A Review of the Application and Progress of Deep Learning in Automatic Essay Grading Systems
DOI:
https://doi.org/10.53469/jtpms.2025.05(4).01Keywords:
Deep learning, Automatic grading of essays, Natural language processing, Artificial intelligenceAbstract
With the rapid development of technology, deep learning technology has demonstrated excellent application effects in many fields due to its powerful feature learning and representation capabilities. In the automatic essay grading system, the application of deep learning is becoming increasingly important. This article aims to comprehensively review the latest applications and significant progress of deep learning in automatic essay grading in recent years. The current automatic essay grading technology still faces some challenges, such as the accuracy of cross language and cross-cultural grading, as well as the stability and reliability of the grading system. We have conducted a thorough analysis of these issues and proposed possible solutions. Looking ahead to the future, we expect deep learning technology to play a greater role in the field of automatic essay grading, promoting the intelligence and precision of educational evaluation.
References
Xiangyu, G., Yao, T., Gao, F., Chen, Y., Jian, X., & Ma, H. (2024). A new granule extrusion-based for 3D printing of POE: studying the effect of printing parameters on mechanical properties with “response surface methodology”. Iranian Polymer Journal, 1-12.
Chen, K., Zhao, S., Jiang, G., He, Y., & Li, H. (2025). The Green Innovation Effect of the Digital Economy. International Review of Economics & Finance, 103970.
Meng, Q., Wang, J., He, J., & Zhao, S. (2025). Research on Green Warehousing Logistics Site Selection Optimization and Path Planning based on Deep Learning.
Wang, Y., Yang, T., Liang, H., & Deng, M. (2022). Cell atlas of the immune microenvironment in gastrointestinal cancers: Dendritic cells and beyond. Frontiers in Immunology, 13, 1007823.
Li, X., Wang, J., & Zhang, L. (2025). Gamifying Data Visualization in Smart Cities: Fostering Citizen Engagement in Urban Monitoring. Authorea Preprints.
Song, X. (2025). Improving User Experience in E-commerce Through Intelligent Demand Forecasting and Inventory Visualization.
Wang, J. (2025). Bayesian Optimization for Adaptive Network Reconfiguration in Urban Delivery Systems.
Li, T. (2025). Enhancing Adverse Event Monitoring and Management in Phase IV Chronic Disease Drug Trials: Applications of Machine Learning.
Li, X., Wang, J., & Zhang, L. (2025). Named entity recognition for smart city data streams: Enhancing visualization and interaction. Authorea Preprints.
Yang, J. (2025). Application of LightGBM in the Chinese Stock Market.
Song, X. (2025). User-Centric Internal Tools in E-commerce: Enhancing Operational Efficiency Through AI Integration.