This paper conducts in-depth research and analysis on the design and development of interactive courses for distance international Chinese language education using the cognitive heuristic model. The A-frame framework and JavaScript (JS) language are used to complete the construction of the virtual supermarket environment and initially design the basic framework of task-based courseware for Chinese language teaching based on virtual reality technology. Experts were invited to conduct two rounds of prestudy on the index system and determine the evaluation indexes. The international students in the junior Chinese class of X University were randomly selected as the research subjects and divided into experimental and control groups for the experimental intervention teaching. The content and form of classroom teaching were the same for both groups, and they were asked to review and consolidate the grammar points learned in class after class. The only intervention was that the experimental group was required to conduct microlearning after class with the classroom grammar points, while the control group was not required to do the same form of review after class. In addition, the students in the experimental group were followed up as a case study, and their attitudes, behaviors, and knowledge proficiency in the microlearning were recorded in detail to assess the students’ acceptance and satisfaction of the microlearning from the perspective of qualitative analysis. It was found that the microlessons for teaching and learning Chinese as a foreign language can stimulate learners’ interest in learning, achieve good interactive effects, and contribute to the improvement of learners’ performance. This paper combines virtual reality technology, fully explores the role that immersive VR technology can play in providing cultural scenarios in the target language, and maximizes the use of existing technological vehicles and resources for Chinese teaching application design, to solve the challenges of poor interactivity in online teaching and the lack of socio-cultural contexts that have long existed in overseas Chinese teaching.

This study proposes a novel approach for analyzing learning behaviors in Chinese language education by integrating generative attention mechanisms and generative state transition equations. This method dynamically adjusts attention weights and models real-time changes in students’ emotional and behavioral states, addressing key limitations of existing approaches. A central innovation is the introduction of a generative loss function, which jointly optimizes sentiment prediction and behavior analysis, enhancing the adaptability of the model to diverse learning scenarios. This study is based on empirical experiments involving student behavior tracking, sentiment analysis, and personalized learning path modeling. Experimental results demonstrate this method’s effectiveness, achieving an accuracy of 90.6%, recall of 88.4%, precision of 89.3%, and F1-score of 88.8% in behavioral prediction tasks. Furthermore, this approach attains a learning satisfaction score of 89.2 with a 94.3% positive feedback rate, significantly outperforming benchmark models such as BERT, GPT-3, and T5. These findings validate the practical applicability and robustness of the proposed method, offering a structured framework for personalized teaching optimization and dynamic behavior modeling in Chinese language education.