AI LITERACY, SELF-EFFICACY, AND SELF-COMPETENCE AMONG COLLEGE STUDENTS: VARIANCES AND INTERRELATIONSHIPS AMONG VARIABLES

Abstract

Understanding and securely using AI systems and tools requires AI literacy. In contrast, AI self-efficacy is a person's confidence in completing an AI task. Also, AI self-competence is the ability to explain how AI technologies are used at work and how they affect society.  This study examines college students' AI literacy, self-efficacy, and self-competence. Using a descriptive-correlational approach, the proponent assessed respondents' AI literacy, self-efficacy, and self-competence. The study also examined variations and connections between factors. The study participants were 1000 college students selected by purposive sampling. Before data collection, the proponent employed a modified instrument that was validated. Data was descriptively and inferentially analyzed using SPSS 23. Results suggest most pupils were "somewhat literate" in AI. They regarded themselves as "somewhat self-efficient" but "self-competent" in AI. The inferential analysis showed substantial differences in AI literacy by college, year level, and birth sex. Self-efficacy varied by college, year, age, and birth sex. The study found college and year-level differences in self-competence. Demographic traits and study variables were associated to some extent. According to the study's findings, the proponent recommended AI training programs, skill development for students and teachers, and institution-wide policy development and implementation to maximize AI's use in learning.