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

Ramón Sanguino (), Nilgün Çağlarırmak Uslu, Pınar Karahan-Dursun, Caner Özdemir, Ascensión Barroso, María Isabel Sánchez-Hernández and Eftade O. Gaga
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Ramón Sanguino: Business Administration Department, University of Extremadura, 06006 Badajoz, Spain
Nilgün Çağlarırmak Uslu: Department of Economics, Anadolu University, Eskişehir 26470, Turkey
Pınar Karahan-Dursun: Department of Economics and Finance, Mudanya University, Mudanya 16940, Turkey
Caner Özdemir: Department of Labor Economics and Industrial Relations, Zonguldak Bülent Ecevit University, Zonguldak 67100, Turkey
Ascensión Barroso: Business Administration Department, University of Extremadura, 06006 Badajoz, Spain
María Isabel Sánchez-Hernández: Business Administration Department, University of Extremadura, 06006 Badajoz, Spain
Eftade O. Gaga: Department of Environmental Engineering, Eskisehir Technical University, Eskişehir 26555, Turkey

World, 2025, vol. 6, issue 4, 1-17

Abstract: Education–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.

Keywords: education–employment mismatch; artificial intelligence; reskilling motivation (search for similar items in EconPapers)
JEL-codes: G15 G17 G18 L21 L22 L25 L26 Q42 Q43 Q47 Q48 R51 R52 R58 (search for similar items in EconPapers)
Date: 2025
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