ARTIFICIAL INTELLIGENCE READINESS AND TEACHINGSELF-EFFICACY IN HIGHER EDUCATION: THE MEDIATINGROLE OF DIGITAL SELF-EFFICACY

Abstract

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 selfefficacy. 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 selfefficacy, 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 selfefficacy, 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.