For a long time, Chinese language teachers in primary and secondary schools have been confronting challenges of heavy workload, low efficiency, and difficulty in improving the quality of composition evaluations. This article introduces “ELion”, an intelligent Chinese composition tutoring system based on large language models. The system utilizes deep linguistic features to evaluate the quality of compositions and provide interpretable feedback. By discussing the overall design, evaluation framework structure, and scoring algorithm principles of ELion, this paper addresses the theoretical, technical, and engineering issues of intelligent evaluation of Chinese compositions in the educational context. Small-scale experiments conducted in schools demonstrate that ELion performs well in language error detection, rhetorical techniques, and the expression of actions and emotions. It can basically meet the needs of Chinese language teaching in primary and secondary schools. In the future, ELion will further develop algorithms for ”instruction-learning-evaluation” alignment assessment, and personalized precise feedback generation, based on the GPT model. This will improve the evaluation effectiveness in topic analysis, text structure, and genuine emotional expression. Additionally, systematic field experiments for the system will be conducted to explore the application of artificial intelligence in education.
Zheng, Chanjin; Guo, Shaoyang; Xia, Wei; and Mao, Shaoguang
"ELion: An Intelligent Chinese Composition Tutoring System Based on Large Language Models,"
Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊: Vol. 4:
3, Article 3.
Available at: https://www.ce-jeme.org/journal/vol4/iss3/3