Artificial intelligence masters’ programs - An analysis of curricula building blocks

Autor(es):
Dodero, J. M | López Cobo, Montserrat | De Prato, G
Comisión Europea Centro Común de Investigación
Editor: Luxembourg : Publications Office of the European Union, 2021Descripción: 172 p. : gráf. ; 1 documento PDFTipo de contenido: texto (visual)
Tipo de medio: electrónico
Tipo de soporte: recurso en línea
ISBN: 978-92-76-37417-6Tema(s): Inteligencia Artificial | Educación digital | Europa | IA | educación superior | tecnologías de la información | plan de estudios | enseñanzaRecursos en línea: Acceso al documento Resumen: This report identifies building blocks of master programs on Artificial Intelligence (AI), on the basis of the existing programs available in the European Union.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. 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.
Lista(s) en las que aparece este ítem: Novedades Junio 2021
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This report identifies building blocks of master programs on Artificial Intelligence (AI), on the basis of the existing programs available in the European Union.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. 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.

The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence European Union

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