Modeling the Mediating Role of Self-Efficacy in the Relationship Between Teachers’ Attitudes Toward Artificial Intelligence and Their Readiness to Teach It
Keywords:
Artificial intelligence, self-efficacy, instructional readiness, teachers’ attitudes, structural equation modelingAbstract
Purpose: The present study aimed to model the mediating role of self-efficacy in the relationship between teachers’ inclination toward artificial intelligence (AI) and their readiness to teach AI in educational settings.
Methods and Materials: This study employed a correlational design using Partial Least Squares Structural Equation Modeling (PLS-SEM). The statistical population consisted of 872 elementary, lower secondary, and upper secondary school teachers in Lamerd County, from whom 270 participants were selected using stratified proportional sampling based on the Krejcie and Morgan table. Data were collected using the Teachers’ Inclination and Readiness Toward Artificial Intelligence Questionnaire adapted from Ayanwale et al. (2022) and the Self-Efficacy in Using Artificial Intelligence Tools Questionnaire based on Montag et al. (2023). Reliability and validity were assessed through Cronbach’s alpha, composite reliability, and Average Variance Extracted (AVE). Structural relationships among the variables were examined using path analysis and model fit indices including SRMR, d_ULS, and d_G.
Findings: The results demonstrated that confidence in teaching AI (β = 0.200, p < .01), behavioral intention (β = 0.204, p < .01), AI relevance (β = 0.168, p < .01), perceived usefulness (β = 0.175, p < .01), and attitude toward AI use (β = 0.188, p < .01) had positive and significant effects on self-efficacy. Furthermore, self-efficacy had a strong direct effect on AI readiness (β = 0.621, p < .001). The indirect effects of confidence, behavioral intention, relevance, usefulness, and attitude on readiness through self-efficacy were also significant, confirming the mediating role of self-efficacy in the structural model. Model fit indices indicated acceptable model fit (SRMR = 0.091).
Conclusion: The findings indicate that teachers’ positive attitudes and motivational tendencies toward AI are transformed into actual readiness for AI instruction primarily through self-efficacy beliefs. Self-efficacy serves as a critical psychological mechanism linking teachers’ perceptions and intentions to practical readiness for AI integration.
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