Generative AI in Personalized Learning: A Systematic Review of Implementation in Indonesia - Mardeli Mardeli; Juwita Puspita Sari; Kris Setyaningsih; Muhamad Fauzi; Emilia Fitri; Novia Ballianie (LP. Ma'arif Janggan Magetan, 2025)
Indonesia's digital education transformation has experienced significant acceleration post-COVID-19 pandemic, with generative AI emerging as a disruptive technology offering revolutionary potential for personalized learning. However, significant knowledge gaps remain regarding how generative AI is implemented within Indonesia's unique educational context characterized by distinctive socio-cultural conditions, technological infrastructure, and educational policies. This study aims to map implementation patterns of generative AI in personalized learning across various educational levels in Indonesia and identify encountered challenges. Employing a systematic literature review methodology following PRISMA 2020 guidelines, the research analyzed 28 studies published between 2020-2024 from international and Indonesian local databases. Findings reveal concerning implementation distribution disparities with concentration in higher education and dominance of global commercial platforms like ChatGPT, indicating dependence on proprietary systems raising data sovereignty concerns. Effective implementations adopt hybrid human-AI orchestration models with tiered personalization reflecting Indonesian cultural values. Major challenges include three-level digital divides, low teacher competency in AI literacy, rigid curriculum structures, and absence of adequate data governance frameworks. This research contributes to developing contextual generative AI implementation frameworks and fills literature gaps on educational AI in Indonesia, though longitudinal research and explicit equity interventions remain necessary to ensure just and sustainable implementation.