Advancing University Education: A Review of Generative AI and Machine Learning in Learning and Innovation

Abstract

The implementation of generative artificial intelligence (AI) and Machine Learning (ML) is revolutionizing higher education. These technologies enable more personalized teaching, optimize academic management processes, and promote innovative pedagogical methodologies. This study aims to systematically review the state of the art of AI and ML in the university context between 2015 and 2024, based on 50 articles selected from the Scopus database. A mixed methodology was used that combined bibliometric and content analysis to identify trends, technological tools, and their influence on academic performance. The findings highlight ChatGPT, Microsoft Insights, and Turnitin as the most used generative artificial intelligence tools, contributing to academic writing, plagiarism detection, and educational data analysis. Furthermore, evidence shows that the application of machine learning models, such as Random Forest and Support Vector Machine, favors the prediction of student performance and the detection of at-risk students. Despite significant benefits, challenges persist related to ethics, data privacy, and technology dependence. In conclusion, the integration of AI and machine learning drives efficiency in teaching processes and promotes educational innovation; however, it is essential to develop regulatory frameworks and adaptive pedagogical approaches to ensure its sustainable implementation.