Toward Sustainable Education: Generative AI-Powered Argument Mining in Student Writing

As critical elements in argumentative writing, argument components and strategies significantly influence argument quality. However, the existing research lacks an in-depth exploration of how students construct and utilize these elements in argumentative writing. This study first evaluates the performance of leading large language models (LLMs) in identifying argument components and strategies using three approaches: single-task learning (STL), chain-of-thought (CoT), and multi-task learning (MTL). With the aid of learning analytics methods (Epistemic Network Analysis (ENA) and two-mode network), the study further reveals the intrinsic mechanisms linking argument components, strategies, and writing quality. Specifically, the research trains and evaluates LLMs on 226 argumentative essays, encompassing 4726 components and 4837 strategies. Compared to basic STL, the CoT and MTL methods significantly improve LLMs’ performance in both tasks. Moreover, learning analytics indicate that high-quality essays possess rich and complex logical relations, presenting multidimensional and multi-layered reasoning structures, whereas low-quality essays predominantly rely on simple and repetitive connections, lacking deeper logical support. These findings have significant implications for the automated analysis of argumentative writing and the sustainable development of education, not only providing valuable insights for educators in argumentation instruction but also contributing to the systematic enhancement of students’ argumentative abilities and critical thinking.