The Mechanism Design of an AI-Based Evaluation Model for Teaching Chinese as a Foreign Language

This study focuses on improving teaching efficiency and enhancing learner engagement in Chinese as a Foreign Language (CFL) education through the application of AI-driven evaluation models. By leveraging data mining techniques, the proposed approach prioritizes recommendations according to user interest weights and integrates an adaptive feedback system. The research methodology includes four key stages: data collection, data processing, evaluation, and feedback. Data is collected from diverse learner and teacher activities, processed for accuracy, and analyzed using AI algorithms to evaluate both teaching effectiveness and learner performance. Tailored feedback and actionable suggestions are then provided to refine instructional strategies and learning outcomes. This adaptive system equips teachers with performance insights while designing personalized learning pathways for students, fostering an interactive and effective educational environment. Ultimately, this AI-powered model represents a significant advancement in the teaching of CFL, offering a dynamic framework for optimized educational practices.