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Integrating Automation and Artificial Intelligence into Educational Practice
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Integrating Automation and Artificial Intelligence into Educational Practice
Journal
EAI/Springer Innovations in Communication and Computing
Emerging Technologies in Applied Engineering and Education
ISSN
2522-8595
2522-8609
Date Issued
2026
Author(s)
Adrián Vargas-M.
Esteban Fabricio Gonzabay-Jiménez
Homero J. Velasteguí
Castro Chuquiana , Ricardo David
Facultad de Ingenierías
Type
Resource Types::text::conference output::conference proceedings::conference paper
DOI
10.1007/978-3-032-10310-9_20
URL
https://cris.indoamerica.edu.ec/handle/123456789/9981
Abstract
Digital transformation has introduced technologies such as artificial intelligence (AI), automation, and learning analytics into education, generating more personalized, efficient, and data-driven learning environments. These innovations support intelligent tutoring systems, automated feedback, performance prediction, and administrative decision making. However, their implementation also presents ethical, social, and technical risks that remain insufficiently understood and regulated. This study presents a narrative review of scientific literature published between 2021 and 2025, aiming to analyze the uses, benefits, and challenges of AI in educational contexts, with a particular focus on learning analytics. Articles were selected from major databases such as Scopus, IEEE, SpringerLink, and Web of Science, prioritizing studies that offered practical applications and a critical lens. The review highlights promising developments, including systems that enhance personalized learning trajectories, reduce grading time significantly, and predict student dropout risk with high accuracy. Nonetheless, it also exposes significant concerns, such as the reinforcement of algorithmic biases, excessive surveillance, and the exacerbation of digital inequalities—especially in Latin America, where a substantial portion of students still lack stable access to quality Internet. These findings underscore the dual nature of AI in education: while it has the potential to improve quality and equity, its effectiveness depends on ethical governance, equitable infrastructure, and meaningful teacher training. The review concludes that AI should be used to complement, rather than replace educators’ roles, placing emphasis on learner needs, human interaction, and contextualized pedagogy. Achieving a more human-centered and inclusive education will require a deliberate balance between technological innovation and professional educational judgment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Subjects
Artificial intelligen...
Educational automatio...
Learning analytics
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Acquisition Date
Apr 15, 2026
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