In order to realize a digital twin, in addition to future prediction technology using AI, modeling technology for virtualization specialized for physical systems and know-how for identifying data used in modeling are required. However, there are many cases in which the know-how specialized in these physical systems is not sufficient in educational settings. For this reason, we set assumptions about modeling techniques specific to physical systems and data to be collected, and prepare prototypes that realize digital twins based on those assumptions. This prototype provides a foundation for machine learning, AI model tuning, and implementation of trained AI models into digital twins. By experiencing the construction of this prototype, we will cultivate the ability to construct future prediction systems using AI in digital twins. In addition, by setting various hypotheses and carrying out realization and verification with prototypes, the educational effect will be enhanced. This paper describes the proposal of this educational method and its practice and evaluation.
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