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“Who will be left behind?”: A Swedish case of learning AI in vocational education
Stockholm University, Engelska institutionen, Stockholm, SE-106 91, Sweden, Engelska institutionen.
University of Luxembourg, 11 Prte des Sciences, Esch-sur-Alzette, 4366, Luxembourg, 11 Prte des Sciences.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-2161-6395
University of Luxembourg, 11 Prte des Sciences, Esch-sur-Alzette, 4366, Luxembourg, 11 Prte des Sciences.
2025 (English)In: International Journal of Educational Research, ISSN 0883-0355, E-ISSN 1873-538X, Vol. 133, article id 102697Article in journal (Refereed) Published
Abstract [en]

Increasing numbers of job roles emphasise skills related to Artificial Intelligence (AI). Alongside this trend, AI education has been incorporated into curricula at all levels, from K-12 to higher education. However, how to teach AI knowledge and transfer this knowledge into practice, especially to vocational students, remains under-investigated. To address this research gap, we first examined the goals and components of an AI curriculum that has been running for more than three years in Sweden. Then, we interviewed fourteen vocational students from different cohorts within the curriculum about their learning experiences. We found that the students expected more personalised learning and guidance from dedicated instructors, and a better curriculum structure. Further to this, many students wanted to learn more practical skills; it also became clear that technical training, sustainability, and reflection opportunities were given surprisingly little consideration. Building on the results of this case study, we provide recommendations for the future integration of AI into vocational education.

Place, publisher, year, edition, pages
Elsevier BV , 2025. Vol. 133, article id 102697
Keywords [en]
AI curriculum, AI education, AI literacy, AI upskilling, Artificial Intelligence, Case study, Emerging jobs, Empirical study, Future of work, Vocational education and training
National Category
Pedagogy Educational Work
Identifiers
URN: urn:nbn:se:kth:diva-369035DOI: 10.1016/j.ijer.2025.102697ISI: 001571303200001Scopus ID: 2-s2.0-105011052426OAI: oai:DiVA.org:kth-369035DiVA, id: diva2:1997511
Note

QC 20250912

Available from: 2025-09-12 Created: 2025-09-12 Last updated: 2025-12-08Bibliographically approved

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Hedman, Anders

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