Abstract: In the era of digital disruption, madrasas face challenges to accommodate the diversity of students' potential (multiple intelligences) in a precise manner. Conventional curriculum is often one-size-fits-all, which is less than optimal in boosting students' unique talents. The use of Artificial Intelligence (AI) in strategic management offers the opportunity to personalize the curriculum automatically and accurately through big data analysis. This research aims to analyze the formulation, implementation, and evaluation of strategic management that integrates AI technology to create personalized learning paths in Madrasahs. The main focus is on how AI helps decision-makers in adapting materials, learning speed, and teaching methods to students' cognitive profiles and interests without abandoning the basic values of madrasas. This study uses the library research method to collect and analyze data. The initial stage of the research is the identification and selection of the type of literature needed, focusing on primary sources such as recent articles and books that discuss AI in education and adaptive learning. After that, library materials were collected from various credible academic databases and digital libraries. The next process is a critical analysis of the library materials that have been collected with the techniques of synthesis and integration of ideas. This analysis aims to formulate a comprehensive picture of the practices, benefits, and challenges of AI integration in personalization and curriculum design in leading schools. This approach is appropriately used to build a strong theoretical foundation and present a systematic and scientifically documented study. AI-based curriculum personalization has been shown to increase learning effectiveness and student motivation. From the perspective of Islamic education management, this technology serves as a tool to actualize the concept of li-kulli maqaam maqaal (putting things in their place) in the educational process. However, the main challenges lie in ...
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