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Cited by 12Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningEducational Technology and E-Learning

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Mapping the intelligent classroom: Examining the emergence of personalized learning solutions in the digital age

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Lagos-Castillo, Alez, Chiappe, Andrés, Ramirez-Montoya, María-Soledad, Becerra Rodríguez, Diego Fernando·2025·Contemporary Educational Technology
DOI 10.30935/cedtech/15617
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Abstract

It may seem that learning platforms and systems are a tired topic for the academic community; however, with the recent advancements in artificial intelligence, they have become relevant to both current and future educational discourse. This systematic literature review explored platforms and software supporting personalized learning processes in the digital age. The review methodology followed PRISMA guidelines, searching Scopus and Web of Science databases. Results identified three main categories: artificial intelligence, platforms/software, and learning systems. Key findings indicate artificial intelligence plays a pivotal role in adaptive, personalized environments by offering individualized content, assessments, and recommendations. Online platforms integrate into blended environments to facilitate personalized learning, retention, and engagement. Learning systems promote student-centered models, highlight hybrid environments’ potential, and apply game elements for motivation. Practical implications include leveraging hybrid models, emphasizing human connections, analyzing student data, and teacher training. Future research directions involve comparative studies, motivational principles, predictive analytics, adaptive technologies, teacher professional development, cost-benefit analyses, ethical frameworks, and diverse learner impacts. Overall, the dynamic interplay between artificial intelligence, learning platforms, and learning systems offers a mosaic of opportunities for the evolution of personalized learning, emphasizing the importance of continuous exploration and refinement in this ever-evolving educational landscape.

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  • Lagos-Castillo (2025) analizó tecnologías explicables (XAI) y el Internet of Behavior, subrayando su potencial para mejorar la transparencia y confianza en sistemas inteligentes.

    “Lagos-Castillo (2025) complementó estos hallazgos al analizar tecnologías explicables (XAI) y el Internet of Behavior, subrayando su potencial para mejorar la transparencia y confianza en sistemas inteligentes [7].”

    From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [7]

    The Artificial Intelligence section (p. 7) discusses XAI and the Internet of Behavior while reporting a study by Embarak (2022). It describes their potential for personalized and adaptive education, but does not say they improve transparency or trust in intelligent systems. (LLM, conf=0.86)

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Metadata

Authors
Lagos-Castillo, A., Chiappe, A., Ramirez-Montoya, M.-S., & Becerra Rodríguez, D. F.
Affiliations
  • Universidad de La Sabana (CO)
  • Tecnologico de Monterrey, Monterrey, MEXICO
  • Universidad de La Sabana, Chía, Cundinamarca, COLOMBIA
ORCID
Venue
Contemporary Educational Technology
Publisher
Bastas Publications
Published
2025-01-06
Volume
17
Issue
1
Pages
ep543
ISSN
1309-517X (ISSN-L)
DOI
10.30935/cedtech/15617
OpenAlex
W4404542979
Cited by
12
References
6
Citations by year
2026: 3 · 2025: 9
FWCI
4.53
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Contemporary Educational Technology (publishedVersion, cc-by)
  • Intellectum (Universidad de La Sabana) (submittedVersion, cc-by)
Funders
Universidad de La Sabana
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
Keywords
personalized learning, artificial intelligence in education, adaptive learning systems, learning platforms, virtual learning environments, blended learning, game-based learning, student motivation, student-centered learning, teacher professional development, educational technology ethics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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