The KICK 4.0 research project focuses on the approach of effectively using large language model-based (LLM) generative AI technologies in laboratory-based engineering education. The aim of the project is to enable students and instructors to explore the benefits and limitations of generative AI systems in higher education.

Using a customized Design-Based Research (DBR) approach, an LLM-based AI tool is integrated into an ongoing course, evaluated iteratively, and refined. To this end, criteria for determining the effectiveness of an AI tool in terms of the quality of feedback were developed based on a systematic literature review and a survey of students and instructors. Given the small number of participants, the surveys were evaluated using qualitative empirical educational research methods. The results of this requirements analysis show that these technologies are considered to have great potential in supporting the individual learning process and that feedback quality is central in this.

This article describes the implementation of a teaching and learning scenario with the aim of integrating a Retrieval-Augmented Generation (RAG) chatbot into a laboratory-based course in fluid mechanics. In the Computational Fluid Dynamics (CFD) course, students learn how to use OpenFOAM, a software tool for solving complex fluid mechanics problems. Classes are held in a computer lab. Students work at individual workstations in front of a PC and the instructor demonstrates the relevant calculations and answers students' questions. In this setting, students are provided with a customized RAG chatbot as a “digital AI assistant.” The chatbot is trained to give students feedback that is comprehensible and conducive to learning. The results of this work will highlight factors for instructional design with regard to the effective use of LLM-based AI tools in instructional settings.