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

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Udgivet i:Information vol. 16, no. 7 (2025), p. 540-571
Hovedforfatter: Pérez-Jorge, David
Andre forfattere: González-Afonso, Miriam Catalina, Santos-Álvarez, Anthea Gara, Plasencia-Carballo Zeus, Perdomo-López Carmen de los Ángeles
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MDPI AG
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100 1 |a Pérez-Jorge, David  |u Department of Didactics and Educational Research, Faculty of Education, University of La Laguna, 38204 San Cristóbal de La Laguna, Spain; dpjorge@ull.edu.es (D.P.-J.); mcglez@ull.edu.es (M.C.G.-A.); asantosa@ull.edu.es (A.G.S.-Á.) 
245 1 |a The Impact of AI-Driven Application Programming Interfaces (APIs) on Educational Information Management 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Teaching 
653 |a Pedagogy 
653 |a Interoperability 
653 |a Collaboration 
653 |a Feedback 
653 |a Adaptation 
653 |a Application programming interface 
653 |a Educational technology 
653 |a Learning management systems 
653 |a Automation 
653 |a Ethics 
653 |a Generative artificial intelligence 
653 |a Chatbots 
653 |a Machine learning 
653 |a Tutoring 
653 |a Learning 
653 |a Academic achievement 
653 |a Education 
653 |a Personalized learning 
653 |a Neural networks 
653 |a Algorithms 
653 |a Artificial intelligence 
653 |a Customization 
653 |a Real time 
653 |a Information management 
653 |a Systematic review 
700 1 |a González-Afonso, Miriam Catalina  |u Department of Didactics and Educational Research, Faculty of Education, University of La Laguna, 38204 San Cristóbal de La Laguna, Spain; dpjorge@ull.edu.es (D.P.-J.); mcglez@ull.edu.es (M.C.G.-A.); asantosa@ull.edu.es (A.G.S.-Á.) 
700 1 |a Santos-Álvarez, Anthea Gara  |u Department of Didactics and Educational Research, Faculty of Education, University of La Laguna, 38204 San Cristóbal de La Laguna, Spain; dpjorge@ull.edu.es (D.P.-J.); mcglez@ull.edu.es (M.C.G.-A.); asantosa@ull.edu.es (A.G.S.-Á.) 
700 1 |a Plasencia-Carballo Zeus  |u Department of Specific Didactics, Faculty of Education, University of La Laguna, 38204 San Cristóbal de La Laguna, Spain; zplasenc@ull.edu.es 
700 1 |a Perdomo-López Carmen de los Ángeles  |u Department of Specific Didactics, Faculty of Education, University of La Laguna, 38204 San Cristóbal de La Laguna, Spain; zplasenc@ull.edu.es 
773 0 |t Information  |g vol. 16, no. 7 (2025), p. 540-571 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
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