Evaluation of Experience in Higher Education, innovating with artificial intelligence in the implementation of predictive models

Abstract

This study explores the impact of artificial intelligence (AI) and predictive models in higher education, focusing on the personalization of learning, student retention, and academic success prediction. In a constantly evolving educational environment, it is crucial for universities to implement innovative curricula that prepare students for the challenges of the current job market. The study's methodology is based on a descriptive approach, using a semi-structured survey administered to 250 higher education students. The survey included questions about academic performance, academic support, guidance, skills, and interests. The collected data were analyzed using regression techniques with SPSS software, allowing for the identification of relationships between predictor variables and student satisfaction. The analysis results revealed that, although the predictor variables explain a small proportion of the variance in student satisfaction, the identified patterns provide valuable insights for improving educational offerings. Personalization of learning and optimization of student retention emerge as key areas where AI can have a significant impact. In conclusion, while the implementation of AI predictive models presents challenges related to accuracy and ethics, these models have the potential to transform the educational experience in higher education. The study suggests that, with proper integration and alignment with educational objectives, AI can enhance the quality of education and meet the individual needs of students, contributing to a more efficient and personalized educational experience