A Review of Virtual Tutoring Systems and Student Performance Analysis Using GPT-3

Abstract

In contemporary education, accurately predicting student performance and delivering prompt feedback is paramount for fostering a comprehensive grasp of academic progress. Consequently, educators must adapt their teaching methodologies to optimize learning outcomes. To tackle this challenge, researchers have proposed and implemented diverse alternative and advanced strategies. This study conducts a systematic review of prior research endeavors centered on virtual tutoring and learning environments, aiming to pinpoint significant contributions in educational systems. Emphasis lies on the utilization of machine learning and deep learning models, along with the datasets utilized. Through this exploration, the study illuminates associated hurdles and proposes potential remedies for implementing virtual tutors and performance evaluation. Moreover, it proposes a solution for efficiently managing the abundant data in e-learning platforms. By synthesizing findings from multiple studies, this research enriches the existing knowledge in education systems, offering valuable insights for educators and researchers. The study's outcomes hold promise for enhancing virtual tutoring and learning environments, ultimately enriching students' educational journey and fostering academic advancement.