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

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) powe...

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Bibliographic Details
Published in:Information (Basel) Vol. 16; no. 7; p. 540
Main Authors: Pérez-Jorge, David, González-Afonso, Miriam Catalina, Santos-Álvarez, Anthea Gara, Plasencia-Carballo, Zeus, Perdomo-López, Carmen de los Ángeles
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Academic achievement
Adaptation
Algorithms
Application programming interface
application programming interfaces (APIs)
Applications programming
Artificial intelligence
artificial intelligence in education
automated feedback
Automation
Chatbots
Collaboration
Computational linguistics
Customization
Education
educational data analytics
Educational evaluation
Educational technology
Ethics
Feedback
Generative artificial intelligence
Information management
Interoperability
Knowledge management
Language processing
Learning
Learning management systems
Machine learning
Natural language interfaces
Neural networks
Pedagogy
Personalized learning
Real time
Systematic review
Teaching
Tutoring
ISSN:2078-2489, 2078-2489
Online Access:Get full text
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Summary: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.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info16070540