Reform in University Education as a Consequence of Artificial Intelligence: A Systematic Review of the Literature

Abstract

The present study aims to analyze the impact of artificial intelligence (AI) in university education, with the objective of synthesizing its most effective applications, identifying its advantages and limitations, and exploring the ethical, technical, and pedagogical challenges associated with its implementation. The review covers recent research on the nature of AI driven transformations of teaching methods, learning experiences and internal educational policies. Thus, we carried out a systematic analysis of 20 relevant studies, using a methodology based on searching and selecting academic literature in databases such as Scopus, EBSCO, and Web of Science. The data were grouped according to common approaches and then compared, to identify overlaps, debates, and research gaps. The results show that AI has significantly improved the personalization of learning and assessment processes, although challenges related to accessibility, algorithmic biases, and acceptance by educational stakeholders remain. The main conclusions highlight the need for ethical and sustainable approaches to integrate AI in diverse educational contexts, as well as to promote future research that addresses equity and cultural adaptation issues in its use.