This article is published as part of the KICK 4.0 project, which aims to successfully integrate natural language processing (NLP) AI technologies into higher engineering education. One aspect of this is the investigation of the potential of NLP systems to increase the learning success of students, especially in the context of laboratory-based engineering education. Methodologically, an adapted design-based research approach (DBR) is pursued. An NLP AI will be integrated into a fluid mechanics laboratory course and successively modified as the project progresses. Based on detailed requirements analysis carried out together with the lecturers and students, an evaluation framework will be developed to assess the feedback generated, both for the research focus on the part of the students and on the part of the lecturers. The data from both groups will then be analyzed and evaluated to determine whether the intended goals have been achieved and where there is potential for optimization. The results of the project so far show that NLP AI technologies can be used in the context of laboratory teaching at universities and can have a positive influence on both learning success and learning motivation. Furthermore, users should develop a deep understanding of the benefits and limitations of NLP AI, develop their own skills in dealing with it and reduce reservations.