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The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management

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Publicat a:Information vol. 16, no. 7 (2025), p. 540-571
Autor principal: Pérez-Jorge, David
Altres autors: González-Afonso, Miriam Catalina, Santos-Álvarez, Anthea Gara, Plasencia-Carballo Zeus, Perdomo-López Carmen de los Ángeles
Publicat:
MDPI AG
Matèries:
Teaching
Pedagogy
Interoperability
Collaboration
Feedback
Adaptation
Application programming interface
Educational technology
Learning management systems
Automation
Ethics
Generative artificial intelligence
Chatbots
Machine learning
Tutoring
Learning
Academic achievement
Education
Personalized learning
Neural networks
Algorithms
Artificial intelligence
Customization
Real time
Information management
Systematic review
Accés en línia:Citation/Abstract
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Resum:In today’s digitalized educational landscape, the intelligent use of information is essential for personalizing learning, improving assessment accuracy, and supporting data-driven pedagogical decisions. This systematic review examines the integration of Application Programming Interfaces (APIs) powered by Artificial Intelligence (AI) to enhance educational information management and learning processes. A total of 27 peer-reviewed studies published between 2013 and 2025 were analyzed. First, a general description of the selected works was provided, followed by a breakdown by dimensions in order to identify recurring patterns, stated interests and gaps in the current scientific literature on the use of AI-driven APIs in Education. The findings highlight five main benefits: data interoperability, personalized learning, automated feedback, real-time student monitoring, and predictive performance analytics. All studies addressed personalization, 74.1% focused on platform integration, and 37% examined automated feedback. Reported outcomes include improvements in engagement (63%), comprehension (55.6%), and academic achievement (48.1%). However, the review also identifies concerns about privacy, algorithmic bias, and limited methodological rigor in existing research. The study concludes with a conceptual model that synthesizes these findings from pedagogical, technological, and ethical perspectives, providing guidance for more adaptive, inclusive, and responsible uses of AI in education.
ISSN:2078-2489
DOI:10.3390/info16070540
Font:Advanced Technologies & Aerospace Database