Abstract

The rapid integration of artificial intelligence (AI) within higher education promises transformative gains in pedagogy, engagement, and learner outcomes. These technological advancements coexist with rising concerns around user anxiety, technostress, and the sociocultural factors shaping AI technology use. Despite the demonstrable benefits of AI such as enhanced self-regulated learning and performance (He et al., 2023), AI use remains uneven across culturally diverse populations. This study addresses a critical question: How do technostress, general anxiety, and social influence interact to shape AI use in higher education across cultural contexts? We develop and empirically test a mediated model with anxiety serving as a key mechanism linking technostress and social influence on AI use. While prior research has established that AI-enabled learning environments can significantly improve academic outcomes (e.g., Kim et al., 2021; Wang & Johnson, 2022), little is known about the psychological and cultural contingencies that condition these benefits. By highlighting anxiety as a central mediator in the AI use and adoption process, this study offers novel theoretical insights and actionable implications for the design, implementation, and scaling of culturally responsive AI systems in higher education. The proposed model (see Figure 1) contributes to the growing literature on AI adoption and technostress, advancing a more nuanced understanding of user-centered AI integration in global educational contexts.

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