DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-BASED TQM TO IMPROVE TEACHER PERFORMANCE AND THE QUALITY OF LEARNING IN KINDERGARTENS
Keywords:
Total Quality Management; Artificial Intelligence; Kinerja Guru; Mutu Pembelajaran; Taman Kanak-KanakAbstract
This article aims to examine the development of Artificial Intelligence (AI)-based Total Quality Management (TQM) to enhance teacher performance and learning quality in kindergarten education. This study employs a literature review approach by analyzing recent theoretical frameworks, empirical studies, and policy documents related to TQM in education, AI integration in educational management, and teacher performance improvement in early childhood education. The review indicates that integrating AI into TQM strengthens continuous quality improvement through data-driven decision-making, systematic performance evaluation, and structured feedback mechanisms. AI supports the PDCA cycle by enabling real-time monitoring, predictive analysis, and evidence-based managerial decisions. Conceptually, AI-based TQM offers an innovative quality management framework that enhances teacher professionalism and optimizes learning processes in kindergarten settings. This approach is relevant to digital transformation in education and provides a foundation for future empirical research in early childhood quality management systems.
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