The Impact of Artificial Intelligence on the Academic Performance of Undergraduate Engineering Students: A Bibliometric Review (#1748)

Authors

Díaz Desposorio, Félix Napoleón

Cerna Velazco, Nhell Heder

Liza Neciosup, Rafael Ángel

Abstract

This review examines the impact of Artificial Intelligence and Natural Language Processing on the academic performance of undergraduate engineering students. Data were collected from Scopus and Web of Science, analyzed following PRISMA guidelines, and processed using the Bibliometrix package. The review encompasses 100 peer-reviewed articles published between 2000 and 2024. The findings reveal a marked surge in publications after 2020, underscoring the growing integration of AI tools such as machine learning models and ChatGPT into engineering education. Key contributors and influential journals were identified, with significant research outputs originating from China, the United States, Spain and Peru. The thematic analysis indicates a clear shift from traditional educational methods toward data-driven learning strategies, positioning AI, machine learning, and engineering education as central themes in current research. This study offers valuable insights into the evolving role of AI in education, providing an important foundation for future research aimed at enhancing academic performance through technological innovations.