeable supervised lifelong learning systems. The Journal of Artificial Intelligence Research, 68, 159–224. https://doi.org/10.1613/jair.1.11432

Ifargan, T., Hafner, L., Kern, M., Alcalay, O., & Kishony, R. (2024). Autonomous LLM driven research from data to human-verifiable research papers. arXiv. https://doi.org/10.48550/arXiv.2404.17605

Khan, S. (2023). Brave new words: How AI will revolutionize education (and why that’s a good thing). Crown Publishing Group.

Kowch, E. G. (2025). An innovation policy analysis of G10 nations’ strategic plans for future AI leaders and researchers in education. In S. Sabbaghan (Ed.)., Navigating Generative AI in Higher Education (pp. 211–234). https://doi.org/10.4337/9781035337873.00017 

Shin, C., Gi Seo, D., Jin, S., Hwa Lee, S., & Je Park, H. (2024). Educational technology in the university: A comprehensive look at the role of a professor and artificial intelligence. IEEE Access, 12, 116727–116739. https://doi.org/10.1109/ACCESS.2024.3447067

Weiner, S., Lake, R., & Rosner, J. (2024). AI is evolving, but teacher prep is lagging: A first look at teacher preparation program responses to AI. Center for Reinventing Public Education. October, 2024).  https://crpe.org/ai-is-evolving-but-teacher-prep-is-lagging/

Dr. Eugene G. Kowch
Guest Editor

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• artificial intelligence
• digital transformation
• leadership development
• organizational change
• social innovation
• higher education innovation
• education innovation
• educational change