Bridging the Education–Employment Gap in Europe: An AI-Driven Approach to Skill Matching

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dc.authorid0000-0002-8289-6570
dc.contributor.authorSanguino, Ramón
dc.contributor.authorÇağlarırmak Uslu, Nilgün
dc.contributor.authorKarahan Dursun, Pınar
dc.contributor.authorÖzdemir, Caner
dc.contributor.authorBarroso, Ascensión
dc.contributor.authorSánchez-Hernández, María Isabel
dc.contributor.authorGaga, Eftade O.
dc.contributor.authorid414023
dc.date.accessioned2025-11-24T14:08:51Z
dc.date.available2025-11-24T14:08:51Z
dc.date.issued2025-10
dc.departmentFakülteler, Sanat ve Sosyal Bilimler Fakültesi, Ekonomi ve Finans Bölümü
dc.descriptionEmerging Sources Citation Index (ESCI)
dc.description.abstractEducation–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.
dc.identifier.citationSanguino, R., Uslu, N. Ç., Karahan-Dursun, P., Özdemir, C., Barroso, A., Sánchez-Hernández, M. I., & Gaga, E. O. (2025). Bridging the Education–Employment Gap in Europe: An AI-Driven Approach to Skill Matching. World, 6(4), 1-17. https://doi.org/10.3390/world6040143
dc.identifier.doi10.3390/world6040143
dc.identifier.endpage17
dc.identifier.issn2673-4060
dc.identifier.issue4
dc.identifier.startpage1
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/365
dc.identifier.volume6
dc.identifier.wosqualityQ1
dc.institutionauthorKarahan Dursun, Pınar
dc.language.isoen
dc.publisherMDPI
dc.relation.journalWorld
dc.relation.publicationcategoryMakale- Uluslararası- Hakemli Dergi- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjecteducation–employment mismatch
dc.subjectartificial intelligence
dc.subjectreskilling motivation
dc.titleBridging the Education–Employment Gap in Europe: An AI-Driven Approach to Skill Matching
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