Exploring AI Literacy and AI‐Induced Emotions among Chinese University English Language Teachers: The Partial Least Square Structural Equation Modeling (PLS‐SEM) Approach

• Published in: International journal of applied linguistics
• Main Authors: Xie, Xiao, Teng, Mark Feng, Zhang, Lawrence Jun, Alamer, Abdullah
• Format: Journal Article
• Language: English
• Published: 26.06.2025
• ISSN: 0802-6106; 1473-4192
• DOI: 10.1111/ijal.12798

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. 尽管人工智能 (AI) 已成为语言教育领域创新与变革的重要推动力, 但如何提升教师的AI素养, 以及理解语言教师在以AI为媒介的教学中的情绪体验, 仍然较少受到关注。本研究基于评价理论, 探讨语言教师的AI素养与其情绪反应之间的相互作用。通过在线问卷, 本研究向来自中国高校的148名英语作为外语 (EFL) 教师收集相关数据。采用偏最小二乘结构方程模型 (PLS‐SEM) 分析AI素养的四个维度, 了解与理解AI (KUAI) 、应用AI (AAI) 、评估AI应用 (EAIA) 以及AI伦理 (AIE), 对三种情绪 (享受、愤怒和焦虑) 的影响。研究结果表明, AI素养的四个维度与三种AI诱发情绪之间存在显著的正相关关系。此外, AAI和EAIA能够正向预测教师的享受情绪, 而EAIA还能够正向预测教师的愤怒情绪。然而, KUAI和AIE未能预测任何AI诱发的情绪结果, 且AI素养的四个维度均未预测焦虑情绪。本研究强调了制定针对性干预措施的必要性, 为更全面的教师培训项目和政策举措奠定基础, 使教育者在以AI为媒介的教学环境中既具备技术知识, 又具备情绪韧性, 从而支持他们有效且合乎伦理地应用AI。