Development of a Data-Driven AI Convergence Education Program Using the History of Science: Focusing on Kepler's First Law

This study aims to develop a data-based artificial intelligence convergence education program utilizing the historical context of science. This study analyzed the scientific context of Kepler's abductive reasoning process in deriving Kepler's First Law through the historical context of science. Based on this, Mars orbit data from NASA was selected, and the data was preprocessed and a dataset was constructed to promote abductive reasoning in learners, taking into consideration the science and mathematics curricula. Using Orange3 for data analysis, Kepler's Second Law was inferred, and scientific inquiry activities were conducted to set hypotheses and verify Kepler's First Law through data visualization using GeoGebra and the dataset combined with Orange3. These activities were proposed as a data-driven AI-integrated education program utilizing the scientific inquiry process and data learning models. Through this program, we expect learners to recognize the necessity of active data preprocessing, the potential of scientific inquiry using data and AI, and the differences in the inference processes between scientists and AI. Further validation of the program's effectiveness for instructors and verification of its impact on learners are necessary in future studies.