Understanding AI Stress Among Higher Education Students Through Validation of the AI Anxiety Scale
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Abstract
The rapid integration of Artificial Intelligence (AI) into higher education has significantly transformed students’ learning practices, particularly as AI tools become increasingly embedded in academic activities. While AI enhances efficiency and productivity, it also raises psychological concerns related to competence development, technical complexity, and future career uncertainty. This study aims to validate the AI Anxiety Scale (AIA Scale) within the Indonesian higher education context and to examine the determinants of AI anxiety among students. A quantitative approach was employed using survey data collected from 240 university students. The instrument demonstrated good validity and reliability, and the structural model met acceptable goodness-of-fit criteria using SEM-AMOS analysis. The model includes four antecedent variables, learning, AI configuration, job replacement, and sociotechnical blindness. The findings reveal that learning and AI configuration have positive and significant effects on AI anxiety, indicating that anxiety is primarily driven by challenges in acquiring AI-related competencies and dealing with technical aspects of AI systems. In contrast, job replacement does not significantly influence AI anxiety, suggesting that career-related concerns remain relatively abstract for students. Interestingly, sociotechnical blindness shows a negative and significant effect, implying that broader concerns about the social and ethical implications of AI do not necessarily increase personal anxiety. This study highlights that AI anxiety is more strongly influenced by immediate, experience-based factors rather than long-term career concerns. The findings contribute to the literature on technology-related anxiety and offer practical implications for designing supportive and adaptive AI-integrated learning environments in higher education.
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