Artificial Intelligence in Contemporary Education: Teachers’ Competencies, Challenges, and Pedagogical Implications
TL;DR
The findings indicate that the competencies of teachers for artificial intelligence should be viewed as a multidimensional basis for further research, improvement of teaching practice, and planning of professional development in the field of application of AI in education.
Abstract
The increasing integration of AI in education imposes the need to examine the competencies of teachers for its pedagogically meaningful, safe, and responsible application. The goal of this research was to examine the self-assessments of teachers’ AI-related competencies, as well as to determine whether there were statistically significant differences regarding the subject area and years of teaching experience. The research was conducted on a sample of 165 primary and secondary school teachers in the Republic of Serbia. Data were collected using the Serbian-language version of the original Teacher Artificial Intelligence Competence Self-Efficacy (TAICS) Scale. The reliability and factor structure of the instrument were checked using Cronbach’s alpha coefficient, Kaiser–Meyer–Olkin (KMO) indicator, Bartlett’s test of sphericity, and exploratory factor analysis using principal axis factoring with Varimax rotation, while one-way analysis of variance (ANOVA) was used to examine differences. The results showed high internal consistency of the scale (Cronbach’s α = 0.972) and an interpretable three-factor solution explaining 71.202% of the total variance. The following factors were extracted: pedagogical–evaluative AI competencies, ethical–safety AI competencies, and operational–technical AI competencies. No statistically significant differences in pedagogical–evaluative or ethical–safety AI competencies were found according to subject area or years of teaching experience. For operational–technical AI competencies, an unadjusted difference was observed according to teaching experience; however, this finding did not remain statistically significant after the Bonferroni correction for multiple testing. These findings should be interpreted as preliminary and require replication in larger and more representative samples. The findings indicate that the competencies of teachers for artificial intelligence should be viewed as a multidimensional basis for further research, improvement of teaching practice, and planning of professional development in the field of application of AI in education.
