<![CDATA[The digital transformation of higher education has driven the need for a more adaptive curriculum approach that is responsive to the diverse characteristics of students. Instructional differentiation practices that have been implemented manually and partially have been considered insufficient to address the complexity of heterogeneity in students’ abilities, learning styles, and levels of self-regulation at the institutional scale. This study aimed to formulate and analyze an Adaptive Curriculum Model Based on Self-Directed Learning Algorithms to optimize instructional differentiation in higher education. The study employed a descriptive qualitative approach using a Systematic Literature Review design of scientific publications from 2017 - 2024 obtained from reputable databases. Data were analyzed through content analysis and thematic synthesis to identify conceptual patterns, key components, and empirical trends. The findings indicated that the integration of self directed learning algorithms based on learning data enabled the mapping of student learning profiles, the design of adaptive learning pathways, and the systematic strengthening of self-directed learning. This study produced a conceptual model of an adaptive curriculum integrating learning analytics, digital instructional differentiation, and continuous evaluation within a coherent framework. The contribution of this research lies in reinforcing the perspective of curriculum as a data-driven adaptive system and providing an implementative framework for higher education curriculum development in the era of digital transformation.]]>
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