Article: Intelligent human-computer collaborative scoring system for English writing using large-scale model representation learning and knowledge reasoning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.93 pp.64 - 82 Abstract: Traditional manual English writing scoring is inefficient and subjective, while existing automated systems lack semantic understanding and instructional interpretability. This study proposes an intelligent human-computer collaborative scoring system integrating large-scale model representation learning with knowledge reasoning. The system combines representation, reasoning, and collaboration through a fine-grained feature learning algorithm and a domain knowledge map, enabling multidimensional evaluation and traceable scoring decisions. Experimental results on 8,800 English compositions demonstrate that the proposed system achieves 91.7% scoring accuracy (36.8% higher than traditional models), 72% lexical error detection coverage with a 5.2% omission rate, and a human-computer semantic consistency coefficient (SCC) of 43% in evaluating high-level dimensions like semantic coherence and argumentation logic. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent human-computer collaborative scoring system for English writing using large-scale model representation learning and knowledge reasoning

Authors: Lulu Hao

Addresses: School of Foreign Languages, Luoyang Normal University, Luoyang, 471000, China

Abstract: Traditional manual English writing scoring is inefficient and subjective, while existing automated systems lack semantic understanding and instructional interpretability. This study proposes an intelligent human-computer collaborative scoring system integrating large-scale model representation learning with knowledge reasoning. The system combines representation, reasoning, and collaboration through a fine-grained feature learning algorithm and a domain knowledge map, enabling multidimensional evaluation and traceable scoring decisions. Experimental results on 8,800 English compositions demonstrate that the proposed system achieves 91.7% scoring accuracy (36.8% higher than traditional models), 72% lexical error detection coverage with a 5.2% omission rate, and a human-computer semantic consistency coefficient (SCC) of 43% in evaluating high-level dimensions like semantic coherence and argumentation logic.

Keywords: large model representation learning; knowledge reasoning; English writing scoring; human-computer collaborative; explainable artificial intelligence.

DOI: 10.1504/IJICT.2026.155870

International Journal of Information and Communication Technology, 2026 Vol.27 No.93, pp.64 - 82

Received: 08 Apr 2026
Accepted: 13 May 2026
Published online: 24 Aug 2026 *

Title: Intelligent human-computer collaborative scoring system for English writing using large-scale model representation learning and knowledge reasoning

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