Artificial Intelligence Attitudes Among North Macedonian Primary Teachers: Demographics and Training as Predictors
DOI:
https://doi.org/10.57125/ELIJ.2026.09.25.01Keywords:
artificial intelligence attitudes, in-service teacher education, North Macedonia, professional development, technology adoption, transitional educational systems, Western Balkans.Abstract
Despite growing international interest in teacher attitudes towards artificial intelligence in education, empirical evidence from transitional educational systems in Southeast Europe (and North Macedonia in particular) remains critically limited. This study investigated the overall level of attitudes towards artificial intelligence among North Macedonian in-service primary school teachers and examined the extent to which demographic characteristics and artificial intelligence training and exposure predict those attitudes. A quantitative cross-sectional survey design was employed with a sample of 350 in-service primary school teachers recruited through purposive and snowball sampling across multiple regions of North Macedonia. Data were collected via an online questionnaire comprising demographic items and the 13-item Artificial Intelligence Attitude Scale, which measures three dimensions: Perceived Benefits, Perceived Risks (reverse-coded), and Inclination to Use artificial intelligence. Analyses included descriptive statistics, independent samples t-tests, one-way analysis of variance with post-hoc comparisons, Pearson correlations, and two-block hierarchical multiple regression. Results indicated moderately positive overall attitudes (total scale mean = 3.82, SD = 0.68), with the highest scores recorded for Perceived Benefits (M = 3.94) and the lowest for Perceived Risks (M = 3.18). Male teachers scored significantly higher than female teachers on Inclination to Use and total attitudes, though effect sizes were small. Early-career teachers reported significantly more positive attitudes than experienced colleagues, representing a medium effect. Postgraduate-qualified teachers scored significantly higher than those holding undergraduate degrees across all subscales. Artificial intelligence training and exposure emerged as the strongest predictor, producing a large effect size for total attitudes and accounting for an additional 13% of explained variance in hierarchical regression beyond the 10% attributable to demographic variables alone, yielding a total explained variance of 23%. These findings identify structured professional development in artificial intelligence as the most consequential lever for improving teacher readiness in resource-constrained transitional systems, with direct implications for national teacher preparation policy and digital education reform across the Western Balkans.
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Copyright (c) 2026 Bujar Adili, Makfirete Ameti, Gzim Xhambazi, Jehona Rushidi-Rexhepi, Arbresha Zenki-Dalipi
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