An Immersive Japanese Learning System Using Multimodal AI for Improved Engagement and Skills

This study presents the design and evaluation of an immersive Japanese language learning system based on multimodal artificial intelligence to address limitations of traditional learning environments, including minimal interaction and delayed feedback. The system integrates speech, visual, and emotion-recognition technologies to enhance learner engagement through context-aware interactions and adaptive content delivery. The study examines key multimodal fusion techniques, identifies limitations in existing models, and optimizes algorithm design and interaction patterns accordingly. Empirical results indicate that the proposed system significantly improves oral accuracy, learning motivation, listening and speaking skills, and overall learning engagement, confirming its effectiveness and scalability. This work provides a practical framework for the development of immersive language learning systems and offers insights for future research on personalized and interactive artificial intelligence–supported education.