Age-Related Differences in Artificial Intelligence Acceptance: Evidence from a Multi-Generational Adult Sample

Although artificial intelligence (AI) technologies are increasingly integrated into everyday life, their acceptance varies across age groups. The present study examined age-related differences in AI acceptance among 460 adults across five age cohorts (18–24, 25–34, 35–44, 45–54, and 55–64 years) using the Generative Artificial Intelligence Acceptance Scale (GAIAS), grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). One-way analyses of variance revealed statistically significant age effects on overall acceptance, performance expectancy, effort expectancy, and social influence, while facilitating conditions did not differ significantly by age. Post hoc Bonferroni comparisons showed that the 45–54 age group frequently appeared as the lower-scoring comparator, particularly in relation to the two youngest cohorts. Effort expectancy emerged as the dimension most clearly differentiated by age, accounting for four of the nine significant pairwise comparisons and yielding the largest mean differences between younger and older cohorts. These findings suggest that age-related variation in AI acceptance is expressed mainly through perceived ease of use rather than thro