Teacher readiness for AI integration: Bridging the gap between digital competence and pedagogical use of AI

Abstract

The study aimed to develop and validate a structural model of higher education teachers’ readiness for pedagogical integration of artificial intelligence, which consisted of six interrelated components: digital competence, pedagogical attitudes towards AI, self-efficacy, real practices of using AI, institutional support and perceived barriers. The study was conducted within the framework of a quantitative strategy using a descriptive correlational design. At the same time, the data were collected based on a mixed questionnaire among 278 higher education teachers. Descriptive statistics, correlation analysis, confirmatory factor analysis (CFA) and structural modeling (SEM) were used to analyze the data. The results indicated satisfactory psychometric characteristics of the developed instrument (α = .76–.89; CFI = .96; RMSEA = .041). The data also confirmed the existence of a gap between teachers' levels of digital competence and their actual use of AI. Structural modeling showed that self-efficacy is the strongest predictor of pedagogical use of AI (β = .42). However, the impact of digital competence is mainly mediated by self-efficacy and pedagogical attitudes. The practical significance of the study is that the developed APRS tool can be used by higher education institutions to diagnose the readiness of teaching staff and design targeted professional programs.