GENERATIVE ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: AN INTERDISCIPLINARY QUALITATIVE ANALYSIS OF TEACHING PRACTICES AND STUDENT LEARNING

Abstract

This article analyzes the incorporation of Generative Artificial Intelligence in higher education based on teaching experiences in Medicine, Biomedicine, and Computing programs. It is an experience report with a qualitative and descriptive-analytical approach, grounded in reflective records from three university professors, pedagogical observations, and assessment activities conducted between 2022 and 2025. Thematic analysis identified four main axes: student immediacy, uncritical use of Artificial Intelligence, outsourcing of cognitive tasks, and pedagogical potential of guided tool use. The results indicate that Generative Artificial Intelligence may support personalized learning, assist teaching planning, and expand learning strategies. However, when used without mediation, it may compromise academic authorship, student autonomy, critical thinking, and the gradual construction of knowledge. The study concludes that the presence of these tools in higher education requires intentional pedagogical practices, Artificial Intelligence literacy, clear institutional policies, and assessment strategies focused on the learning process rather than solely on the final product.