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We demonstrated that a deep-learning model can reliably operationalize an established developmental coding system for preschool children's drawings, creating a scalable method for conducting large cross-cultural developmental research. By automating the classification process, this machine-learning algorithm not only reduces the burden on human coders but also enables high-throughput analysis of large data sets, facilitating scalability for similar studies. The findings have implications for education researchers involved in data analysis of preschool children’s drawings, offering valuable insights into their school readiness. 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