Eusterwiemann, TobiasTobiasEusterwiemannEiling, FlorianFlorianEilingGauger, Isabelle KatharinaIsabelle KatharinaGaugerBildstein, AndreasAndreasBildstein2022-03-062022-03-062021https://publica.fraunhofer.de/handle/publica/27079110.2139/ssrn.3862399Demonstration factories are a special type of learning factories with the intention to transfer knowledge with regard to innovative technological solutions in a tangible and use case-based manner. With Artificial Intelligence (AI) emerging as a global key technology, designers of demonstration factories are seeking ways for the evolution towards AI use cases. This paper shows how requirements for the design of educational use cases can be derived from CRISP-DM, an open standard for the implementation of data mining applications, which uses machine learning (ML), a subfield of AI. It further presents how suitable demonstrators can be planned and integrated in demonstration factories, based on these findings. As a result, an approach for the transfer of basic principles of AI to visitors in demonstration factories is presented. The approach is applied in a case study on the practical implementation of the data science track in the demonstration factory Future Work Lab.enKünstliche IntelligenzLernfabrikBildungpaper