ORIGINAL RESEARCH article

Front. Educ.

Sec. Digital Education

Volume 10 - 2025 | doi: 10.3389/feduc.2025.1611189

Artificial intelligence for determining learning strategies in university students

Provisionally accepted
  • 1Pontifical Catholic University of Ecuador Santo Domingo, Santo Domingo, Ecuador
  • 2Universidad Autónoma de Sinaloa, Mazatlán, Sinaloa, Mexico
  • 3Escuela Superior Politécnica del Chimborazo, Riobamba, Chimborazo, Ecuador
  • 4Technical University of Manabi, Portoviejo, Manabí, Ecuador
  • 5State University of Milagro, Milagro, Ecuador

The final, formatted version of the article will be published soon.

Background: University students employ various learning strategies that influence their academic success and retention in the educational system. However, those who fail to use these strategies effectively may be at risk of dropping out. In this context, the objective of this study was to determine the learning strategies of students at the Pontifical Catholic University of Ecuador, Santo Domingo campus (PUCESD) using artificial intelligence; Methods: The research followed a quantitative, correlational, and predictive approach, with a probabilistic sample of 162 students aged 17 to 24, of whom 29% were male and 71% female, from public, private religious, private secular, and semi-private institutions. Through the ACRA questionnaire, three dimensions were evaluated: cognitive strategies, study habits, and learning support; Results: The results revealed a structure with adequate internal consistency and structural validity, high-lighting a significant relationship between cognitive strategies and study habits, suggesting a positive interaction between the two to optimize learning; Conclusions: artificial intelligence proved effective in identifying patterns in learning strategies. However, it is recommended to adjust certain questionnaire items to enhance its precision and applicability in diverse contexts, thereby facilitating targeted interventions.

Keywords: factor analysis, university student, Education evaluation, artificial intelligence, Learning method

Received: 13 Apr 2025; Accepted: 05 Jun 2025.

Copyright: © 2025 Sabando García, Olguín-Martínez, Benavides-Lara, Salazar-Echeagaray, Huerta-Mora, Bumbila-García, Cedeño-Barcia and Moreira-Choez. 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: Jenniffer Sobeida Moreira-Choez, State University of Milagro, Milagro, Ecuador

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