Open AccessArtificial Intelligence Literacy Structure and the Factors Influencing Student Attitudes and Readiness in Central Europe Universities
Author(s) -
Jan Skalka,
Malgorzata Przybyla-Kasperek,
Eugenia Smyrnova-Trybulska,
Cyril Klimes,
Radim Farana,
Valentina Dagien,
Vladimiras Dolgopolovas
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573575
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.