HARNESSING ARTIFICIAL INTELLIGENCE AND MOTIVATION THEORIES FOR TRANSFORMATIVE EDUCATIONAL LEADERSHIP: A DATA-DRIVEN MODEL FOR SCHOOL MANAGEMENT AND PERSONALISED LEARNING

Abstract

Personalised learning and school administration are changing due to the introduction of artificial intelligence (AI) into education. AI-powered solutions provide data-driven insights that improve decision-making, maximise teaching methods, and raise teacher and student motivation. Nonetheless, there is still a lack of research on how AI and motivation theories relate to educational leadership. It is crucial to comprehend how AI might complement motivational frameworks to promote efficiency, engagement, and general academic performance. This study aims to investigate the connection between motivation theories and AI-driven educational leadership. Through the integration of psychological concepts, including Maslow's Hierarchy of Needs, Herzberg's Two-Factor Theory, Self-Determination Theory, Expectancy Theory, and Goal setting Theory. It investigates how AI technologies can improve personalised learning experiences, improve school management, and support teacher motivation. The study also suggests a conceptual model for AI-driven school administration. The study emphasises AI's capacity to monitor student participation, tailor instruction, and offer prognostic information for early interventions. However, issues like bias in AI, data privacy, and ethical ramifications need to be addressed. The finding made important theoretical and practical contributions, by offering highlights on the need to develop ethical frameworks, integrate AI-based analytics for teacher and students support, and implement AI literacy initiatives, stressing that cooperation between legislators, educators, and AI developers is essential to maximise AI's educational benefits.