GENERATIVE ARTIFICIAL INTELLIGENCE, LEARNING ANALYTICS AND LEARNING PERSONALIZATION: A REVIEW OF PEDAGOGICAL TRANSFORMATIONS IN HIGHER EDUCATION
TL;DR
Analyzing the main pedagogical transformations associated with Generative AI, Learning Analytics, and learning personalization in higher education concluded that integrating these technologies has transformative potential when guided by ethical principles, qualified pedagogical mediation, and consistent institutional policies.
Abstract
The incorporation of Generative Artificial Intelligence (AI) into higher education has transformed pedagogical practices, particularly through the development of personalized strategies, educational data analysis, and the reorganization of teaching and learning processes. This study aimed to analyze the main pedagogical transformations associated with Generative AI, Learning Analytics, and learning personalization in higher education, highlighting their potential, challenges, and contemporary implications. An integrative literature review was conducted using the ERIC, Latindex, SpringerLink, and Miguilim databases, considering publications from 2021 to 2026 in Portuguese and English. Studies addressing generative technologies, adaptive learning, educational data analysis, and personalized learning pathways were analyzed. The findings indicated potential to expand learning individualization, support content production, facilitate feedback, improve assessment processes, and inform pedagogical decision-making. However, challenges related to academic integrity, privacy, information reliability, equity, AI literacy, and teacher training were also identified. It is concluded that integrating these technologies has transformative potential when guided by ethical principles, qualified pedagogical mediation, and consistent institutional policies.