The development of artificial intelligence (AI) technology has enhanced the use of automated speech evaluation systems for language learners to practice speaking skills. This study investigated whether various automatic feedback offered by AI speech evaluation programs can help English as a foreign language (EFL) learners develop speaking skills. Forty EFL learners in China participated in this study. Data collection included qualitative and quantitative data. The results showed that the majority of participants believed they improved their speaking skills with the feedback offered by the AI speaking evaluation program. The findings also revealed that there were significant improvements in their mean scores of speaking skills in pre- and post-tests. Therefore, it is suggested that AI speaking evaluation systems could provide more varied textual feedback and practical suggestions to assist EFL learners in developing speaking skills.

Background: The integration of generative artificial intelligence (AI) into education has revolutionised teaching practices, offering educators advanced tools for lesson planning, content creation, personalised learning and administrative automation. While AI enhances efficiency and instructional effectiveness, concerns have emerged regarding teachers' potential overreliance on these technologies, leading to AI addiction. Objectives: This study applies the I‐PACE model (Interaction of Person‐Affect‐Cognition‐Execution) to explore the psychological and behavioural mechanisms underlying teachers' dependence on generative AI. Methods: Using survey data from 1750 teachers in Huanghua, China, the study examines factors such as self‐efficacy, need for cognition, mood regulation, positive affect, perceived usefulness and cognitive absorption in shaping AI addiction. Results: Findings indicate that cognitive absorption is the strongest predictor of AI dependence, while perceived usefulness, self‐efficacy and positive affect contribute indirectly through reinforcement mechanisms. Notably, mood regulation and need for cognition do not significantly influence AI addiction, suggesting that AI engagement in education is driven more by functional efficiency than emotional dependence. Conclusions: The results highlight the importance of fostering mindful AI integration in teaching to prevent habitual overreliance. This study provides theoretical contributions by extending the I‐PACE model to the context of AI addiction in education and offers practical insights for educators, institutions and policymakers in promoting responsible AI use while maintaining teachers' professional autonomy and cognitive engagement.

It is evident that sarcasm can be interpreted differently due to various factors, However, rare research was conducted to investigate the influence of national culture on sarcasm comprehension despite its valuable theoretical implication. This study used an online rating task to explore how national culture impacts the comprehension of sarcasm, focusing on the differences between Chinese and American cultural values (i.e., power distance, uncertainty avoidance, collectivism, long-term orientation, and masculinity) and their influence on comprehending sarcastic praise and criticism. The study showed that Chinese participants tend to understand sarcasm less than Americans. It also found that Power Distance is linked to better sarcasm comprehension in both cultures, while Uncertainty Avoidance has a negative effect on it, especially in Chinese participants. Collectivism is also associated with improved sarcasm comprehension, especially in Chinese participants. However, Masculinity and Long-Term Orientation do not seem to have a significant impact on sarcasm comprehension, regardless of nationality or the type of comment (praise or criticism). Overall, the study reveals nuanced differences in how cultural values shape the comprehension of sarcasm in Chinese and American contexts, underscoring the complex interplay between culture and communication.