Design and Application of a Hybrid Deep Learning Platform for Korean Multimedia Teaching Courses

The evolution of web technologies has profoundly shifted language learning, with the need for more interactive and immersive platforms for proper education. A multimedia-driven Korean education platform provides an interactive model by merging audio-visual content, interactive quizzes, real-time feedback, and user-adaptive learning modules for rich linguistic proficiency. Current systems tend to be non-personalized, lack multilingual capabilities, and provide non-real-time learner analytics, resulting in poor learner engagement and optimal learning results. The conventional methods were dependent on static content delivery or mobile apps without end-to-end multimedia integration. With the proposed model, a Web-Based Intelligent Multimedia Learning System based on a hybrid architecture integrating Natural Language Processing (NLP) and Convolutional Neural Networks (CNN) is used for audio-text alignment, emotion-based content recommendation, and real-time evaluation. A comparative evaluation affirms remarkable improvements in performance over traditional systems, registering 98.5% accuracy and 97.2% F1-score in learner response assessment and comprehension monitoring. Performance excellence is credited to the adaptive content delivery, interactive user interface, and feedback-driven progression engine. The platform enables a personalized and scalable Korean learning experience aligned with varied learner profiles.