La inteligencia artificial en la educación: vacíos, tendencias y desafíos éticos. Una revisión de literatura (2019–2024)

This article presents a systematic review of the scientific literature on the use of artificial intelligence (AI) in education, aiming to identify trends, conceptual gaps, and emerging challenges in its pedagogical integration. The search was conducted in indexed databases such as Scopus, Web of Science, and ERIC, retrieving 186 publications between 2019 and 2024. After applying PRISMA criteria, 47 empirical and theoretical studies were selected, all addressing the implementation of AI across various educational levels. Although academic production on AI in education has grown significantly, most studies originate from technical fields, with limited critical articulation from pedagogical and social science perspectives. This study addresses that gap by providing a synthesis that combines methodological rigor with an integrated educational perspective, aiming to understand the phenomenon beyond its functional applications. Findings were organized into six thematic axes: (1) personalized learning, highlighting the use of adaptive systems; (2) ethics and digital equity, focusing on privacy and algorithmic bias; (3) generative artificial intelligence and its didactic implications; (4) teacher training and student perceptions; (5) predictive models and educational data mining; and (6) applications across different educational levels. The results indicate a positive impact of AI on personalized learning support and process optimization. However, persistent challenges remain, such as teacher training gaps, unequal access to technology, and the need for regulatory frameworks. It is concluded that the effective integration of AI in education requires ethical and interdisciplinary approaches, alongside public policies that promote pedagogical innovation, inclusion, and long-term sustainability.