AI-Driven Predictive Analytics in Education

Artificial intelligence (AI)-driven predictive analytics is transforming educational practice by using data to forecast student outcomes and inform interventions in both K–12 and higher education. This paper provides a comprehensive literature-based analysis of how predictive models are employed to identify at-risk students early, personalize learning pathways, and improve institutional decision-making. The review synthesizes theoretical insights from learning analytics and empirical findings from case studies and systematic reviews. Current evidence highlights significant potential benefits – such as improved student retention and tailored instruction – alongside pressing ethical, legal, and practical challenges. Key issues include algorithmic bias and fairness, the “black box” lack of explainability in AI models, and student data privacy concerns under laws like FERPA. A literature-based methodology is described, and findings are discussed in depth. The discussion examines how predictive analytics systems have been implemented (often with notable success in improving graduation rates) and how unresolved challenges (e.g., bias propagation or stakeholder mistrust) may undermine these gains. Finally, implications and recommendations for practice and policy are explored, emphasizing the need for transparent, equitable, and responsible use of AI in education. The paper’s formal academic tone and APA-structured presentation make it suitable for dissemination at a national AI in Education conference.