This paper discusses the associative meaning in social and cultural context. It introduces the seven types of meaning and its definition on associative meaning put forward by Leech. Associative meaning is highly characterized by culture and influenced greatly by society. Words sharing the same meaning may have their respective associative meaning in different social context, and produce different communicative effect. Therefore, exploring the associative meaning is helpful in cross-culture communication.

ChatGPT has shown considerable potential for Automated Item Generation, but the quality of ChatGPT-generated items in language assessment remains insufficiently substantiated. This research recruited 121 participants to systematically compare the psychometric properties of the test items and the linguistic features of the reading passages in ChatGPT-generated and official CET-4 reading comprehension materials, using Item Response Theory and Coh-Metrix. Key findings are as follows: (1) generated items fell short in higher-order reading skills; (2) the generated items were less difficult than official ones, showing weaker discrimination and providing measurement information mainly for lower-performing students; (3) only 22.9% distractors functioned effectively, indicating insufficient distractor performance; and (4) ChatGPT-generated passages were characterized by irregular lexical distribution, higher lexical complexity, weaker cohesion but simpler sentences than CET-4 passages. Although ChatGPT-generated passages were less readable than CET-4 passages, the corresponding items were easier and showed lower discrimination. This discrepancy can be attributed to inadequate distractor functioning that facilitates option elimination without complete passage comprehension, as well as to the underrepresentation of higher-order reading skills. The findings corroborate the conclusion that ChatGPT may function effectively as a supplementary tool in low-stakes assessment; however, substantial refinements in item quality are imperative before its application in high-stakes testing.

Generative artificial intelligence (GenAI) has been rapidly integrated into higher education, yet current studies largely rely on quantitative frameworks that capture initial adoption, leaving university EFL teachers’ motivational evolution for continued use of GenAI underexplored. To address this gap, this longitudinal qualitative study explores the motivational evolution and psychological need experience of 15 university EFL teachers’continued use of GenAI grounded in Self-Determination Theory (SDT). Data were collected through two rounds of semi-structured interviews over one year and analyzed in NVivo 11 PLUS. Our findings reveal a dynamic evolution of motivation from external regulation to value identification and professional identity integration. Crucially, need satisfaction and need frustration provide important psychological foundations for teachers’ motivation internalization. Notably, while need satisfaction directly facilitates university EFL teachers’ continued use of GenAI, need frustration can actually act as a potential catalyst that facilitates EFL teachers to form long-term continued use of GenAI through iterative reflection and instructional adjustment. This study extends the application of SDT within the GenAI education context, indicating that university EFL teachers’ continued use of GenAI is not merely a process of technology adoption, but also a process of professional growth and professional identity integration. The results provided preliminary implications for teacher professional development, GenAI design and education policy.

Despite a burgeoning body of research on using AI scoring systems in English assessments, concerns regarding their reliability persist. To fill this gap, this meta-analysis examined the AI-human scoring differences and the variables moderating these differences by synthesizing the results of 21 empirical studies with a total of 401,698 participants. Results indicate no statistically significant differences between AI and human scoring; the small effect size implies that the average systematic difference between the two was relatively modest. However, extremely high heterogeneity suggests that this overall finding masks considerable variability across study conditions. Moderator analyses reveal that AI-human scoring differences are significantly influenced by factors such as AI system type, number of human raters, agreement index employed, learner proficiency level (CEFR), and publication year. These findings suggest that while AI cannot fully replace human judgment, it can serve as a diagnostic reference tool within broader quality assurance frameworks. When significant discrepancies arise, they warrant investigation of both scoring sources. Based on these findings, this study offers evidence-based recommendations for educators on the effective use of AI scoring systems in language assessments.