Abstract : This pilot cross-sectional study examined the extent to which perceived resilience, mindset orientation, and demographic variables are associated with attitudes toward artificial intelligence (AI) adoption. Using a quantitative, cross-sectional design, secondary data from 88 adult participants were analyzed. Participants completed measures of AI attitudes (AIAS-4), perceived resilience (Brief Resilience Scale), and mindset orientation (adapted Theories of Intelligence Scale), along with demographic variables including age, biological sex, and education. Descriptive analyses indicated generally favorable attitudes toward AI, moderate to high levels of resilience, and a tendency toward growth-oriented mindset characteristics. Pearson correlation analyses revealed weak associations between AI attitudes and resilience, mindset, and age. Independent samples t-tests indicated no significant differences in resilience or mindset across sex; however, a statistically significant difference in AI attitudes was observed, with male participants reporting higher AI adoption scores than female participants. One-way analyses of variance demonstrated no significant differences in AI attitudes or resilience across educational levels or mindset categories. A small but statistically significant difference was observed in mindset orientation across education levels. Multiple regression analysis indicated that resilience, mindset, and age did not significantly predict AI attitudes, accounting for a minimal proportion of variance. Overall, findings suggest that attitudes toward AI adoption are not strongly explained by general psychological traits or most demographic variables. These results highlight the potential importance of domain-specific factors, such as experience with AI, perceived utility, and contextual exposure, as potential predictors of AI adoption attitudes. Future research should explore these factors using longitudinal and experimentally informed designs.
Howard Jeremy D. (Fri,) studied this question.