(untitled) (المجلة الدولية للعلوم المالية والإدارية والاقتصادية, 2026)
Artificial intelligence is increasingly being employed to improve the measurement and control of the quality of education, especially in institutions that have limited resources. The current research offers innovative technology that integrates machine learning and deep learning into a Production and Operations Management strategy. Two publicly available Kaggle datasets were examined which deal with various variables of student performance and engagement. One Education Quality Index (EQI) was developed to combine the academic outcomes, student attendance, and participation in a single measurable score. A number of predictive models were done to ascertain which approach gives the most credible understanding. The deep learning models were significantly more effective than the traditional methods and the most successful results were achieved with a stacked model that combined the best learners and had an overall accuracy of 0.995. More to the point, the model was found to be stable in a variety of evaluation measures and was able to capture variation that more basic algorithms were typically less sensitive to as the model dealt with the multidimensional and multimodal nature of educational data. The key contribution of this research is that the quality of education could be taken as a measurable, and thus, an enable outcome, as opposed to an abstract one. The proposed solution offers an effective, proven tool of early trend identification, and enables planning and informing evidence-based actions, both classroom- and institution-level.