Computational Thinking and AI Literacy: A Gender-Based Analysis Among Early Learners
Andrea E. Cotino-Arbelo, Jezabel Molina‐Gil, Carina Soledad González González · 2025
While women remain underrepresented in STEM fields, our study reveals insights from an overlooked frontier: preschool classrooms. Despite decades of global initiatives aimed at increasing female participation in technology fields, gender-interest stereotypes continue to discourage girls from pursuing computer science careers. Our research challenges these barriers by implementing an innovative educational program integrating Computational Thinking (CT) and Artificial Intelligence (AI) literacy. Using a quasi-experimental pretest-posttest design with$N=114$preschoolers, we assessed the impact of hands-on activities in algorithms, debugging, and control structures through the TechCheck questionnaire. Our findings challenge prevailing assumptions: while initial CT skills showed no gender disparity (boys M=58.8, girls M=53.95, p=,445), post-intervention results revealed girls significantly outperforming boys (girls M=66.11, boys M=51.61,$p=,022$). On the contrary, AI literacy assessments showed complete gender parity (p=,995). These results suggest that early childhood education could be the key to dismantling gender barriers in computer science, demonstrating that when learning environments are equitable, girls not only match but can exceed traditional performance expectations. This study opens new pathways for understanding how early educational interventions can reshape the future of gender equity in STEM fields, beginning with our youngest learners.