Analysis of artificial intelligence policies for higher education in Europe

Christian M. Stracke; Dai Griffiths; Dimitra Pappa; Senad Bećirović; Edda Polz; Loredana Perla; Annamaria Di Grassi; Stefania Massaro; Marjana Prifti Skenduli; Daniel Burgos Solans; Veronica Punzo; Denise Amram; Xenia Ziouvelou; Dora Katsamori; Sonja Gabriel; Nurun Nahar; Johannes Schleiss; Paul Hollins

Ayuda

Analysis of artificial intelligence policies for higher education in Europe

Stracke, Christian M. ^[1] ; Griffiths, Dai ^[2] ; Pappa, Dimitra ^[6] ; Bećirović, Senad ^[7] ; Polz, Edda ^[7] ; Perla, Loredana ^[8] ; Di Grassi, Annamaria ^[3] ; Massaro, Stefania ^[8] ; Skenduli, Marjana Prifti ^[9] ; Burgos, Daniel ^[2] ; Punzo, Veronica ^[4] ; Amram, Denise ^[4] ; Ziouvelou, Xenia ^[6] ; Katsamori, Dora ^[6] ; Gabriel, Sonja ^[10] ; Nahar, Nurun ^[5] ; Schleiss, Johannes ^[11] ; Hollins, Paul ^[5]
1. [1] University of Bonn
  
  University of Bonn
  
  Kreisfreie Stadt Bonn, Alemania
2. [2] Universidad Internacional de La Rioja
  
  Universidad Internacional de La Rioja
  
  Logroño, España
3. [3] University of Foggia
  
  University of Foggia
  
  Foggia, Italia
4. [4] Sant'Anna School of Advanced Studies
  
  Sant'Anna School of Advanced Studies
  
  Pisa, Italia
5. [5] University of Bolton
  
  University of Bolton
  
  Reino Unido
6. [6] National Centre for Scientific Research “Demokritos”
7. [7] University College of Teacher Education Lower Austria
8. [8] University of Bari
9. [9] University of New York Tirana
10. [10] University College for Teacher Education of Christian Churches Vienna/Krems
11. [11] Otto von Guericke University Magdeburg
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Localización: IJIMAI, ISSN-e 1989-1660, Vol. 9, Nº. 2, 2025, págs. 124-137
Idioma: inglés
DOI: 10.9781/ijimai.2025.02.011
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Resumen
- This paper analyses 15 AI policies for higher education from eight European countries, drawn from individual universities, from consortia of universities and from government agencies. Based on an overview of current research findings, it focuses the comparison of different aspects among the selected AI policies. The analysis distinguishes between four potential target groups, namely students, teachers, education managers and policy makers. The paper aims at contributing to the further development and improvement of AI policies for higher education through the identification of commonalities and gaps within the existing AI policies. Moreover, it calls for further and in particular evidence-based research to identify the potential and practical impact of AI in higher education and highlights the need to combine AI use in (higher) education with education about AI, often called as AI literacy.
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