A Comprehensive Review of AI Techniques for Forecasting Student Performance and Retention across Multilingual Learning Environments

Abstract

The paper provides a detailed examination of Artificial Intelligence techniques which predict student educational success and academic retention within multilingual educational settings. Quality prediction models are essential to support educational opportunities for multilingual learners because educational institutions are now dealing with increased linguistic diversity. The paper discusses the efficiency of traditional Machine Learning approaches followed by sophisticated Deep Learning models and Ensemble techniques for multilingual purposes. The research assesses which combinations of demographics data alongside grade information and language proficiency preserve the best predictive effectiveness. Future discoveries have prompted this article to highlight how prediction models have progressed towards more complex and extensive approaches. The research presents an evaluation of AI method strengths and disadvantages which indicates prospective applications for future AI deployment and research development. This research concludes by studying upcoming opportunities as well as challenges that emerge when using AI for multilingual education analytics which prepares the ground for adaptive multilingual learning solutions.