Theoretical, methodological and technological foundations for the application of artificial intelligence at the university level
Keywords:
Artificial intelligence, higher education, adaptive learning, learning analytics, technological innovationAbstract
The application of Artificial Intelligence (AI) in the university environment has become a field of growing interest, given its potential to transform educational, research, and administrative processes. This article explores the theoretical, methodological, and technological foundations that support the integration of AI in higher education, analyzing its role in enhancing teaching and learning, educational personalization, academic analytics, and the optimization of university management. From a theoretical perspective, the contributions of constructivist, connectivist, and adaptive learning approaches are reviewed, in which AI acts as a mediator between knowledge and students. Methodologically, the design and implementation strategies of intelligent systems, machine learning models, and the use of adaptive learning platforms are examined. Technologically, tools based on machine learning algorithms, natural language processing, academic chatbots, and recommendation systems are highlighted, as they expand the possibilities of support for both teachers and students. The research shows that AI is not only a support resource but also a catalyst for educational innovation, capable of fostering new digital, analytical, and critical competencies among university students. Finally, ethical, equity, and technological governance challenges are discussed, which must be considered to ensure a responsible and sustainable application of AI in higher education.
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