Educational Leadership in the Era of Emerging AI Adoption: An Exploratory Study of Human - AI Collaboration in Higher Education
DOI:
https://doi.org/10.65222/VIRAL.2026.7.44.64Keywords:
Abstract
The rapid emergence of generative artificial intelligence (AI) is reshaping teaching, learning, and academic work, creating new opportunities and challenges for educational leadership. While recent studies have documented increasing AI adoption in higher education, existing evidence is largely derived from technologically advanced contexts and early adopters, offering limited insight into institutions where AI integration remains in its initial stages. Consequently, there is a need to better understand how emerging AI practices may influence future leadership strategies and human–AI collaboration in higher education.
This exploratory study investigates the patterns of AI use among students and educators within an emerging adoption context. Drawing on survey data collected from 76 university students and 38 higher education educators from multiple institutions, the study examines the educational tasks supported by AI, perceived benefits and challenges, and expectations regarding the evolving role of AI in academic activities. Rather than measuring institutional AI maturity or leadership effectiveness, the study seeks to identify early signals of human–AI collaboration that may inform educational leadership in the context of gradual AI adoption.
The findings indicate that AI is primarily employed to support routine educational activities, including information retrieval, content summarization, brainstorming, text improvement, lesson preparation, and administrative tasks. Both students and educators perceive AI predominantly as an augmentation tool that enhances productivity and facilitates learning rather than as a substitute for human expertise. However, concerns regarding academic integrity, critical thinking, overreliance on AI-generated content, digital competencies, and the absence of institutional policies remain significant barriers to broader adoption. These findings suggest that higher education institutions are currently experiencing an early phase of AI integration in which leadership priorities extend beyond technology implementation toward fostering responsible AI use, developing digital capabilities, and establishing governance mechanisms that support effective human–AI collaboration.
The study contributes to the emerging literature on educational leadership by extending discussions of AI adoption beyond technologically mature environments and highlighting the importance of leadership preparedness during the early stages of organizational AI adoption. Rather than offering generalizable conclusions, the findings provide exploratory evidence that can support the development of future conceptual models and empirical research on educational leadership, human–AI collaboration, and institutional AI governance across diverse higher education contexts.
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