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Oksanen, A., Osma, T., Heiskari, M. , Cvetkovic, A., Ruokosuo, E. S., Koike, M. ...Savolainen, L. (2026). Mapping AI learning readiness self-efficacy worldwide: Scale validation and cross-continental patterns. Computers in Human Behavior: Artificial Humans. 7

A. Oksanen et al.,  "Mapping AI learning readiness self-efficacy worldwide: Scale validation and cross-continental patterns", in Computers in Human Behavior: Artificial Humans, vol. 7, 2026

@article{oksanen2026_1770127795792,
	author = "Oksanen, A. and Osma, T. and Heiskari, M.  and Cvetkovic, A. and Ruokosuo, E. S. and Koike, M.  and Arriaga, P. and Savolainen, L.",
	title = "Mapping AI learning readiness self-efficacy worldwide: Scale validation and cross-continental patterns",
	journal = "Computers in Human Behavior: Artificial Humans",
	year = "2026",
	volume = "7",
	number = "",
	doi = "10.1016/j.chbah.2026.100251",
	url = "https://www.sciencedirect.com/journal/computers-in-human-behavior-artificial-humans"
}

TY  - JOUR
TI  - Mapping AI learning readiness self-efficacy worldwide: Scale validation and cross-continental patterns
T2  - Computers in Human Behavior: Artificial Humans
VL  - 7
AU  - Oksanen, A.
AU  - Osma, T.
AU  - Heiskari, M. 
AU  - Cvetkovic, A.
AU  - Ruokosuo, E. S.
AU  - Koike, M. 
AU  - Arriaga, P.
AU  - Savolainen, L.
PY  - 2026
SN  - 2949-8821
DO  - 10.1016/j.chbah.2026.100251
UR  - https://www.sciencedirect.com/journal/computers-in-human-behavior-artificial-humans
AB  - In today's world, knowing how to use artificial intelligence (AI) technologies is becoming an essential skill. While methods for measuring the perceived efficacy of AI use are emerging, brief measures of users' self-evaluated learning and self-efficacy regarding AI use are still lacking. This study aimed to validate the five-item AI Learning Readiness Self-Efficacy (AILRSE) scale and examine cross-national differences between 12 countries on six continents. We used large-scale, adult population samples from Australia, Brazil, Finland, France, Germany, Ireland, Italy, Japan, Poland, Portugal, South Africa, and the United States collected in 2024–2025 (N = 20,173), enabling both cross-sectional and longitudinal analysis. Scale validation involved confirmatory factor analysis and measurement invariance testing across countries and over time. The results supported a one-factor structure with high internal consistency and scalar invariance across countries as well as strict invariance in Finnish cross-sectional and longitudinal data. AI positivity emerged as the strongest predictor of AILRSE-5 scores across all models, followed by younger age and more frequent use of text-to-text AI tools (e.g., ChatGPT, Copilot). Education and gender effects were small and context dependent. The findings indicate that AILRSE-5 is a brief, reliable, and valid tool for assessing self-efficacy in AI learning readiness. Its invariance across diverse national contexts supports its applicability in cross-cultural research, while its longitudinal invariance suggests stability over time. Furthermore, our results provide rare cross-national evidence on the individual factors shaping AI learning readiness self-efficacy. The study advances understanding of how people adapt to the rapidly evolving AI landscape.
ER  -