This study addresses the persistent challenges of content homogeneity, limited interaction formats, and rigid assessment methods prevalent in current children’s programming education systems by exploring the integration pathways and trans-formative potential of generative artificial intelligence (GAI). After establishing a conceptual framework that outlines the definitions, classifications, and existing pedagogical challenges in children’s programming education, the research examines the foundational principles and application mechanisms of GAI. It then systematically analyzes how GAI can mitigate these educational constraints through three interconnected dimensions: (1) dynamically generated learning content, (2) human–machine collaborative instruction, and (3) multi modal assessment approaches. To enhance analytical rigor, quantitative metrics are incorporated to evaluate the effectiveness of these interventions. The findings provide both theoretical insights and practical recommendations for improving the quality of children’s programming education, offering valuable pedagogical implications for educators and stakeholders in academic institutions, training organizations, and related sectors, thereby supporting innovation in technology-enhanced learning environments.