Transforming Islamic Higher Education through Artificial Intelligence: A Conceptual Framework for Educational Management and Learning Innovation
DOI:
https://doi.org/10.58230/socratika.v3i2.684Keywords:
artificial intelligence, Islamic higher education, institutional transformation, educational management, learning innovation, conceptual frameworkAbstract
This study aimed to develop a conceptual framework explaining how artificial intelligence (AI) drives institutional transformation in Islamic higher education through the integration of educational management, learning innovation, research development, and Islamic ethical governance.
The study employed a conceptual research design using a Systematic Literature Review (SLR), thematic synthesis, and conceptual synthesis. Relevant literature on artificial intelligence, higher education transformation, educational management, university governance, and Islamic higher education was reviewed following PRISMA 2020 principles. Thematic analysis involved open coding, axial coding, and selective coding, while Dynamic Capabilities Theory was used to interpret the relationships among the identified dimensions.
The synthesis identified six interconnected dimensions of AI-driven institutional transformation: strategic leadership, institutional readiness, intelligent governance, learning innovation, research ecosystem transformation, and Islamic ethical governance. These dimensions were integrated into an AI-Driven Institutional Transformation Framework for Islamic Higher Education, positioning AI as a catalyst that reconfigures governance, learning, research, and organizational capacity while remaining guided by Islamic ethical principles.
AI-driven transformation in Islamic higher education requires more than technological adoption. Sustainable transformation depends on the integration of technological capability, institutional readiness, strategic leadership, academic innovation, and Islamic ethical governance. The proposed framework provides a theoretical foundation for future empirical testing and practical guidance for responsible AI-based institutional transformation.
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Copyright (c) 2026 Umi Kulsum, Nurwinda Apriyani, Dewi Yanti, Sri Ekowati, Riskun Iqbal
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