ARTIFICIAL INTELLIGENCE FOR EDUCATIONAL INTELLIGENCE: AN ENSEMBLE LEARNING APPROACH USING XGBOOST, GRADIENT BOOSTING, AND ADABOOS
Rajender Radharam
Abstract
The integration of Artificial Intelligence (AI) in education holds significant promise, primarily by enabling the efficient personalization of learning experiences.Modern technologies are increasingly leveraging AI to create adaptive learning systems that tailor instruction to a student's specific level of development, provide personalized support for linguistically diverse learners, and utilize learning analytics that blend automated data processing with human insight.A key benefit for educational institutions is the automation of repetitive administrative tasks, which frees up educators to focus on providing personalized student support.However, the successful adoption of this technology requires careful consideration of its legal, ethical, and socio-psychological implications.To demonstrate the powerful predictive capabilities of AI, this study evaluated three machine learning regression algorithms-AdaBoost, Gradient Boosting, and XGBoost.In a practical application, the analysis found that while square footage had an almost perfect correlation with house price (0.99), other