This study aims to examine the integration of Generative AI in higher education through the perspective of the SAMR framework, which consists of Substitution, Augmentation, Modification, and Redefinition. The study used a narrative literature review method by analyzing 30 relevant academic publications published between 2023 and 2025. The findings reveal that the implementation of GenAI in higher education is still predominantly situated at the substitution and augmentation levels, with primary functions focused on improving efficiency, automation, and academic assistance. In contrast, transformative applications categorized under modification and redefinition remain relatively limited and are still in the early stages of development. This study concludes that although Generative AI possesses substantial potential to transform higher education practices, its current implementation has not yet reached an optimal transformative stage. Therefore, future educational practices should emphasize the development of more transformative implementation strategies that move beyond efficiency-oriented utilization toward fostering pedagogical innovation and meaningful learning transformation.
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