The rapid diffusion of generative artificial intelligence (AI) and large language models (LLMs) has intensified the need to foster AI literacy in higher education. Although students frequently use AI tools, their ability to apply them critically and purposefully remains limited, underscoring the relevance of instructional formats that integrate structured AI use. This study examines the rede-sign of a bachelor-level wind channel laboratory in chemical engineering. The traditional cook-book-style experiment was converted into a problem-based learning (PBL) format aligned with the SOLO taxonomy and the European Commission/OECD AI literacy framework. In the revised structure, students autonomously engage with theoretical content, select one of two problem scenarios, and use LLMs for planning, conceptual understanding, data interpretation, and reflec-tion. Pre- and post-tests, motivation and technology acceptance questionnaires, and an instrument on generative AI use were employed. Qualitative results indicate increased student autonomy, deeper engagement with aerodynamic concepts, and improved efficiency compared to the in the years 2024 implementation. Students used LLMs across the first three AI literacy domains engag-ing, creating, and managing consistent with the laboratory’s intended learning outcomes. Supervi-sors observed higher motivation and more purposeful inquiry, while the restructured script re-duced time pressure and improved clarity.