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.

Higher Degree by Research (HDR) students in language and education disciplines, particularly those enrolled in thesis-only programmes, are increasingly expected to interpret complex statistical data. However, many lack the analytical skills required for independent statistical analysis, posing challenges to their research competence. This study investigated the pedagogical potential of ChatGPT-4o feedback and peer feedback in supporting students’ evaluative judgement during a 14-week doctoral-level statistical analysis course at a research-intensive university. Thirty-two doctoral students were assigned to receive either ChatGPT feedback or peer feedback on a mid-term assignment. They were then required to complete written reflections. Follow-up interviews with six selected participants revealed that each feedback modality influenced their evaluative judgement differently across three dimensions: hard (accuracy-based), soft (value-based), and dynamic (process-based). While ChatGPT provided timely and detailed guidance, it offered limited support for students’ confidence in verifying accuracy. Peer feedback promoted critical reflection and collaboration but varied in quality. We therefore argue that strategically combining ChatGPT feedback and peer feedback may better support novice researchers in developing statistical competence in hybrid human–AI learning environments.

The integration of artificial intelligence (AI) into language education is transforming pedagogical practices. This transformation is influenced by two distinct yet interconnected domains: the cognitive domain, encompassing AI literacy, and the affective domain, involving AI-induced emotions. However, the dynamic interplay between cognition and emotion in this context is a critical yet underexplored area of research. This study presents a systematic review, compliant with PRISMA guidelines, synthesizing findings from the Web of Science, Scopus, and ProQuest databases. The review delineates the specific characteristics of AI literacy and catalogues the range of emotions arising from AI use. To bridge the identified theoretical gap, we introduce a conceptual framework based on Appraisal Theory, which provides a mechanism to link cognitive evaluations of AI with emotional responses. This leads to the proposal of a holistic strategy that addresses not only the literacy required for effective AI tool usage but also the emotional support necessary to mitigate the challenges and stresses of technological integration.

Boredom, as an inhibitory negative emotion, is prevalent in foreign language learning but its effects on learning are often underestimated. Within the context of Chinese as a foreign language (CFL), few studies have examined the dynamic relationship of boredom with motivational sources and learning engagement. To address this research gap, we probed the relationship between the ideal L2 self and affective, behavioural, and cognitive engagement, specifically focusing on the mediating role of boredom. Data via an online questionnaire from 791 CFL learners at a Thai senior high school were collected to gauge their ideal L2 self, boredom, and engagement. Results of PLS-SEM analysis indicated that the ideal L2 self positively impacted affective engagement and behavioural engagement, with boredom acting as a mediator in these relationships, highlighting the distinctiveness of cognitive engagement. These findings offer pedagogical implications for reducing boredom in foreign language learning and optimising affective, behavioural, and cognitive engagement.

Society in the Covid-19 Pandemic: Inequalities, Challenges, and Opportunities Genel Sağlık Kitapları This book presents a collection of conceptual and research articles related to human communities during the COVID-19 Pandemic. The chapters in this book examine the modern societies from different perspectives. Our