Pemanfaatan Artificial Intelligence dalam Sistem Informasi Manajemen Pendidikan untuk Mendukung Pengambilan Keputusan - Taufik Durrahman; Saidatul Maghfiroh; Mohammad Ilham; Mochammad Sofwil Anam; Muhammad Zyebi Zanubie; Yeni Tri Nur Rahmawati (CV. Lentera Literasi Indonesia, 2026)
The development of Artificial Intelligence (AI) has brought significant transformations to educational management, particularly through its integration into Education Management Information Systems (EMIS). This study aims to analyze the utilization of AI within EMIS as a decision-making support tool in educational institutions, while identifying the benefits, challenges, and implementation opportunities. Employing a qualitative approach, this study uses the Systematic Literature Review (SLR) method adhering to the PRISMA 2020 guidelines. Research data were gathered from national and international scientific articles published between 2021 and 2026 across databases including Google Scholar, Scopus, ScienceDirect, SpringerLink, IEEE Xplore, and Garuda/SINTA. The data were analyzed using content analysis techniques through a rigorous process of identification, screening, evaluation, and synthesis of literature meeting the inclusion criteria. The findings indicate that AI implementation enhances EMIS effectiveness through administrative process automation, improved data processing accuracy, predictive analytics, and the provision of data-driven recommendations, thereby supporting faster, more objective, and accurate decision-making. Beyond improving operational efficiency and the quality of educational governance, AI implementation still faces various challenges, such as technological infrastructure limitations, human resource competencies, data security, and ethical concerns regarding AI usage. Therefore, successful AI implementation requires policy support, enhanced digital literacy, strengthened data governance, and the development of adequate technological infrastructure. This study is expected to serve as a reference for educational institution managers and researchers in developing an adaptive EMIS that aligns with digital transformation and focuses on data-driven decision-making.