Conference Agenda

Cross-university development of teaching and learning materials has advantages for lecturers and students. This paper investigates potential and challenges of the development and the use of materials in Engineering Design Education courses on an exemplary project. For the investigation, lecturers were interviewed about today’s benefits of cross-university developed materials five years ago. These findings allow generalization for future cross-university development of learning materials, for example the importance of a high modularity which allows easy selection of individual materials.

The fundamental education of engineering students in the field of machine elements and engineering design is essential for future lectures and for a successful career. These fundamentals include technical drawings, standardizations, and machine components, along with their applications in the field. Students, particularly those in earlier semesters, show weaknesses in the fundamental understanding of the components employed in the domain of mechanical engineering. New learning methods and technologies, such as virtual reality and artificial intelligence, can be employed to assist these students. This paper analyzes the mistakes students made in their gearbox design assignments and proposes methods to minimize these errors. Despite the substantial initial time and energy investment, it is shown that courses using new technologies such as virtual reality enhance the student’s study while providing a fun way to further develop their skills.

Digital fabrication courses require tools that support iterative, process-oriented documentation and team collaboration. This paper presents the evolution of DesignEDU, a web-based platform for documenting learning progress in university-level digital fabrication education. Across two course iterations (248 students, n=226 survey responses), user feedback systematically drove platform changes: from cloud-hosted to self-hosted infrastructure, from a basic block editor to a feature-rich collaborative editor with real-time collaboration via Conflict-free Replicated Data Types (CRDTs), and from no AI integration to an embedded AI teaching assistant with speech-to-text and text improvement capabilities. Surveys in both cohorts confirmed consistent usability (ease of use: 3.97–4.03/5, learnability: 4.09/5) alongside persistent critical gaps, namely no autosave, no simultaneous editing, and cloud dependency, that motivated the platform's redesign. We trace each user-identified issue to a specific engineering solution, demonstrating a feedback-driven methodology for evolving educational technology in design education contexts.

AI technologies are expanding rapidly into product development processes (PDP). This study examines the role and potential of AI-based object detection (AIOD) within the PDP framework as defined by Pahl and Beitz. A literature review reveals that, while AIOD is currently applied primarily in production-proximate quality-assurance settings, its integration into the development workflow remains limited. Initial efforts have focused on CAD- and drawing-based methods that incorporate AIOD during the concept and embodiment phases. A complementary survey of practitioners confirms that AIOD is perceived as especially useful in the concept phase, offering benefits such as error detection, time savings and shorter development cycles. However, respondents also express concerns about reliability and potential negative impacts on technical competence. Overall, the findings reveal a distinct discrepancy: While AIOD is technologically mature in manufacturing, its adoption in the PDP remains limited.