What drives students’ AI learning behavior: a perspective of AI anxiety

Abstract

As artificial intelligence (AI) technology rapidly develops and is deployed, students increasingly need to understand and learn AI-related skills for future employment. This study investigates how students’ AI learning anxiety and AI job replacement anxiety affect intrinsic/extrinsic learning motivations and subsequent AI learning intention. The moderating effect of learning self-efficacy is also examined. An online survey instrument collected data from a sample of students in Taiwan, and partial least-squares structural equation modeling (PLS-SEM) technique was employed to test the proposed model. The results indicate AI learning anxiety negatively affects learning motivations, but AI job replacement anxiety has a positive impact on extrinsic motivation. Learning self-efficacy and intrinsic/extrinsic motivations positively affect learning intention. Learning self-efficacy positively moderates the influence of intrinsic learning motivation on student AI learning intention but negatively moderates the influence of extrinsic learning motivation on student AI learning intention. The findings highlight the importance of AI anxiety and can be used to guide course design in an AI learning setting.