This study investigated how machine translation (MT) and generative AI (GenAI) differentially influence multiple dimensions of L2 writing anxiety among 146 young Japanese EFL learners. Using pre-post data from the second language writing anxiety inventory (SLWAIr), the analyses showed that both MT and GenAI reduced somatic anxiety and avoidance behavior, but neither tool produced group-level differences. GenAI, however, was accompanied by a non-significant increase in appraisal concerns and a significant increase in communication apprehension, whereas MT did not. These contrasting within-group patterns indicate that AI tools do not uniformly alleviate writing anxiety but instead redistribute its internal structure across subcomponents. Regression analyses further revealed that post-test anxiety was most strongly predicted by avoidance behavior and communication apprehension, underscoring the need to examine multidimensional profiles rather than relying on global scores. The findings contribute to a more nuanced understanding of the affective mechanisms underlying AI-assisted writing and highlight the importance of selective, guided integration of AI tools in classroom contexts. Limitations related to the lack of usage data and the short-term design are noted, along with directions for future research.

This study examined the comparative effectiveness of corpus-based data-driven learning (DDL; Linguee) and artificial intelligence (AI)-assisted DDL (ChatGPT) among 69 Japanese university EFL learners. Both approaches produced comparable learning gains, with no significant difference between groups after controlling for pretest performance. However, proficiency emerged as a key moderating factor: intermediate-level learners achieved greater improvements than low-proficiency learners. Learner perceptions, assessed through the technology acceptance model (TAM), indicated higher ratings of perceived ease of use, behavioral intention, and overall preference for AI-assisted DDL. These findings underscore the importance of aligning DDL implementation with learner proficiency and technology acceptance. AI-assisted tools such as ChatGPT offer accessible, engaging alternatives to traditional corpus-based methods, broadening opportunities for inductive, data-driven language learning.

This qualitative study examines faculty course design choices for on-demand classes in post-COVID higher education in Japan. Through interviews with four faculty members, the researchers found that instructors implemented on-demand formats to address previous teaching challenges and develop new approaches. These classes featured more learner-centered instruction with enhanced active learning through self-study activities, flexible pacing, and multimedia resources. Faculty shifted to formative assessment methods with regular feedback, particularly in large lectures. Challenges included providing adequate feedback, assessment issues, and recording difficulties, suggesting the need for improved faculty development support and recording facilities. © 2025 IEEE.