Artificial Intelligence Research in School Leadership and Management: A Text Mining-Based Computational Literature Review and Future Directions

Abstract

As artificial intelligence (AI) becomes increasingly embedded in school leadership and management literature, it is important to understand not only which themes are emerging, but also how coherent, mature, and distinct these themes have become. To address this need, this study conducted a text-mining-based computational literature review of 112 articles published between 2021 and 2026. The corpus was analyzed through three complementary procedures: Structural Topic Modeling (STM), conceptual maturity analysis, and semantic network fragmentation analysis. The STM analysis yielded five thematic axes: AI readiness, adoption, and self-efficacy; ethical and data-informed AI decision-making; AI literacy and digital leadership; teacher leadership and distributed agency in AI-integrated teaching; and governance frameworks for ethical and human-centered AI leadership. Ethical and data-informed decision-making exhibited the highest expected topic proportion and conceptual maturity, while AI readiness and adoption displayed moderate maturity and temporal continuity. By contrast, AI literacy and digital leadership, teacher leadership and distributed agency, and human-centered governance remained less consolidated. The semantic network analysis indicated that, within the controlled concept dictionary, the selected core concepts formed a connected co-word structure rather than a fully fragmented one; however, this pattern is interpreted as semantic convergence among dictionary-defined concepts rather than as evidence of complete theoretical integration. Future research should therefore strengthen conceptual and empirical work on these emergent themes, while extending mature themes through longitudinal, comparative, and intervention-oriented designs.