Explaining AI Anxiety Among University Students: The Roles of Career Anxiety, Dehumanization, and Algorithmic Fairness

Abstract

Beyond its instructional benefits, AI in higher education can evoke anxiety when students perceive AI as diminishing human uniqueness, disrupting career trajectories, or operating in ways that feel difficult to evaluate or contest. This study aims to examine the effects of career anxiety, dehumanization, and perceived algorithmic fairness on students’ AI anxiety in the context of AI-supported learning. Using an explanatory quantitative survey design, data were collected from 70 university students who actively used AI-based learning tools, and the proposed relationships were tested using PLS-SEM. The results indicate that career anxiety positively predicts AI anxiety (β = 0.234, t = 1.691, p = 0.045) and dehumanization is the strongest predictor (β = 0.415, t = 2.958, p = 0.002), whereas perceived algorithmic fairness is not significant (β = 0.103, t = 0.740, p = 0.230), with the model explaining 48.2% of the variance in AI anxiety (R² = 0.482). These findings imply that AI anxiety is driven more by emotional and identity-related threats than by fairness evaluations, suggesting that institutions should adopt human-centered AI integration, strengthen AI literacy, and provide career-focused and psychological support to reduce student anxiety in AI-supported learning environments.

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