Cross-National Survey Data on Student Attitudes Toward Artificial Intelligence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F25%3A43927417" target="_blank" >RIV/62156489:43110/25:43927417 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.dib.2025.112022" target="_blank" >https://doi.org/10.1016/j.dib.2025.112022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dib.2025.112022" target="_blank" >10.1016/j.dib.2025.112022</a>

Result language

angličtina

Original language name

Cross-National Survey Data on Student Attitudes Toward Artificial Intelligence

Original language description

This data article presents responses from a comprehensive, multi-year survey conducted between 2022 and 2024 at several universities in Central and Eastern Europe, focusing on students&apos; artificial intelligence (AI) literacy, attitudes, and readiness. The data collection was part of a longitudinal project within the FITPED consortium, which built upon previous EU-funded initiatives to support digital education. A total of 1,146 university students participated, representing a diverse range of study programs, academic years, and countries, including Slovakia, Poland, the Czech Republic, Lithuania, Indonesia, Turkey, France, and Ukraine. The structured questionnaire was based on validated instruments and included constructs such as AI literacy, AI readiness, AI anxiety, behavioural intention, satisfaction, confidence, perceived relevance of AI, and social goods. Items were rated on a 5-point Likert scale, and demographic information, including gender, age, year of study, field of study, and previous experience with AI-related courses, was collected. The survey was administered anonymously via Google Forms and the Moodle LMS in multiple languages, ensuring accessibility across disciplines. The dataset supports cross-disciplines and longitudinal comparisons and is suitable for quantitative analytical methods such as factor analysis and structural equation modeling. This openly shared dataset provides a foundation for tracking trends in AI readiness and perceptions in higher education. It allows for its reuse for comparative international research, curriculum development, and targeted educational interventions that promote inclusive and context-aware AI literacy.

Czech name

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Czech description

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Type

J_imp - 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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Data in Brief

ISSN

2352-3409

e-ISSN

2352-3409

Volume of the periodical

62

Issue of the periodical within the volume

October

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

112022

UT code for WoS article

001573770100006

EID of the result in the Scopus database

2-s2.0-105015302264