Applications of Generative Artificial Intelligence in Higher Education: A Systematic Review of the Literature

Abstract

Generative Artificial Intelligence (GenAI) has attracted growing attention in higher education due to its potential to transform teaching, learning, assessment, and academic production. This article presents a systematic literature review focused on the use of GenAI in university settings, conducted following Kitchenham's methodological model. A total of 41 studies published between 2020 and 2025 were analyzed across major databases such as Scopus, Web of Science, Springer, and IEEE Xplore. The findings reveal a wide range of applications, including learning personalization, automated feedback, academic writing support, content generation, and optimization of teaching and administrative tasks. However, the review also identifies significant ethical, pedagogical, and epistemological challenges, such as academic integrity, algorithmic bias, and the urgent need to strengthen critical digital literacy among both instructors and students. This review systematizes 26 GenAI tools, 21 educational strategies, and 12 ethical implications, providing a comprehensive foundation for institutional policy development and future research. The study ultimately aims to promote the responsible, ethical, and pedagogically meaningful integration of emerging generative AI technologies within higher education.