A Dual-Path Gated Attention-Based Deep Learning Model for Automated Essay Scoring Using Linguistic Features

Automated Essay Scoring (AES) has become a critical tool for scaling writing assessment in modern education. However, existing AES models often struggle to effectively evaluate both the syntactic structure and semantic meaning of essays while maintaining interpretability and fairness. This study presents a novel deep learning-based model that integrates syntactic and semantic analysis using an improved LSTM architecture. The model employs a dual-path structure: one path processes semantic representations using BERT-tokenized input, while the other captures syntactic patterns via part-of-speech sequences. These paths are fused using a gated mechanism and enhanced through multi-head attention to emphasize important linguistic cues. Additional student metadata, such as grade level and gender, is also incorporated to improve personalization and fairness. The model jointly predicts both holistic and grammar scores, trained and evaluated on the ASAP 2.0 dataset. Performance is measured using multiple statistical metrics, including MAE, MSE, RMSE, R², Pearson’s r, and Spearman’s ρ. The proposed model achieves a high prediction accuracy of 92%, significantly outperforming traditional and single-path models. These results demonstrate the model’s ability to capture both surface-level and deep linguistic features, offering a robust, interpretable, and scalable solution for automated writing evaluation.