Monitoring and Academic Accompaniment of University Students Using Emerging Technologies

Abstract

In the current context, where academic competitiveness is high, it is essential to provide academic support to improve the experience and achievements of students before they opt to drop out due to economic, social, and institutional factors. The main objective of the research is to understand how academic support and monitoring programs are implemented, identify different support styles, and the factors influencing their effectiveness. The methodology used was a systematic review of indexed articles in databases such as Scopus, DOAJ, Scielo, Semantic Scholar, and ProQuest, following PRISMA guidelines. The most important results highlight that the implementation of technologies such as artificial intelligence, smart monitors, and machine learning algorithms in academic monitoring programs improves student retention and success. However, challenges are identified, such as the adaptation of students and teachers to new technologies and the need for quality design and implementation to ensure the effectiveness of support. Additionally, the importance of predictive factors such as initial academic performance and motivation is emphasized to identify at-risk students and provide effective interventions.