Creating artificial intelligence: An active approach using machine learning in elementary education
Programming and machine learning (ML) education can foster computational thinking, problem-solving, creativity, and artificial intelligence (AI) literacy, equipping learners to engage actively with AI beyond simply using existing tools. This study examines the implementation of an active learning approach based on ML in primary education, where students create and train their own ML models using accessible visual programming environments such as Teachable Machine and Responsible AI for Social Empowerment and Education. A pre-experimental pretest–posttest design was conducted with 996 students aged 10–11 from 11 schools. Learning outcomes were assessed through a knowledge test analyzed with paired-samples t-tests and a Likert-scale questionnaire analyzed using the Wilcoxon signed-rank test. Results revealed statistically significant improvements in students’ understanding of ML and AI, with higher posttest scores (t = −16.13, p < 0.001) and a moderate effect size. Questionnaire findings indicated that 97.9% of participants reported that ML activities improved their understanding of AI, while 94.6% reported enjoying the process of creating ML models. The findings suggest that engaging students in designing and training AI models may support computational thinking, creativity, motivation, and a critical understanding of intelligent systems. The study supports the integration of ML through visual programming as a promising strategy for AI literacy in primary education, which may foster active participation and responsible engagement with emerging technologies.
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