ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
This article is part of the Research TopicArtificial Intelligence in Educational and Business Ecosystems: Convergent Perspectives on Agency, Ethics, and TransformationView all 12 articles
Understanding Higher Education Students' Reluctance to Adopt GenAI in Learning in Latvia and Ukraine
Provisionally accepted
- 1Biznesa makslas un tehnologiju augstskola RISEBA, Riga, Latvia
- 2Ekonomikas un kulturas augstskola, Riga, Latvia
- 3West Ukrainian National University, Ternopil, Ukraine
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This study investigates the factors driving students' reluctance to adopt generative artificial intelligence (GenAI) in higher education in Latvia and Ukraine. Although GenAI tools are rapidly diffusing across educational settings, little empirical research has examined why students choose not to use them or how these reasons differ across institutional and demographic contexts. A cross-sectional survey (N = 945) was conducted across three universities, and data were analysed using descriptive statistics and binary logistic regression. The findings show that 22% of students do not use GenAI in their studies, with part-time students, distance learners, older students, and graduate students showing the highest rates of nonuse. The leading reasons for non-adoption were disbelief in GenAI effectiveness (46.9%), insufficient information (26.5%), and lack of knowledge about how to use GenAI tools (19.7%). Regression results indicate that learning format, age, and disciplinary affiliation significantly predict GenAI use in Ukraine, whereas only learning format predicts use in Latvia, suggesting that institutional context moderates students' technological engagement. These findings provide one of the first cross-national, quantitatively grounded analyses of GenAI non-use in Europe and Central and Eastern Europe. They highlight the importance of targeted institutional interventions, including structured training, explicit guidelines, and discipline-specific support, to ensure equitable and informed GenAI adoption and to better prepare students for an AI-transformed labor market.
Keywords: Gen AI application, university students, higher education, Generative AI, HEI
Received: 31 Jul 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Prohorovs, Užule and Tsaryk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Olga Tsaryk
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