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

Johannes Schleiß, Matthias Carl Laupichler, Tobias Raupach, Sebastian Stober

2023Education Sciences67 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Relevance (law)Computer scienceDomain (mathematical analysis)Context (archaeology)UsabilityAutomated planning and schedulingField (mathematics)Artificial intelligenceHuman–computer interactionPaleontologyMathematical analysisBiologyMathematicsPolitical sciencePure mathematicsLawOnline Learning and AnalyticsScientific Computing and Data ManagementArtificial Intelligence in Healthcare and Education
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