Teaching Machine Learning in K-12 Education: Potential and Challenges

Del Pilar Aldana Bravo G.; Caballero J.E.A.P.; De La Cruz J.R.R.; De Ruiz K.F.C.; Purguaya R.J.T.; Cristobal O.I.M. · 2025 International Conference on Pervasive Computational Technologies, ICPCT 2025 (2025) · DOI: 10.1109/icpct64145.2025.10940750

Resumen

A growing number of from kindergarten to twelve and colleges and universities are using machine learning (ML) methods, which have shown promise in a variety of computer domains. The study needs to comprehend how awareness and freedom are formed in these frameworks. However, one of the challenges facing computer education studies is incorporating artificial intelligence and classical mathematical thinking into the school curriculum. Research on how individuals learn to prepare, evaluate, enhance, and implement machine learning systems is lacking, particularly in the K-12 educational area, despite machine learning's pivotal role in today's technology. This article places current research in the computing learning setting. Outlines variations that K-12 computing instructors should consider when tackling this obstacle, and graphs arising pathways in educational reality, concept, and innovation related to instructing machine learning in schools in grades K-12. To properly include ML in the more comprehensive K-12 computer curriculum, the paper focuses on significant aspects of the perspective shift.

Instituciones: Universidad Nacional de Ucayali; Universidad César Vallejo