ARTIFICIAL INTELLIGENCE READINESS AND TEACHING SELF-EFFICACY IN HIGHER EDUCATION: THE MEDIATING ROLE OF DIGITAL SELF-EFFICACY

This study examined the relationship between faculty readiness for artificial intelligence (AI) applications and teaching self-efficacy among higher education faculty members, and tested whether digital self-efficacy mediated this relationship. Grounded in Bandura’s Social Cognitive Theory, the study proposed that faculty readiness for AI applications would positively predict both digital self-efficacy and teaching self-efficacy, that digital self-efficacy would positively predict teaching self-efficacy, and that digital self-efficacy would mediate the relationship between AI readiness and teaching self-efficacy. A sample of 200 faculty members from higher education institutions completed self-report measures of AI readiness, digital self-efficacy, and teaching self-efficacy. Data were analyzed using descriptive statistics, Pearson correlation, multiple regression, and mediation analysis via the PROCESS macro. Results indicated that faculty readiness for AI applications significantly predicted teaching self-efficacy and digital self-efficacy, and that digital self-efficacy significantly predicted teaching self-efficacy. The regression model explained 58% of the variance in teaching self-efficacy. Mediation analysis confirmed that digital self-efficacy partially mediated the relationship between AI readiness and teaching self-efficacy, accounting for approximately 51% of the total effect. These findings suggest that faculty confidence in using digital technologies is a key mechanism through which AI readiness translates into stronger teaching self-efficacy, highlighting the importance of digital-skills training in faculty development programs.