An AI-driven digital transformation model for empowering educational technology staff in private schools: The mediating role of technological readiness
Abstract
This study examines the impact of AI Capability Integration, Digital Leadership, and Continuous Digital Training on the empowerment of Educational Technology (EdTech) staff in private schools, with particular emphasis on the mediating role of Technological Readiness. It investigates how AI-enabled systems, leadership support, and ongoing digital skill development contribute to strengthening technological readiness and enhancing staff empowerment within digitally transformed educational environments. Data were collected from 372 EdTech staff working in private schools using a structured questionnaire and analyzed using Structural Equation Modeling (SEM) via SmartPLS. The findings reveal that AI Capability Integration, Digital Leadership, and Continuous Digital Training all have significant positive effects on both Technological Readiness and Staff Empowerment. In addition, Technological Readiness shows a strong positive effect on Staff Empowerment. Moreover, Technological Readiness was found to significantly mediate the relationships between AI Capability Integration, Digital Leadership, and Continuous Digital Training with Staff Empowerment, confirming its critical role in translating digital transformation efforts into empowerment outcomes. All hypotheses were statistically supported. This study contributes to the digital transformation and educational technology literature by explaining how AI-enabled capabilities and organizational factors enhance staff empowerment through technological readiness. It also provides practical implications for school leaders and policymakers aiming to strengthen EdTech staff performance through integrated AI adoption, leadership support, and continuous training strategies.
Fields of Study & Subjects
Educational Leadership and Innovation
Technostress in Professional Settings
Teacher Education and Leadership Studies
Publication details
- Journal / source
- International Journal of Data and Network Science
- Publisher
- Growing Science
- Volume
- 10
- Issue
- 4
- Pages
- 1763–1776
- Publication date
- 1 January 2026
- Language
- EN