Gendered paths of AI anxiety: Exploring self-competence and efficacy in college students

In a world where artificial intelligence (AI) is slowly integrating itself into students’ lives, one thing is certain: it is inevitable. College students are adamant and still uncertain of AI’s potential. Hence, this study explores the complex relationships among AI anxiety, AI self-efficacy, and AI self-competency among college students. The study used a descriptive correlational research design employing a moderated mediation analysis with 1,006 convenience-sampled college students from a higher education institution in the Philippines. The study was also conducted during the 2nd semester of the 2024-2025 academic year. A standardized instrument was also used to determine AI anxiety, AI self-efficacy, and AI self-competence. Both descriptive statistics (means and standard deviations) and inferential statistics (Pearson’s r and multiple linear regression) were calculated using statistical software. Findings indicate average levels across the three constructs, with significant positive correlations observed between AI anxiety, AI self-efficacy, and AI self-competency. Gender, on the other hand, emerged as a moderating factor, influencing the relationships between AI self-efficacy and self-competency. Mediation analysis further demonstrated that AI self-efficacy significantly mediates the relationship between AI anxiety and AI self-competency. These results offer important insights into how cognitive and emotional factors interact in AI-related learning, contributing to a more nuanced understanding of students’ preparedness and adaptability in environments increasingly shaped by AI. The findings have implications for educational strategies that support students in technology-rich academic settings.