Artificial Intelligence Anxiety, Self-Efficacy, and Self-Competence among Students: Implications to Higher Education Institutions

Abstract

Artificial intelligence (AI) has become a trending topic of study, especially in education. However, due to its unknown potential, students are adamant about it. The objective of this study is to investigate college students’ perceptions of artificial intelligence (AI) anxiety, AI self-efficacy, and AI self-competence in a tertiary education institution. The study used a correlational research design with the help of an online survey to determine the variances and relationships among 1,030 purposively chosen students. This study adopted and modified measures that underwent reliability testing to gather data. The collected data were subjected to descriptive and inferential analysis using SPSS 23 for data computation. Results show that students have a moderate level of AI anxiety and AI self-efficacy; however, in terms of AI self-competency, they have a high level of it. Inferential analysis also revealed significant differences when the three variables were grouped according to demographic characteristics. At the same time, the study also found significant associations between AI anxiety, AI self-efficacy, and AI self-competence. The regression analysis confirmed that learning, job replacement, sociotechnical blindness, and AI configuration significantly influenced AI self-efficacy. On the other hand, job replacement, sociotechnical blindness, and AI configuration also predict AI self-competence. The study concludes that variances and relationships exist among AI anxiety, self-efficacy, and self-competence among college students.