This review delves into the transformative impact of integrating data analytics and artificial intelligence (AI) in education, ushering in adaptive, student-centric, and data-informed learning environments. Employing a systematic review, the study informs educators, policymakers, and stakeholders about the potentials, challenges, and ethical considerations associated with AI and data analytics in education. The results highlight diverse applications of AI tools, including sentiment analysis and predicting academic achievements, stressing the need for customized methods aligned with specific educational goals. Addressing challenges in education, such as complex visualization in Computer-Supported Collaborative Learning dashboards, is crucial for effective AI tool utilization, with practical implications emphasizing hands-on training in content creation and problem-solving for teacher education. The review concludes by summarizing key findings, emphasizing AIs potential to enhance teaching and learning, and highlighting the ongoing evolution of education through AI and data analytics integration, contributing valuable insights for future research in this rapidly evolving field.

Data Analytics

Artificial Intelligence

Data Mining

Education

AI-integration