AI competency levels and teaching profiles: a multivariate analysis in the field of Music education - Pozo-Sánchez, Santiago; López-Belmonte, Jesús; Benítez Aguilar, Gonzalo; Nunes-Corredeira, Rui-Manuel (IJERI: International Journal of Educational Research and Innovation, 2026)
This research analyzes the level of Artificial Intelligence competency in a sample of 387 primary school music teachers in Spain to determine the degree of transfer of these resources to their teaching practice. Under a non-experimental and quantitative design, the validated ECIA-EMUS scale was applied, evaluating dimensions ranging from technical understanding and pedagogical integration to ethical use and specific training. The results reveal a hierarchy of domains where the group’s main strength lies in ethical and inclusive use, while the greatest vulnerability is located in operational integration within teaching-learning processes. Inferential analysis confirms that age is the contextual factor with the greatest predictive weight, evidencing a significant generational gap in technological readiness. Likewise, multivariate cluster analysis allowed for the identification of three distinct profiles: initial literacy, intermediate competency, and digital leadership. It is concluded that a dichotomy exists between deontological commitment and actual technical capacity, positioning specific training as the fundamental driver for transposing the theoretical framework into effective classroom practice that enhances musical creativity and inclusion.