Use of artificial intelligence to detect learning styles in elementary school students
DOI:
https://doi.org/10.71112/eaktff84Keywords:
artificial intelligence, learning styles, learning analytics, basic education, personalized learningAbstract
This study examines the use of artificial intelligence (AI) to identify learning styles in students from a public Basic Education institution in Ecuador. A quantitative, non-experimental, and descriptive–correlational approach was applied, combining a traditional learning-style questionnaire with behavioral data obtained through a digital learning platform. Using a supervised learning model, the AI system classified predominant learning styles based on patterns such as response time, navigation sequence, and preference for visual or verbal resources. Results showed a higher proportion of students with visual and active styles, while global and sequential styles were less frequent. The model achieved solid metrics of accuracy, precision, and F1-score, demonstrating its reliability for analyzing data in real educational contexts. These findings indicate that AI can serve as a strategic tool to personalize instruction, support teacher decision-making, and address the diversity present in public classrooms. Additionally, the study highlights the importance of integrating AI technologies in an ethical, responsible, and pedagogically consistent manner to ensure meaningful impacts on student learning.
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Copyright (c) 2026 Richard Oswaldo Bravo Loaiza, Karla Michelle Preciado Portocarrera, Yadira Rocío Ordoñez Lapo, Alexandra Patricia Tigrero Martínez, Felipa Eugenia Tello Vera (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
