Exploring AI Literacy and AI‐Induced Emotions among Chinese University English Language Teachers: The Partial Least Square Structural Equation Modeling (PLS‐SEM) Approach
Xiao Xie, Mark Feng Teng, Lawrence Jun Zhang, Abdullah Alamer
Abstract
ABSTRACT Despite artificial intelligence (AI) emerging as a key driver of innovation and transformation in language education, how to enhance language teachers’ AI literacy and understand their emotional experiences in AI‐mediated teaching remains largely unexplored. Drawing upon Appraisal Theory, this study seeks to uncover the interplay between language teachers’ AI literacy and their emotional responses. Data were collected from 148 English as a foreign language (EFL) teachers at universities and colleges in China through an online questionnaire. Partial least squares structural equation modeling (PLS‐SEM) was employed to examine the effects of four dimensions of AI literacy, including Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Applications (EAIA), and AI Ethics (AIE), on three types of emotions: enjoyment, anger, and anxiety. The results revealed significant positive correlations between the four dimensions of AI literacy and the three types of AI‐induced emotions. Furthermore, AAI and EAIA were found to positively predict teachers’ enjoyment, while EAIA also positively predicted teachers’ anger. However, KUAI and AIE did not predict any of the AI‐induced emotional outcomes, and none of the four dimensions of AI literacy were found to predict anxiety. This study highlights the necessity of targeted interventions, paving the way for more comprehensive teacher training programs and policy initiatives that equip educators with both technical knowledge and emotional resilience in AI‐mediated teaching environments, thereby supporting their effective and ethical adoption of AI.