Planning Interdisciplinary Artificial Intelligence Courses For Engineering Students

As Artificial Intelligence (AI) becomes increasingly important in engineering, instructors need to incorporate AI concepts into their subject-specific courses. However, many teachers may lack the expertise to do so effectively or don’t know where to start. To address this challenge, we have developed the AI Course Design Planning Framework to help instructors structure their teaching of domain-specific AI skills. This workshop aimed to equip participants with an understanding of the framework and its application to their courses. The workshop was designed for instructors in engineering education who are interested in interdisciplinary teaching and teaching about AI in the context of their domain. Throughout the workshop, participants worked hands-on in groups with the framework, applied it to their intended courses and reflected on the use. The workshop revealed challenges in defining domain-specific AI use cases and assessing learners\u27 skills and instructors\u27 competencies. At the same time, participants found the framework effective in early course development. Overall, the results of the workshop highlight the need for AI integration in engineering education and equipping educators with effective tools and training. It is clear that further efforts are needed to fully embrace AI in engineering education

Alternative depth-averaged models for gravity currents and free shear flows

Two approaches have traditionally been used to describe the widening rate of jets and plumes: the diffusion concept of Prandtl, and the entrainment principle of Morton, Taylor and Turner. The entrainment concept is based on depth-averaged flow scales, and was later applied to plane gravity currents on an incline by Ellison and Turner [ET]. The two parameterizations are compared here for free shear flows, and gravity currents. It is shown that the diffusion concept is suitable for supercritical gravity currents, and that both approaches agree for subcritical ones. Depth-averaged models are also used for open channel flows, but the depth and velocity scales for the two flows are different. Those of ET are derived from the velocity distribution, whereas the depth of an open channel flow is the vertical extent of the dense liquid phase, and the velocity is derived from its flux. To reconcile the two descriptions, we extended the mass-based flow scales of open channel flows to gravity currents in an earlier contribution. In the present study these scales are outlined, and extended further to axisymmetric and non-buoyant free shear flows. Ratios of the diffusion rates in terms of mass- and velocity-based flow scales, are obtained from available experimental data for free shear flow

Zukunftsbild Hochschullehre 2025

Das Diskussionspapier zur Hochschullehre 2025 zeichnet sich methodisch durch seine partizipative Entstehung aus: In einem der HFDcon 2022 zeitlich vorgelagerten Prozess trafen sich 21 angemeldete Teilnehmer:innen aus dem deutschen Hoch- schulumfeld, um den Wandel der Hochschullehre für das Jahr 2025 zu skizzieren und Thesen für eine neue Denkkultur zu formu- lieren. In mehreren virtuellen Sitzungen wurden zunächst die Ziele des Papiers definiert, Themen geclustert und erste Forderungen ent- wickelt. Innerhalb der folgenden vier Wochen wurde dann – teils in Kleingruppen – kontrovers diskutiert, formuliert und überarbeitet, bis das Papier am 30. Juni 2022 in seiner ersten Fassung auf der Konferenz HFDcon 2022 vorgestellt wurde

Alternative depth-averaged models for gravity currents and free shear flows

Two approaches have traditionally been used to describe the widening rate of jets and plumes: the diffusion concept of Prandtl, and the entrainment principle of Morton, Taylor and Turner. The entrainment concept is based on depth-averaged flow scales, and was later applied to plane gravity currents on an incline by Ellison and Turner [ET]. The two parameterizations are compared here for free shear flows, and gravity currents. It is shown that the diffusion concept is suitable for supercritical gravity currents, and that both approaches agree for subcritical ones. Depth-averaged models are also used for open channel flows, but the depth and velocity scales for the two flows are different. Those of ET are derived from the velocity distribution, whereas the depth of an open channel flow is the vertical extent of the dense liquid phase, and the velocity is derived from its flux. To reconcile the two descriptions, we extended the mass-based flow scales of open channel flows to gravity currents in an earlier contribution. In the present study these scales are outlined, and extended further to axisymmetric and non-buoyant free shear flows. Ratios of the diffusion rates in terms of mass- and velocity-based flow scales, are obtained from available experimental data for free shear flows

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes