Artificial intelligence for determining learning strategies in university students - Sabando-García, Ángel Ramón; Olguín-Martínez, Cynthia Michel; Benavides-Lara, Raul Marcelo; Salazar-Echeagaray, Teresa Irina; Huerta-Mora, Eduardo Alfonso; Bumbila-García, Bibian Bibeca; Cedeño-Barcia, Lizandro Agustín; Moreira-Choez, Jenniffer Sobeida (Frontiers in Education, 2025)
BackgroundUniversity 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.MethodsThe research followed a quantitative, correlational, and predictive approach, with a probabilistic sample of 162 students aged 17–24, 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.ResultsThe 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.ConclusionsArtificial 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.