Examination of Artificial Intelligence Integration and Impact on Higher Education

Abstract

This research investigates utilizing Machine Learning (ML) and Artificial Intelligence (AI) within academic settings. Drawing upon scholarly sources, we explore the strategic deployment of ML algorithms for tasks such as detecting AI-generated content, evaluating students' graduation potential, and enhancing personalized learning experiences. Our methodology encompasses several key stages: gathering and understanding ML, selecting appropriate models, collecting and prepossessing data, model training, evaluation, testing, and comparative analysis. Through rigorous evaluation using diverse datasets, we assess the performance of Decision Trees, Multinomial Naive Bayes, and Neural Network models in accurately classifying text samples. The findings from this study provide valuable insights into the efficacy of ML algorithms in academic contexts and offer practical implications for their implementation.

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