Article: Temporal edges application of the ST-GCN algorithm in logical consistency assessment of academic English writing Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.31 pp.44 - 61 Abstract: Assessing logical consistency in academic writing is challenging. Traditional methods relying on shallow features struggle to capture deep semantic logic and textual structure. This study proposes using spatiotemporal graph convolutional networks (ST-GCN) for this task. Texts are modelled as graphs where nodes are sentences; spatial edges represent static logical links like semantic similarity, and temporal edges model dynamic sequential dependencies. Trained on 12,000 academic texts from journals and student papers across disciplines, the model captures logical consistency from micro to macro levels. In evaluation, it achieved accuracy rates of 92.5% for global consistency, 89.3% for local consistency, 91.7% for lexical cohesion, and 93.2% for argumentative consistency. It achieved a Pearson correlation of 0.89 with human evaluation and a mean absolute error below 0.15, significantly outperforming baselines and offering an effective path for automated assessment. Inderscience Publishers - linking academia, business and industry through research

Title: Temporal edges application of the ST-GCN algorithm in logical consistency assessment of academic English writing

Authors: Jiaqi Yin

Addresses: School of Art and Science, Chengdu College of University of Electronic Science and Technology of China, Chengdu 611731, China

Abstract: Assessing logical consistency in academic writing is challenging. Traditional methods relying on shallow features struggle to capture deep semantic logic and textual structure. This study proposes using spatiotemporal graph convolutional networks (ST-GCN) for this task. Texts are modelled as graphs where nodes are sentences; spatial edges represent static logical links like semantic similarity, and temporal edges model dynamic sequential dependencies. Trained on 12,000 academic texts from journals and student papers across disciplines, the model captures logical consistency from micro to macro levels. In evaluation, it achieved accuracy rates of 92.5% for global consistency, 89.3% for local consistency, 91.7% for lexical cohesion, and 93.2% for argumentative consistency. It achieved a Pearson correlation of 0.89 with human evaluation and a mean absolute error below 0.15, significantly outperforming baselines and offering an effective path for automated assessment.

Keywords: logical consistency assessment; academic English writing; spatiotemporal graph convolutional networks; ST-GCN; automated writing assessment; discourse structure analysis.

DOI: 10.1504/IJICT.2026.152856

International Journal of Information and Communication Technology, 2026 Vol.27 No.31, pp.44 - 61

Received: 20 Nov 2025
Accepted: 07 Jan 2026
Published online: 13 Apr 2026 *

Title: Temporal edges application of the ST-GCN algorithm in logical consistency assessment of academic English writing

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