Artificial intelligence in academic performance optimization: A systematic literature review

Abstract

This paper is a systematic literature review article that aims to examine how Artificial Intelligence (AI) could help enhance the academic performance of computer science students pursuing undergraduate degrees. The analysis was conducted on five primary areas, namely, academic performance, personalized learning, assessment and feedback, dropout-risk prediction, and ethical considerations, basing it on 36 peer-reviewed articles from 2020 to 2025. The applications based on AI, in particular, the intelligent tutoring systems and predictive analytics, have been proven to have a substantial positive impact on the results of the student. The AI-driven personalized learning systems perform even higher and achieve over 97 percent of classification accuracy due to the fact that it is capable of tailoring learning content to the cognitive attributes of learners and preferred learning styles. Ensemble models and deep neural networks have been found to be extremely effective in forecasting dropout risks and ranging between 71 and 94 percent levels of accuracy have enabled timely intervention of at-risk students. Despite these self-evident advantages, the review mentions the current ethical issues of privacy, bias, and unequal opportunities, and the necessity to make the development of AI transparent, responsible, and inclusive.

Keywords

References

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Copyright (c) 2026 Ismail Olaniyi Muraina, Moses Adeolu Agoi, Solomon Onen Abam, Bashir Oyeniran Ayinde, Wasiu Olatunde Oladapo