As part of the CrossLab project, LabCAR (Lab Constructive Alignment Recommender) was developed as an innovative tool that supports both students and teachers in the targeted design and implementation of learning processes. The system has a modular structure and enables the analysis and optimisation of learning outcomes (LOs), the coordination of learning and teaching activities, and the generation of well-founded recommendations for monitoring learning success. Students benefit from personalised suggestions for preparing for the defined learning outcomes and receive references to suitable learning resources, like digital laboratories. Teachers receive support in developing and adapting teaching methods and assessment strategies to promote the achievement of LOs. As part of the evaluation, various large language models (LLMs) were analysed in terms of their performance in the context of LabCAR. The study showed that all LLMs tested are fundamentally capable of fulfilling the defined objectives. However, significant differences in the quality and precision of the recommendations generated were found. The results underscore the importance of carefully selecting and adapting LLMs to meet the specific requirements of the education sector and ensure effective support for teaching and learning processes.