Mapping multidimensional artificial intelligence anxiety among academic librarians in the United Arab Emirates: An exploratory survey

This exploratory study examines artificial intelligence anxiety among academic librarians in the United Arab Emirates, a multilingual context undergoing rapid digital transformation. A cross-sectional online survey of 72 academic library professionals, recruited through purposive and snowball sampling, employed a newly developed 35-item instrument covering seven conceptually derived dimensions: job role evolution, ethical and bureaucratic concerns, patron resistance and social trust, governance and strategic alignment, infrastructure and digital divide, vendor and sustainability, and linguistic and cultural anxiety. Internal consistency ranged from acceptable to strong (α = 0.774–0.898). Overall, AI anxiety was moderate (M = 3.33 on a five-point scale), with ethical and bureaucratic concerns (M = 3.47), infrastructure and digital divide (M = 3.42), and vendor and sustainability concerns (M = 3.36) recording the highest means. However, the narrow range across construct means (0.23 points) warrants treating these differences as descriptive rather than substantive rankings. Concerns about storing patron data on foreign servers and shortages of skilled staff received the highest item-level ratings. Although women reported higher scores across all seven dimensions, no gender or other demographic differences remained statistically significant after correction for multiple comparisons. The findings highlight the institutional, infrastructural, ethical and governance conditions associated with AI anxiety in academic libraries. The proposed dimensions offer a context-specific framework for further investigation, subject to validation in larger samples. Practical implications include developing localized and multilingual AI tools, expanding inclusive professional development, and strengthening participatory governance of AI procurement and policy.