How to Measure AI-assisted Applied Chinese Writing: Mitigate Cognitive Load Offloading

With the widespread adoption of generative AI in language education, scholars have increasingly recognized their side effects in AI-assisted writing, particularly cognitive offloading—where students over-rely on AI to fulfil higher-order cognitive tasks. Traditional writing assessment tasks and frameworks, which are predominantly product-oriented, struggle to distinguish between students’ inherent writing competence, AI-augmented outputs, and genuine academic achievement. This dilemma is more prominent in applied Chinese writing, as it emphasizes practical communication and contextual adaptation, making it harder to identify the boundary between students’ independent performance and AI assistance, thereby hindering accurate evaluation of their true competencies. Grounded in cognitive load theory and process-oriented assessment principles, this research proposes a three-dimensional adaptive assessment strategy tailored to applied Chinese writing. The strategy includes: process-focused assessment with multi-source data tracking (e.g., draft revisions, AI tool usage logs, and reflective narratives), assessment criteria centered on AI-irreplicable higher-order competencies (critical thinking, contextual adaptability, and cultural connotation expression), and AI-adaptive tasks designed to prompt active cognitive engagement. Additionally, this study addresses key implementation barriers, such as standardized data collection and ambiguous AI usage guidelines, and puts forward corresponding solutions to enhance practicality. By bridging the gap between AI technology and applied Chinese writing assessment, this research offers actionable insights for educators, assessment designers, and language education researchers. It advances AI-aligned writing assessment practices and aligns closely with conference themes on technology-integrated language education, contributing to the sustainable development of AI-augmented language teaching.