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This study confirmed the importance of AI education for fostering students' AI literacy and derived the necessity of constructivist-oriented datasets that provide contextual relevance to students' lives and real-world problem-solving experiences. By reconstructing the machine learning dataset development cycle through prior research, we developed datasets following each procedural step, then evaluated and refined them through expert panel interviews focusing on dataset quality metrics and characteristics of authentic activities. The datasets were deployed through educational programming platforms commonly used in AI education and designed for sustainable maintenance. To verify effectiveness, we analyzed usage metrics of the developed datasets and conducted comparative analysis of their impact on AI literacy through educational implementations. The research outcomes include development of four AI education datasets demonstrating potential to replace conventional materials like the Iris dataset. Implementation on major Korean AI education platforms confirmed high accessibility and utility, establishing these as crucial educational resources meeting classroom needs. Through application and effectiveness analysis, we verified that AI education datasets developed based on constructivism can: connect students' prior knowledge with real-world experiences, deepen understanding of AI model learning processes, and provide authentic data-driven computing experiences - collectively contributing to comprehensive AI literacy enhancement.
Kim et al. (Fri,) studied this question.