AI Powered Analytics in Education: A Systematic Review of Predictive Models, Learning Insights, and Policy Implications

Kholis Ali Mahmudi (STAI Ma'arif Magetan)
Muhammad Luthvi Al-Hasyimi (STAI Ma'arif Magetan)

TAZKIA JURNAL
Vol. 3 No. 1 (2026): TAZKIA JURNAL JUNI 2026

Artificial intelligence is changing how student outcomes are predicted, how learning is personalized, and how evidence-based decisions are made in education. This review looks at research from 2018 to 2026 to explore how predictive modeling, teaching insights, and policy frameworks interact. It brings together 142 key articles and policy documents from sources such as Scopus, Web of Science, DOAJ, UNESCO, and the OECD. The review focuses on three main areas: designing predictive algorithms, turning data into practical teaching strategies, and developing policies that support accountability and fairness. The results show ongoing gaps between new technologies and what classrooms need, as well as uneven policy adoption, especially in lower-income areas. The review presents a governance matrix that combines transparency, teacher involvement, and policy alignment. By highlighting knowledge gaps and suggesting a step by step regulatory plan, the review aims to support responsible AI use in education and offer practical advice for policymakers, school leaders, and researchers.