Validation of the Educational Leadership Model for Lower Secondary Schools in the Era of Generative Artificial Intelligence
Keywords:
Educational Leadership, Generative Artificial Intelligence, Digital Leadership, Ethical Leadership, Students’ Digital Identity, Lower Secondary Schools, Confirmatory Factor AnalysisAbstract
Purpose: The present study aimed to validate an educational leadership model for lower secondary schools in the era of generative artificial intelligence.
Methodology: This study was conducted using an exploratory mixed-method design consisting of qualitative and quantitative phases. In the qualitative phase, a meta-synthesis method and systematic review based on the PRISMA framework were employed to examine studies related to educational leadership and generative artificial intelligence. Subsequently, qualitative data were collected through semi-structured interviews with 24 experts in educational management, educational technology, and school leadership. Participants were selected through purposive sampling based on the theoretical saturation principle. Qualitative data were analyzed using thematic analysis with the assistance of MaxQDA software. In the quantitative phase, the extracted model was validated through confirmatory factor analysis using SmartPLS-V3 software. The statistical population included lower secondary school principals in Tehran Province, among whom 247 participants were selected as the sample. Data were collected using a researcher-made 59-item questionnaire measured on a five-point Likert scale.
Findings: Inferential findings revealed that the educational leadership model in the era of generative artificial intelligence consisted of four major dimensions: AI-based learning-centered strategic leadership, digital professional empowerment leadership for teachers, ethical AI leadership, and students’ digital identity leadership. Confirmatory factor analysis indicated that all factor loadings were above 0.4 and statistically significant. Furthermore, model fit indices demonstrated satisfactory levels, with GOF = 0.535, Q² = 0.223, and NFI = 1.017. Among the dimensions, AI-based learning-centered strategic leadership obtained the highest AVE value (0.762), while the component of learning design optimization achieved the highest factor loading (0.766).
Conclusion: The findings demonstrated that educational leadership in the era of generative artificial intelligence is a multidimensional construct extending beyond traditional school management and requiring the integration of strategic, technological, ethical, and identity-oriented dimensions. Moreover, the success of schools in adapting to intelligent technologies depends on school leaders’ ability to guide data-driven learning, enhance teachers’ digital competencies, ensure ethical and equitable technology use, and strengthen students’ responsible digital identity development.
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Copyright (c) 2025 Maryam Eskandari Nasrabad (Author); Mahbube Abdollahi; Alireza Chenari (Author)
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