SYSTEMATIC REVIEW article
Front. Educ.
Sec. Digital Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1672901
This article is part of the Research TopicReimagining Education to Improve Metacognitive and Socioemotional Skills for the 21st CenturyView all articles
AI-powered Learning Analytics for Metacognitive and Socioemotional Development: A Systematic Review
Provisionally accepted
- 1Universidad de Barranquilla, Barranquilla, Colombia
- 2Universidad de La Sabana, Chía, Colombia
- 3Universitat de Barcelona, Barcelona, Spain
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This systematic review explores how AI-powered Learning Analytics (LA) contribute to the development of metacognitive and socioemotional competencies in educational settings. Following the PRISMA guidelines, a total of 161 peer-reviewed articles published between 2013 and 2023 were retrieved from the Scopus database and analyzed. The findings reveal a predominant focus on predictive (46%) and prescriptive (28%) analytics, while descriptive (16%) and social-affective (10%) approaches remain significantly underrepresented. This imbalance raises critical concerns regarding the extent to which current LA implementations support higher-order competencies such as self-regulation, reflection, emotional awareness, and collaborative learning. The study identifies four major categories of LA—descriptive, predictive, prescriptive, and social-affective—and examines their pedagogical implications considering learner-centered principles. Special attention is given to the potential of LA to scaffold metacognitive strategies and foster socioemotional growth, particularly when designed with transparency, learner agency, and emotional sensitivity. Ultimately, the review advocates for a more balanced and human-centered research agenda, calling for the redefinition of educational quality through the integration of holistic learner development in AI-enhanced learning environments.
Keywords: Learning Analytics1, Artificial Intelligence2, Educational Technology3, Data DrivenLearning4, Personalized Education5
Received: 25 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Pacheco, Boude Figueredo, A. and Fontán de Bedout. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chiappe, A., Universidad de La Sabana, Chía, Colombia
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