TEM Journal | 2026
Authors: Del Castillo H.C.C.; Cieza-Mostacero S.E.
DOI: 10.18421/TEM152-69
Journal: TEM Journal
Year: 2026
Publisher: UIKTEN - Association for Information Communication Technology Education and Science
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Cyberbullying is a growing problem in digital environments that affects students’ mental health. Anonymity, the broad reach of these platforms, and the lack of regulation worsen the situation. This research proposes that the use of machine learning (ML) can improve the detection of cases by aiming to increase the number of identified cases, reduce detection time, and enhance the accuracy rate. An applied and experimental methodology was implemented, comparing a control group (manual detection) with an experimental group (ML-based system). The results showed significant improvements across all indicators: The number of detected cases increased by 42.12%, detection time was reduced by over 99.9%, and the accuracy rate improved from 85.3% to 98.8%. These findings validate that the use of ML enhances the detection of cyberbullying cases, offering a scalable solution for educational institutions to transition from reactive to preventive strategies, thereby fostering safer digital ecosystems for students. © 2026 Hugo C. Casanova Del Castillo & Segundo E. Cieza-Mostacero.
Artificial Intelligence; cognition; computer science; learning