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.

A growing number of courses seek to increase the basic artificial-intelligence skills (“AI literacy”) of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool would be important to enable quality assurance and comparability. In this study, a validated AI-literacy-assessment instrument, the “scale for the assessment of non-experts’ AI literacy” (SNAIL) was adapted and used to evaluate an undergraduate AI course. We investigated whether the scale can be used to reliably evaluate AI courses and whether mediator variables, such as attitudes toward AI or participation in other AI courses, had an influence on learning gains. In addition to the traditional mean comparisons (i.e., t-tests), the comparative self-assessment (CSA) gain was calculated, which allowed for a more meaningful assessment of the increase in AI literacy. We found preliminary evidence that the adapted SNAIL questionnaire enables a valid evaluation of AI-learning gains. In particular, distinctions among different subconstructs and the differentiation constructs, such as attitudes toward AI, seem to be possible with the help of the SNAIL questionnaire.

Artificial Intelligence (AI) literacy is essential for society as a whole. While general frameworks and resources to support self-directed learning on AI are widely available, research on how to support AI educators, particularly those without AI expertise (non-experts), using external materials and resources is relatively scarce. This article explores the potential of open educational resources (OER) to enhance AI education, with a specific focus on the requirements and practices of AI educators. Through a case study of the AI Campus learning platform, the article examines how educators from diverse sectors such as school education, higher education and professional education utilise OER for AI education. The study aimed to identify patterns of OER usage, AI educator motivations and the sector-specific integration of OER into teaching practices. A survey study of 260 educators from Germany, Austria, and Switzerland using AI Campus content revealed that educators prefer smaller, modular OER formats and value suitable, high-quality and accessible content. The reputation of the person or institution that created the OER content does not seem to play a major role. Sector-specific differences could be observed in particular with regard to full online courses, face-to-face learning scenarios and the AI learning objectives of an educator. By focusing on educators’ perspectives, the study provides insight into how AI education can be strengthened across sectors through the use of OER materials and ultimately benefit learners through suitable, high-quality content and adequate AI learning scenarios.

Der Beitrag untersucht Auswirkungen und Fragestellungen von generativen Sprachmodellen, einer Form der Künstlichen Intelligenz (KI), in den Bereichen von Kompetenzen, Prüfungen und Lehr-Lern-Szenarien. Wir diskutieren, inwiefern KI neue Kompetenzen erfordert, Kompetenzen an Relevanz verlieren und Kompetenzen verloren gehen. Die teilweise veränderten Kompetenzen wirken auch auf die Prüfungsgestaltung: Es stellt sich die Frage, wo KI aus Prüfungen ausgeschlossen und wo sie bewusst eingeschlossen werden sollte. Beide Entscheidungen haben Einfluss auf die Zuverlässigkeit und Alltagsnähe der Prüfungen. KI wirkt hier als Verstärker bestehender Fragen und Problemstellungen. Im Bereich der Lehr-Lern-Szenarien eröffnet KI Lernenden die Möglichkeit, Lernmedien selbst zu adaptieren. Darüber hinaus kann KI als weiterer Akteur in Lehr-Lern-Settings hinzukommen und es stellen sich Fragen, wie dies die soziale Situation des Lernens verändern kann. Im Sinne des Constructive Alignment argumentiert dieser Beitrag, dass KI nicht isoliert, sondern didaktisch verschränkt in Kompetenzen, Prüfungen und Lehr-Lern-Szenarien integriert werden muss. Für die Diskussion und Exploration der sich daraus ergebenden Fragen brauchen Bildungsinstitutionen Offenheit und Experimentierräume.

A major challenge in engineering education is to empower students to use their acquired technical skills to solve real-world problems. In particular, methods of Artificial Intelligence (AI) need to be studied as tools in their respective application contexts. This puts pressure on university lecturers concerning the didactical design and elaboration of a course, and requires them to move towards a practice-based learning approach. Moreover, working on real-world problems leads to uncertainties for the lecturer and their students. Before and during the course, it is not always clear which methods will be used to solve the problem, respectively which competencies the participants need to acquire. Therefore, we propose to combine two established approaches: a project-based learning approach and the use of digital, curated learning content provided by Open Education Resources (OERs). We hypothesise that a practical study project solving a real-world problem using a combination of OERs and project-based learning is beneficial to AI education. Furthermore, we show implementations of our concept in three different courses. The first results indicate that student-centred tasks lead to high intrinsic motivation. At the same time, lecturers have to deal with a modified and extended role: They are no longer the broadcaster of knowledge but rather a guide within the learning process. Using the combination of OERs and project-based learning, the courses are attractive and exciting for students and lecturers without becoming unmanageable.