Artificial Intelligence Literacy Structure and the Factors Influencing Student Attitudes and Readiness in Central Europe Universities
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F25%3A43927072" target="_blank" >RIV/62156489:43110/25:43927072 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ACCESS.2025.3573575" target="_blank" >https://doi.org/10.1109/ACCESS.2025.3573575</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3573575" target="_blank" >10.1109/ACCESS.2025.3573575</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Intelligence Literacy Structure and the Factors Influencing Student Attitudes and Readiness in Central Europe Universities
Original language description
This study examines the structure of artificial intelligence (AI) literacy and factors influencing students' attitudes, readiness, and perceived relevance of AI in higher education at Central European universities. The research, based on data from 1,195 students enrolled in various study programs between 2022 and 2024, examines how variables such as gender, academic discipline, and year of study influence perceptions related to AI. A validated questionnaire targeting constructs including satisfaction, readiness, and relevance of AI was used. Non-parametric statistical methods were used to identify significant differences between groups, including Kruskal-Wallis and Mann-Whitney tests with Dunn-Bonferroni post hoc analysis. The findings reveal consistent differences across genders and disciplines, with males and IT students demonstrating significantly higher readiness and satisfaction with AI. Furthermore, satisfaction levels fluctuated over time, peaking in 2023 - likely influenced by the widespread adoption of tools like ChatGPT. Correlation analysis further highlighted the subtle interrelationships between constructs across different subgroups. The study underscores the importance of tailored AI education strategies and calls for targeted interventions to ensure equitable engagement with AI across diverse student populations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
13
Issue of the periodical within the volume
26 May
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
93235-93258
UT code for WoS article
001502494300002
EID of the result in the Scopus database
2-s2.0-105006706458