This work presents AR FLOW, an Augmented Reality (AR)-supported laboratory environment developed in Unity Engine, that complements conventional teaching of fluid mechanics by making multi-scale flow phenomena interactive. It aims to promote functioning understanding through a modular, level-based learning design that integrates digital lab scripts, AR visualizations, and context-specific learning tasks.

The resulting environment aims to combine script-based learning and interactive laboratories in quest-based learning, which, using Constructive Alignment and the SOLO-taxonomy, can guide students from superficial learning to a relational and extended abstract understanding (deep learning) of the concepts of fluid mechanics.

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.

The integration of artificial intelligence (AI) into remote laboratories presents sig-nificant pedagogical challenges that require empirically grounded design frame-works. This study is part of research conducted within the framework of a mas-ter's degree project, and the objective is to develop seven design principles for the use of AI-assisted remote laboratories in chemistry education, derived from a multiple triangulation methodology with an inductive approach. Three comple-mentary studies were conducted: a focus group with teachers, a student experi-ence survey, and an analysis of student-AI interactions in a remote acid-base titra-tion II laboratory implemented at the University of Buenos Aires. Seven princi-ples emerged from a systematic triangulation of data from previous studies. The conceptual structure of the principles, based on generalisable teaching design ra-ther than specific disciplinary content, allows for their transferability to remote la-boratories in various fields, including physics, biology, and engineering, contrib-uting to the democratisation of science education in contexts where traditional re-sources are limited.

Mixed Reality (MR) technologies are increasingly used in engineering education to enable, e.g., virtual laboratory training. Yet, most applications support only individual learning, offering limited opportunities for teamwork and communication. This work-in-progress introduces an MR application that allows both local and remote collaboration, enabling two users to interact with the same virtual setup - either co-located or connected online - while sharing synchronized device states and interaction awareness.

The system integrates technical, linguistic, and cooperative dimensions within one immersive environment. In a joint bilingual course, German and U.S. students engage in problem-based laboratory tasks, alternating between English and German. The multi-user architecture embeds collaboration and language use directly into technical training, fostering engagement, co-presence, and teamwork. By shifting MR from isolated practice toward shared laboratory experiences, this approach provides a transferable model for communicative, collaborative learning in engineering education.

Students in the Biochemical and Chemical Engineering programs at TU Dortmund University often struggle to connect knowledge from individual courses into a coherent understanding. These challenges can be derived from limited constructive alignment and high informational load. For example, the first-semester course Introduction to Biochemical

and Chemical Engineering, which provides an overview of the curriculum, illustrates how integrating content from multiple courses can lead to students feeling overwhelmed. To support constructive alignment, the course has been redesigned with clear intended learning outcomes and active learning strategies, including student-generated exam questions and one-minute papers. Complementing this, ChemSPARK, a virtual reality model of a chemical plant representing an industrial process, provides an immersive, process-oriented framework. By contextualizing content in a realistic industrial setting, it is intended to foster cognitive map development, engagement, and long-term knowledge retention.