Abstract
This study is targeted to use AI (artificial intelligence) to predict the English proficiency test scores (CET-4) of students in private universities. The study will train and test multiple machine learning models using a dataset of students’ demographic information, English language background, and test scores. These models will use different algorithms such as linear regression, decision trees, and neural networks to identify patterns and relationships in the data and make accurate predictions. The study aims to develop a reliable and effective predictive tool for educators to assess students’ language proficiency levels and provide targeted teaching strategies. Comprehensive evaluation of students’ overall quality will play a crucial role in the prediction model. Factors such as educational resources, teaching methods, student learning habits, and learning atmosphere in each college will affect the accuracy of the prediction. The full-coverage classification prediction of student performance using modern scientific and technological strategies holds great research significance for private universities’ educational tasks and targeted teaching plans.
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