Development of a Python Library to Generate Synthetic Datasets for Artificial Intelligence Education

This study aims to improve the quality of AI education for the AI era by developing an educational dataset library and exploring its applicability. Reflecting the needs of teachers engaged in AI educational activities, the dataset library emphasizes the diversity of topics, forms, and sizes of datasets provided. Additionally, it is designed with a feature to generate outliers and missing values suitable for students' accessibility and educational purposes. The library developed in this research is based on Python and employs the random forest modeling method to generate high-quality synthetic datasets. The functionality and suitability of this library for AI education have been evaluated by an expert panel, which has endorsed its applicability in the field. In detailed assessments of the synthetic datasets generated, the library demonstrated its capability to accurately mirror the statistical characteristics of original datasets, achieving high levels of accuracy and cosine similarity in the modeling results. These outcomes confirm the library's efficacy in reconstructing educational datasets specifically for AI education purposes and crafting high-quality synthetic datasets. This approach offers a practical solution to the existing shortage of educational datasets and substantially enhances the overall quality of education. This research proves immensely beneficial for educators and learners, laying a foundation for ongoing and future research focused on creating and utilizing educational datasets in AI. This paves the way for expanding the possibilities and scope of their application in the educational field, potentially transforming AI education practices.