AI-enabled predictive analytics in education: Enhancing student success and retention through intelligent tutoring systems

The combination of “artificial intelligence (AI)” and predictive analytics (PA) has altered educational surroundings by facilitating modified instruction, early interference, and enhanced student retention. This conceptual study examines the part of AI-driven PA in intelligent tutoring systems to improve student achievement. By synthesizing current literature, the research discovers the application of main AI techniques with “machine learning, deep learning, and natural language processing”, in predicting student outcomes and offering adaptive knowledge pathways. The study highlights numerous profits of AI-enabled PA, like real-time response, early recognition of scholars at risk of dropping out, and the formation of personalized instructional policies. These developments can foster modified learning knowledges, serving students achieve their latent. Moreover, the research also highlights the significance of ethical concerns, with data privacy problems, algorithmic partiality, and the digital division that may hinder reasonable contact with AI-driven learning apparatuses. The research concludes by providing recommendations for AI developers, policymakers, and educators to enhance the execution of AI in education. These references highlight the significance of confirming transparency, inclusivity, and fairness in AI applications, as well as sustaining a balance between technical innovation and ethical considerations in educational backgrounds.