Impact of AI on Student Performance: A Review of Predictive Models, Adaptive Systems, and Implementation Challenges

Abstract

This study provides a systematic literature review (SLR) complemented by a bibliometric analysis to examine the main trends and challenges in the integration of Artificial Intelligence (AI) in classroom learning and student performance between 2020 and 2025. Using the PRISMA protocol, 60 peer-reviewed articles were selected from Scopus, Web of Science, and PubMed databases. The results identify five key research lines: academic performance prediction, intelligent tutoring systems, psychological impacts of AI, automated assessment, and innovative applications such as gamification and robotics. The analysis highlights a strong focus on deep learning models, adaptive learning, and learning analytics, although a gap remains in pedagogical integration. Furthermore, student perceptions and ethical concerns emerge as critical factors influencing adoption. This review contributes to the academic field by offering a comprehensive synthesis of current literature and proposing a balanced integration of technological and pedagogical approaches to promote ethical, effective, and sustainable AI use in education.