Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment

The study on leveraging small open-source Large Language Models (LLMs) for argument mining in education highlights a promising approach to enhance students' argumentative skills. By utilizing the Feedback Prize - Predicting Effective Arguments dataset, the research focuses on grade 6-12 students, demonstrating how fine-tuned small LLMs can outperform baseline methods in tasks such as segmentation, classification, and assessment of arguments. This method not only ensures computational efficiency and accessibility but also allows educators to provide real-time, personalized feedback, which is crucial for fostering effective learning environments. The positive implications of this research extend beyond mere academic performance, as it opens new avenues for integrating AI technologies in educational settings, ultimately aiming to improve student engagement and understanding of argumentative writing.