While AI offers promising benefits for educational leaders, its adoption remains limited due to a range of challenges, including a lack of AI literacy, inadequate professional development, data privacy and fairness concerns, misinformation, the lack of capacity for emotional judgment, as well as access disparities.
Abstract
The integration of AI in education is transforming schools and the work of educational leaders. However, AI adoption also raises ethical concerns and presents substantial challenges that school leaders should consider. This systematic review examines research on the use of AI in K-12 school leadership, focusing on four key areas: (1) ethical considerations, (2) challenges to AI adoption, (3) perceived benefits, and (4) practical applications in leadership practices. We followed a systematic narrative review approach, analyzing peer-reviewed literature from WoS, Scopus, ERIC, and Google Scholar. 26 articles met the inclusion criteria and were included in the review. The results revealed that while AI offers promising benefits for educational leaders, such as enhancing decision-making, efficiency, and dealing with time-consuming administrative tasks, its adoption remains limited due to a range of challenges, including a lack of AI literacy, inadequate professional development, data privacy and fairness concerns, misinformation, the lack of capacity for emotional judgment, as well as access disparities. Despite these obstacles, evidence suggests that some leaders are incorporating AI into administrative tasks, yet often without clear guidelines. Implications for policy, practice, and future research have been discussed.
The study concludes that AI should function as a supportive technology that complements human judgement rather than replacing academic responsibility, providing implications for universities, educators, researchers, students, and policymakers.
Noor Hanim Rahmat· International journal of res...· 0 citations
The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.
Asem S. Obied, Ahmed Raja Haj Ali· Frontiers in Education· 0 citations
AI's ability to change educational leadership and supervision has garnered attention due to its rapid expansion. Early research has examined AI applications in educational administration, leadership decision-making, and learning analytics, but research on its effects on educational supervision is fragmented. Authors will map the current state of AI in educational supervision and leadership, identify new trends, potential, and issues, and provide an integrated conceptual framework for future study and practice. PRISMA-ScR scoping review was used in this investigation. A comprehensive search of major academic databases yielded 29 publications published between 2021 and 2026 that met the inclusion criteria for final analysis. The results were analysed using thematic coding and six domains: supervisory transformation, human–AI collaboration, data-driven governance, sociotechnical preparation, ethical governance, and future-oriented innovation. The results show that AI is making instructional supervision data-driven, predictive, and collaborative. The literature emphasises the growing importance of human–AI collaboration, learning analytics, evidence-based decision-making, and ethical governance in educational growth. However, digital preparation, algorithmic transparency, data privacy, and organisational competency issues hinder uptake. The AI-Supervision Framework, an integrative model of AI-enabled educational supervision as a human-centered ecosystem involving technology, data, leadership, organisational preparation, and ethical governance, is presented in this review. This review contributes to the field by consolidating fragmented scholarship on AI, educational leadership, learning analytics, and instructional supervision into a coherent evidence base. It advances theoretical understanding by conceptualising AI-enabled supervision as a human-centred sociotechnical ecosystem and provides an analytic foundation for future empirical research.
Rachmat Satria· International Journal of Mul...· 0 citations
Artificial Intelligence should be positioned as a complementary educational resource rather than a replacement for human expertise to ensure inclusive and meaningful educational transformation.
Therese Kabala - Mwagalwa· Journal of Literacy Educatio...· 0 citations
Artificial Intelligence should be positioned as a complementary educational resource rather than a replacement for human expertise to ensure inclusive and meaningful educational transformation.
The findings reveal that GenAI can effectively improve teaching efficacy, enable personalised learning experiences, and streamline assessment procedures, however, its implementation also draws attention to concerns regarding academic integrity, data privacy, algorithmic bias, and ethical governance.
Xi Bi· Exploring Science Academic C...· 0 citations
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