# Cross-national survey data on student attitudes toward artificial intelligence

## TL;DR

This paper shares survey data on university students' attitudes and readiness toward AI across multiple countries in Central and Eastern Europe.

## Contribution

The study provides a longitudinal, cross-national dataset on AI literacy and attitudes from diverse student populations.

## Key findings

- The dataset includes 1146 students from eight countries and various academic disciplines.
- It captures AI-related constructs such as anxiety, confidence, and perceived relevance using validated instruments.
- The data supports comparative international research and educational interventions.

## Abstract

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' 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 1146 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.