Artificial Intelligence-Driven Transformation of Educational Governance Models
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Abstract
Background and Aim: The rapid advancements in artificial intelligence (AI) are transforming sectors such as education. The integration of AI in educational governance systems can revolutionize decision-making, administrative efficiency, and learning outcomes. This paper analyzes the impact of AI-driven tools on governance structures, policy-making, and educational management.
Materials and Methods: This study employs a mixed-methods approach, combining qualitative case studies and quantitative data from surveys and experiments conducted in educational institutions. The focus is on AI applications, including data analytics, personalized learning, and automated administrative processes.
Results: Institutions adopting AI tools reported up to a 35% improvement in administrative efficiency and enhanced policy responsiveness. AI-driven governance models have improved decision-making accuracy, reduced administrative burden, and personalized learning. Challenges included data privacy concerns, algorithmic bias, and limited access in underserved areas.
Conclusion: AI has the potential to enhance educational governance efficiency and quality. However, challenges such as ethical concerns, data privacy issues, and the need for adequate training persist. This paper proposes a framework for the responsible integration of AI in educational governance.
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