YOUTH READINESS FOR AI-DRIVEN HR PRACTICES IN THE BALTIC STATES: A COMPARATIVE STUDY
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Published:
Nov 21, 2025
Abstract
This study explores youth readiness for artificial intelligence (AI) applications in human resource (HR) and recruitment practices in Estonia, Latvia, and Lithuania. Based on a cross-country survey and qualitative interviews with individuals aged 18 to 30, the research examines digital competence, exposure to AI tools, and attitudes toward AI in hiring. The findings reveal significant differences across the Baltic states. Estonian youth demonstrate the highest readiness, supported by strong digital education and national AI initiatives. Lithuanian respondents show active use of AI in learning but report moderate institutional support. Latvian participants express interest in AI but indicate limited access and lower digital confidence. While most respondents view AI as a useful and efficient tool in HR, concerns remain regarding data transparency and fairness. The study offers evidence-based recommendations to support the integration of AI in education and employment services, aiming to improve youth adaptability to future AI-driven labour markets.
How to Cite
Khlud, V., & Reshina, G. (2025). YOUTH READINESS FOR AI-DRIVEN HR PRACTICES IN THE BALTIC STATES: A COMPARATIVE STUDY. Baltic Journal of Legal and Social Sciences, 177-190. https://doi.org/10.30525/2592-8813-2025-spec-14
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Keywords
youth; artificial intelligence; recruitment; human resource; Baltic states
References
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12. Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, 1-12. https://doi.org/10.1057/s41599-023-02079-x.
13. Cino, D., Lacko, D., Mascheroni, G., & Šmahel, D. (2022). Predictors of children’s and young people’s digital engagement in informational, communication, and entertainment activities: Findings from ten European countries. Journal of Children and Media, 17(1), 37–54. https://doi.org/10.1080/17482798.2022.2123013.
14. Cui, M., Chen, Z., & Huang, M. (2025). The impact of human interviewers versus AI interviewers on applicant self‐efficacy and offer acceptance. Asian Journal of Social Psychology. https://doi.org/10.1111/ajsp.70009.
15. Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showedbias-against-women-idUSKCN1MK0AG/.
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18. França, T., Mamede, H., Barroso, J., & Santos, V. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), e14694. https://doi.org/10.1016/j.heliyon.2023.e14694.
19. Fritts, M., & Cabrera, F. (2021). AI recruitment algorithms and the dehumanization problem. Ethics and Information Technology, 23, 791 - 801. https://doi.org/10.1007/s10676-021-09615-w.
20. Gilbert, L., Strine, T., Szucs, L., Crawford, T., Parks, S., Barradas, D., Njai, R., & Ko, J. (2020). Racial and Ethnic Differences in Parental Attitudes and Concerns About School Reopening During the COVID-19 Pandemic – United States, July 2020. Morbidity and Mortality Weekly Report, 69, 1848 - 1852. https://doi.org/10.15585/mmwr.mm6949a2.
21. Gonzalez, M., Liu, W., Shirase, L., Tomczak, D., Lobbe, C., Justenhoven, R., & Martin, N. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Comput. Hum. Behav., 130, 107179. https://doi.org/10.1016/j.chb.2022.107179.
22. Hekkala, S., & Hekkala, R. (2021). Integration of Artificial Intelligence into Recruiting Young Undergraduates: the Perceptions of 20-23-Year-Old Students., 1-10. https://doi.org/10.24251/HICSS.2021.021.
23. Holmström, J. (2021). From AI to digital transformation: The AI readiness framework. Business Horizons. https://doi.org/10.1016/J.BUSHOR.2021.03.006.
24. Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4359612.
25. Hunkenschroer, A., & Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda. Journal of Business Ethics, 178, 977–1007. https://doi.org/10.1007/s10551-022-05049-6.
26. Jamal, A., Aissaoui, K., & Kassal, S. (2024). Enhancing Recruitment Transparency and Efficiency with Explainable AI (XAI). 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI), 1-6. https://doi.org/10.1109/ESAI62891.2024.10913744.
27. Jatobá, A., Luz, C. G., Lima, E. P., Silva, L. C., & Costa, A. P. C. S. (2023). Applications of artificial intelligence in human resource management: A review and future research agenda. Journal of Business Research, 163, 113911. https://doi.org/10.1108/JOCM-03-2022-0075.
28. Kaplan, L., Klein, T., Wilson, M., & Graves, J. (2020). Knowledge, Practices, and Attitudes of Washington State Health Care Professionals Regarding Medical Cannabis. Cannabis and cannabinoid research, 5 2, 172-182. https://doi.org/10.1089/can.2019.0051.
29. Labudová, V., & Fodranova, I. (2024). The Impact of Socio-Economic Factors on Digital Skills in the Population of the EU 27 Countries. Virtual Economics. https://doi.org/10.34021/ve.2024.07.03(5).
30. La Sala, A., Fuller, R., Riolli, L., & Temperini, V. (2024). The rise of hybrids: plastic knowledge in human-AI interaction. J. Knowl. Manag., 28, 3023-3045. https://doi.org/10.1108/jkm-10-2023-1024.
31. Lashkari, M., & Cheng, J. (2023). “Finding the Magic Sauce”: Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581548.
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