Cross-reality (XR) laboratories are becoming increasingly relevant in STEM education, enabling students to engage in experimentation beyond the re-strictions of traditional physical laboratory spaces. At the same time, large lan-guage models (LLMs) such as ChatGPT are rapidly entering higher education and raise new questions regarding competence development, learning practices, and educational integrity. The KICK 4.0 project addresses these developments by integrating AI-based natural language processing (NLP) tools into XR-based laboratory scenarios in fluid mechanics, with the aim of fostering both technical and reflexive competencies for a transforming world of work. This paper pre-sents a brief narrative literature review of the use of LLM/NLP tools in STEM learning contexts and analyses these findings through the lens of McLuhan’s Laws of Media (LOM; Enhance, Obsolesce, Retrieve, Reverse). This review suggests that LLMs may enhance creativity, reflection, and problem-solving; obsolesce highly scripted, instruction-driven laboratory formats; and retrieve di-alogical and inquiry-based learning practices. At the same time, risks of reversal become visible, including deskilling, bias, dependency, and ethical concerns. By combining the KICK 4.0 project with a LOM-guided literature analysis, this paper provides a structured reflection on how human–AI collaboration may re-shape laboratory education. The findings underline the need for critical AI liter-acy and balanced integration of LLMs in STEM learning environments.