 Artificial intelligence masters’ programmes - An analysis of curricula building blocksJuan Manuel Dodero ()
No JRC123713, JRC Research Reports from Joint Research Centre Abstract: This report identifies building blocks of master programs on Artificial Intelligence (AI), on the basis of the existing programs available in the European Union. These building blocks provide a first analysis that requires acceptance and sharing by the AI community. The proposal analyses first, the knowledge contents, and second, the educational competences declared as the learning outcomes, of 45 post-graduate academic masters’ programs related with AI from universities in 13 European countries (Belgium, Denmark, Finland, France, Germany, Italy, Ireland, Netherlands, Portugal, Spain, and Sweden in the EU; plus Switzerland and the United Kingdom). As a closely related and relevant part of Informatics and Computer Science, major AI-related curricula on data science have been also taken into consideration for the analysis. The definition of a specific AI curriculum besides data science curricula is motivated by the necessity of a deeper understanding of topics and skills of the former that build up the foundations of strong AI versus narrow AI, which is the general focus of the latter. The body of knowledge with the proposed building blocks for AI consists of a number of knowledge areas, which are classified as Essential, Core, General and Applied. First, the AI Essentials cover topics and competences from foundational disciplines that are fundamental to AI. Second, topics and competences showing a close interrelationship and specific of AI are classified in a set of AI Core domain-specific areas, plus one AI General area for non-domain-specific knowledge. Third, AI Applied areas are built on top of topics and competences required to develop AI applications and services under a more philosophical and ethical perspective. All the knowledge areas are refined into knowledge units and topics for the analysis. As the result of studying core AI knowledge topics from the master programs sample, machine learning is observed to prevail, followed in order by: computer vision; human-computer interaction; knowledge representation and reasoning; natural language processing; planning, search and optimisation; and robotics and intelligent automation. A significant number of master programs analysed are significantly focused on machine learning topics, despite being initially classified in another domain. It is noteworthy that machine learning topics, along with selected topics on knowledge representation, depict a high degree of commonality in AI and data science programs. Finally, the competence-based analysis of the sample master programs’ learning outcomes, based on Bloom’s cognitive levels, outputs that understanding and creating cognitive levels are dominant. Besides, analysing and evaluating are the most scarce cognitive levels. Another relevant outcome is that master programs on AI under the disciplinary lenses of engineering studies show a notable scarcity of competences related with informatics or computing, which are fundamental to AI. Keywords: artificial intelligence; competence-based curriculum; master program; higher education; digital skills (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ipt:iptwpa:jrc123713 Access Statistics for this paper More papers in JRC Research Reports from Joint Research Centre Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.