Teacher training as a key factor in the adoption of artificial intelligence in primary education
DOI:
https://doi.org/10.17398/1695-288X.25.2.247Keywords:
artificial intelligence, Elementary Secondary Education, Elementary School Teachers, Educational Technology, Territorial Inequality, Equal EducationAbstract
Introduction/Objective: This study examines the relationship between formal artificial intelligence (AI) training and its adoption in teaching practice among primary education teachers in Chile's Maule Region, a territory marked by urban-rural inequalities. Method: A cross-sectional exploratory-descriptive contrasting-groups design was used with a sample of 150 teachers (54% women; 58.7% urban, 41.3% rural), who completed a validated 34-item questionnaire (August-October 2025). Descriptive statistics, Chi-square test, and Cramér's V as effect size measure were applied. Results: Complete group differentiation was observed (χ² = 143.96, df = 1, p < .001; Cramér's V = 0.980): 100% of formally trained teachers (n = 31) actively use AI, while no untrained teacher (n = 119) has integrated it. Trained teachers show high-frequency use (90.3%), high confidence (67.7%), and perceived pedagogical improvements (100%). A descriptive pattern of territorial inequality in training access is observed (urban: 25.0% vs. rural: 14.5%; rate ratio = 1.72). Discussion/Conclusions: Findings challenge the teacher-resistance narrative (only 10.1% of untrained teachers express active opposition), pointing to predominantly structural barriers. Formal training emerges as an enabling condition for AI adoption, with direct implications for the design of territorial equity policies governing access to teacher professional development in Latin American contexts marked by urban-rural inequality.
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