aspg-27-4353-1792222719 20261017073839 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Artificial Intelligence and Education Technology IJAIET 2835-2432 2025 4 2 Nahla Moussa Associate Professor, Amity University Dubai, United Arab Emirates Low Hon Loon Alfred Senior Manager (Instructional Design), Higher Colleges of Technology, Abu Dhabi, United Arab Emirates Student-success analytics has moved from experimental prediction toward an institutional capability for reducing attrition, allocating support resources, and improving digital learning governance. This paper develops a business oriented early-warning model for education technology environments in which predictive performance, interpretability, intervention priority, and governance are treated as joint design requirements. The study uses a public student-success dataset from a higher education institution and evaluates decisive outcome prediction for dropout and graduation, while preserving a wider discussion of the enrolled group as an unresolved operational state. The proposed model combines a transparent predictive layer, a risk-to-action prioritization layer, and a governance layer that restricts how predictions are translated into student support decisions. The results show that a parsimonious logistic specification can provide competitive performance compared with more complex tree based models, while producing clearer accountability for academic advising and digital student-success units. The discussion argues that student-success technology should not be judged by accuracy alone, but by whether the analytics pipeline produces timely, explainable, privacy-aware, and operationally usable support signals. 2025 11 18 4353 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAIET.040202 https://americaspg.com/journal/27/article/4353 https://americaspg.com/storage/articles/manuscripts/01M387ERWN28YDM7MNHHB711B9.pdf