Analysis of the Fusion Path between TBLT and Artificial Intelligence in Chinese Writing Instruction

Abstract

The core of task-based language learning (TBLT) is guided learning through task chains, emphasizing the use of language in specific contexts. This is particularly relevant to the need for practical application in Chinese writing—whether it's practical writing like class notices and community initiatives, or creative writing like coming-of-age narratives and commentary on campus hot topics. However, its implementation presents numerous challenges: task design is often one-size-fits-all, leaving students with strong foundations feeling uninspired and unable to learn, while those with weaker foundations struggle to keep up. The content is often disconnected from students' real needs, and feedback on writing is often slow. Fortunately, educational technology can help. For example, iFlytek's text analysis tools can clarify the logic of essays, and its virtual situation system can simulate scenarios like "campus interviews" and "public welfare proposals." This article, drawing on the practice of a junior high school in Jiangsu Province, proposes a framework for integrating TBLT with technology: precise task design, personalized instruction, and multi-dimensional feedback. It also addresses the issues of technology adaptation, teacher training (e.g., East China Normal University's AI training model for rural teachers), and ethical considerations. Research shows that when teachers, students and technology work well together, Chinese writing efficiency can be increased by more than 30%, articles can be more coherent and accurate, and teaching can be promoted from "transmitting knowledge" to "cultivating reading and writing literacy."