How teachers’ AI readiness affects AI integration: insights from modeling analysis

Abstract

This study examined the dynamic relationships among professional development and training, resources and accessibility, attitudes and perceptions, pedagogical knowledge and expertise, teachers’ artificial intelligence (AI) readiness, and the integration of AI in teaching practices. The study employed a non-random purposive approach and utilized the structural equation modeling (SEM) method. Five hypotheses were meticulously formulated and subsequently subjected to empirical scrutiny, involving a cohort of 224 participants. Data were collected via Google Form and subjected to analysis to assess the goodness of fit of the proposed conceptual model. The findings revealed a positive relationship between teachers’ AI readiness and the integration of AI in teaching practices. Additionally, attitudes and perceptions, as well as pedagogical knowledge and expertise, exhibited positive effects on teachers’ AI readiness. However, the associations between teachers’ AI readiness and resources and accessibility, as well as pedagogical knowledge and expertise, were found to be relatively weak and statistically insignificant. The proposed conceptual model accounted for 55.3% of the variance, underscoring the significance of these established relationships for instructors, policymakers, and educators in devising practical intervention strategies to effectively harness the potential of AI.