University Education 5.0: Intelligent Models for Optimizing Learning and Academic Management

Abstract

The study examined the impact of artificial intelligence–based intelligent models on learning and academic management in higher education institutions, using a descriptive–correlational quantitative approach applied to a sample of 182 participants, including students, faculty, and administrative staff. Data were collected through a structured questionnaire, institutional analytics, and documentary records. The findings revealed significant improvements in academic performance, including increased approval rates, reduced dropout, and greater learning personalization. Administrative processes also showed marked optimization, with reductions in processing times, automation of operational tasks, and enhanced student support. Learning analytics techniques identified interaction patterns associated with performance gains and increased teacher feedback. Overall satisfaction reached high levels, confirming positive acceptance of intelligent models within the university environment. These results indicate that AI strengthened institutional efficiency and improved the educational experience, although challenges related to technological infrastructure and digital literacy persist.