Validity Study of Artificial Intelligence Tools in Chinese Heritage Language Learners' Writing Assessment

The application of artificial intelligence technology in education has brought about profound paradigm shifts in the field of language teaching. This paper focuses on the writing assessment system for Chinese heritage language learners, revealing through empirical comparative analysis the structural differences between intelligent assessment mechanisms and traditional teacher assessment in terms of feedback timeliness and error tolerance. The research results show that teacher assessment tends to be based on a comprehensive judgment of the diachronic learning trajectory. In contrast, machine learning models demonstrate strong explanatory power for surface language features. Based on the theory of learning motivation, this study suggests establishing a collaborative framework of AI tools and human assessment in writing courses, achieving a stepwise improvement in learners' self-efficacy through real-time grammar correction and visualization of personalized progress trajectories.