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Article . 2025
License: CC BY
Data sources: ZENODO
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Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Inteligencia artificial en la educación superior para promover un aprendizaje personalizado e inclusivo: una revisión sistemática

Authors: Zambrana Copaja, Ricardo; Salinas Montemayor, Alberto Daniel; Macías García, Fernando Antonio; Escobar, Eduardo Ernesto;

Inteligencia artificial en la educación superior para promover un aprendizaje personalizado e inclusivo: una revisión sistemática

Abstract

Resumen La inteligencia artificial (IA) se ha consolidado como un pilar fundamental en la transformación de la educación superior, al impulsar modelos de enseñanza más personalizados, inclusivos y centrados en las necesidades del estudiante. Su integración permite adaptar contenidos educativos, automatizar tutorías y anticipar posibles dificultades académicas, favoreciendo así un acceso más equitativo al conocimiento y atendiendo a la diversidad de estilos de aprendizaje. Además, la IA contribuye a optimizar la gestión académica y curricular, facilitando la superación de barreras relacionadas con la accesibilidad, las diferencias culturales y lingüísticas. No obstante, su implementación enfrenta desafíos importantes, entre los que destacan la brecha tecnológica, la limitada formación del profesorado en competencias digitales, la resistencia institucional y la ausencia de marcos éticos y pedagógicos bien definidos. Estos retos se acentúan en regiones como América Latina, debido a desigualdades estructurales profundas. En este marco, el presente estudio tiene como objetivo analizar las oportunidades, dificultades y perspectivas futuras derivadas de la incorporación de la IA en la educación superior, con especial atención en su potencial para promover un aprendizaje personalizado e inclusivo. Para ello, se seleccionaron 21 artículos que cumplen con rigurosos criterios metodológicos y que abordan aspectos clave del uso educativo de la IA en países como Perú, México, Ecuador y Argentina, garantizando así una revisión representativa y actualizada del estado del arte.

Abstract Artificial intelligence (AI) has established itself as a fundamental pillar in the transformation of higher education, promoting more personalized, inclusive, and student-centered teaching models. Its integration allows for the adaptation of educational content, the automation of tutoring, and the anticipation of potential academic difficulties, thus promoting more equitable access to knowledge and addressing the diversity of learning styles. Furthermore, AI contributes to optimizing academic and curricular management, facilitating the overcoming of barriers related to accessibility and cultural and linguistic differences. However, its implementation faces significant challenges, including the technological gap, limited teacher training in digital skills, institutional resistance, and the absence of well-defined ethical and pedagogical frameworks. These challenges are accentuated in regions such as Latin America due to deep structural inequalities. Within this framework, this study aims to analyze the opportunities, difficulties, and future prospects arising from the incorporation of AI in higher education, with particular attention to its potential to promote personalized and inclusive learning. To this end, 21 articles were selected that met rigorous methodological criteria and addressed key aspects of the educational use of AI in countries such as Peru, Mexico, Ecuador, and Argentina, thus ensuring a representative and up-to-date review of the state of the art.

Keywords

inclusión educativa, inteligencia artificial, aprendizaje personalizado, educational inclusion, personalized learning, artificial intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green