The network structure of research on artificial intelligence in school leadership and management: insights from a multi-method approach

Research indicates a significant increase in Artificial Intelligence (AI) studies within school leadership and management, yet the underlying knowledge production and citation network structures remain largely unexamined. This study addresses this gap by analyzing the citation network of 105 peer-reviewed studies to understand thematic and regional clustering, and the impact of citation counts on research visibility within this rapidly evolving domain.

Why it matters

Understanding the structure and evolution of research in AI within leadership and management is crucial for identifying key influential works, emerging trends, and potential knowledge gaps. This insight allows for more informed strategic allocation of research resources and the development of future-proof policies and educational frameworks.

Key insights

• Artificial Intelligence is a rapidly growing research focus in school leadership and management.
• Previous research has not statistically examined the knowledge production structure or citation networks of this field.
• Existing methods like thematic analysis and co-word mapping have identified themes but overlooked thematic/regional citation clustering and cumulative advantage of citations.
• This study uses a scientometric and citation-network design on 105 peer-reviewed studies.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1877606