Open Access

Frithjof Dorka, Kaoutar El Otmani, Maximilian Hentsch, Nils Künster, Daniel PalmORCiD

• Technologies related to Industry 4.0, such as the Internet of Things (IoT) and Artificial Intelligence (AI), find increasing applications in manufacturing systems. However, the technical implementation of IoT-based or AI-based solutions requires interaction and information exchange between the various components of complex information processing systems. Students of interdisciplinary study programs, such as industrial engineering, often possess conceptual yet isolated knowledge of manufacturing systems, IT infrastructure, and information processing without proficiency regarding application programming interface (API) usage. However, APIs are paramount for enabling the interaction of individual components of complex information processing systems. Unfortunately, adapting the general descriptions in API documentation to a student's specific application is often challenging, hindering a comprehensive hands-on learning experience for students training on implementing applications into manufacturing systems of learning factories. Therefore, this paper proposes a novel approach for leveraging Large Language Models (LLMs) to facilitate the utilization of APIs for students’ hands-on training on implementing applications and the respective information processing within the context of manufacturing systems and learning factories. The proposed approach comprises an LLM extended using context data specific to the employed test API and enables user interaction via a natural language dialogue-based chat interface.