A Generative Artificial Intelligence-Based Mechanism for Mobile Conversational Interaction in Japanese Language Learning

Japanese language learning has long been constrained by acquisition difficulties arising from linguistic distance and by the limited availability of authentic opportunities for conversational interaction. Although mobile learning technologies offer considerable potential to alleviate these constraints, existing tools remain largely focused on vocabulary acquisition and grammar practice, while the dynamic progression of learner–artificial intelligence conversational interaction has received insufficient attention. In this study, a mobile Japanese conversational interaction system powered by a large language model was developed and integrated with automatic speech recognition and speech synthesis interfaces. Multi-topic conversational environments were constructed for learners at different proficiency levels. A four-week experiment involving 76 learners of Japanese was conducted, and interaction logs, JapaneseLanguage Proficiency Test–aligned mock assessments, and expert ratings were collected to characterize trajectories of conversation deepening and examine their associations with language learning outcomes. Pseudotime trajectory inference based on a minimum spanning tree demonstrated high internal consistency and external validity, and the composite conversation-depth score accounted for 43.8% of the variance in learner performance. Within individual conversational sessions, conversation depth increased significantly as the number of interactions turns increased. Intermediate learners exhibited greater mean pseudotime progression, more branch points, and faster rates of conversation deepening than beginner learners. These findings demonstrate that temporal modeling of interaction trajectories can be used to represent conversation deepening in generative artificial intelligence–supported mobile Japanese language learning as a quantifiable dynamic process. This approach provides a quantitative basis for investigating mobile conversation-based learning mechanisms and informing the design of adaptive conversational interactions.