Networked aspects of how school leaders make their organisations ready for applying Artificial Intelligence

Abstract

This paper examines how school leaders in Australia and Sweden prepare their organisations to adopt Artificial Intelligence (AI), with a focus on the social configurations through which organisational readiness is developed. Although AI in education research has largely concentrated on teachers’ and students’ perspectives, less attention has been given to school leaders’ perceptions of AI and their role in guiding adoption across schools. The study is theoretically grounded in Weiner’s (2009) Organisational Readiness for Change Model, conceptualising readiness through two core dimensions: change commitment (collective willingness to implement AI) and change efficacy (shared confidence in the organisation’s ability to do so). To extend this framework, the paper draws on networked learning perspectives, highlighting how readiness is shaped through leaders’ engagement in professional networks, collegial dialogue, and staff participation and development. A qualitative cross-national design was employed, using semi-structured interviews with school leaders in Australia (n=6) and Sweden (n=4). Interviews explored leaders’ understandings of AI, organisational strategies and visions, perceived enablers and barriers to adoption, and how collaboration with colleagues and external actors supported decision-making. Data were analysed through reflexive thematic analysis (Braun & Clarke, 2019), combining deductive coding guided by Weiner’s model with inductive coding to capture emerging themes related to networked learning. Preliminary findings indicate cautious optimism in both contexts, with AI primarily framed as supporting administrative efficiency and idea generation rather than driving pedagogical transformation. Australian leaders reported a stronger commitment, with explicit school-level strategies legitimising action and reducing perceived risk. Swedish leaders’ commitment was often constrained by concerns about academic integrity, data protection, and uneven staff competence, contributing to a “wait-for-policy” orientation and fragmented engagement. Change efficacy was uneven across both settings, frequently depending on individual champions rather than shared routines, although Australian leaders described growing organisational capacity through targeted professional learning and emerging leadership structures. Overall, the findings suggest that more intentional and sustained networked learning infrastructures, alongside clearer system-level guidance, may strengthen organisational readiness and support scalable AI adoption.