Dynamic Quality Assessment of Chinese Language Courses: Building Smart Education Systems with Generative AI and Deep Behavioral Analytics

The fast-growing availability of online education has shifted the way Chinese is learned, and quality assurance in courses continues to be a problem. Traditional methods of evaluation are often subjective and labor-intensive. This study examined the value of using big data analytics to help assure quality in online Chinese education by developing a model that incorporates participation rates, response times and participation. Empirical analysis has validated the model as an effective way of diagnosing defects in curricula and making useful recommendations. This study provides critical insights into the development of data-driven quality assurance frame-works that facilitate educators and platform developers in improving instructional techniques and customizing learning experiences.