From Learning AI to Fearing Replacement: Reassessing AI Anxiety Among Business Students in the Generative AI Era
DOI:
https://doi.org/10.5281/zenodo.20776910Keywords:
artificial intelligence anxiety, job replacement anxiety, university students, gender differences, generative aiAbstract
The rapid diffusion of artificial intelligence (AI) technologies, particularly generative AI systems such as ChatGPT has transformed students’ perceptions of technological competence and future employment prospects. While previous studies have conceptualized AI anxiety as a multidimensional construct, limited research has examined whether its subdimensions continue to operate similarly in the post-generative AI era. This study investigates the relationship between two dimensions of AI anxiety-AI learning anxiety and job replacement anxiety-among undergraduate business students. Using a quantitative survey design, data were collected from 182 undergraduate students enrolled in the Business Administration program at Sivas Cumhuriyet University. Measurement items were adapted from the Artificial Intelligence Anxiety Scale developed by Wang & Wang (2022), focusing specifically on the learning anxiety and job replacement anxiety dimensions. Descriptive statistics, correlation analysis, regression analysis and independent samples t-tests were conducted to examine relationships between constructs and gender-based differences. Findings indicate a weak but positive relationship between AI learning anxiety and job replacement anxiety (r = .15), which is notably lower than the correlation reported in the original scale development study. In addition, female students reported significantly higher levels of both AI learning anxiety and job replacement anxiety compared to male students. The study contributes to the emerging literature on AI-related psychological responses by reassessing the dimensional structure of AI anxiety in a contemporary educational context. Limitations include the single-institution sample, cross-sectional design and restricted disciplinary focus, which may limit generalizability.
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