Practical inspection and exploration of assistive positioning and generative artificial intelligence empowering civil trials

Authors

DOI:

https://doi.org/10.71451/ISTAER2518

Keywords:

Artificial intelligence; College students; Psychological evaluation; Psychological intervention; Multimodal model

Abstract

With the rapid development of artificial intelligence technology, the application of AI in the judicial field is gradually expanding, especially in civil trials, where artificial intelligence auxiliary positioning and generative artificial intelligence technology show great potential. Through intelligent case analysis, legal text retrieval, case matching, and document generation, artificial intelligence can effectively improve judicial efficiency, ensure the quality of judgments, reduce the workload of judges, and promote judicial fairness. However, the application of artificial intelligence in the judicial field still faces challenges such as technical adaptation, data privacy, and ethical issues. This paper aims to explore how auxiliary positioning and generative artificial intelligence can empower civil trials, and reveal its potential and development prospects by analyzing the current application status, technical principles, practical effects, and challenges of AI in civil trials. Studies have shown that the introduction of artificial intelligence can improve judicial efficiency and consistency of judgments, but it also poses new challenges to the current judicial system and legal framework. In the future, with the continuous advancement of technology and the improvement of policy frameworks, artificial intelligence will play an increasingly important role in civil trials and contribute to the intelligent transformation of the judicial field.

References

[1] Luleci, F. (2024). Investigating emerging technologies in civil structural health monitoring: Generative artificial intelligence and virtual reality.

[2] Zhao, T., Chen, G., Gatewongsa, T., & Busababodhin, P. (2025). Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision. Research on World Agricultural Economy, 207-221. DOI: https://doi.org/10.36956/rwae.v6i1.1429

[3] Lian, Y., & Yang, Y. (2024). Development and application of intelligent judicial trial assistance system based on generative artificial intelligence and machine learning technology. Applied and Computational Engineering, 75, 223-229. DOI: https://doi.org/10.54254/2755-2721/75/20240542

[4] Wang, Z., Zhang, Z., Su, T., Ding, Z., & Zhao, T. (2024, December). Research on Supply Chain Network Optimisation Based on the CNNs-BiLSTM Model. In 2024 International Conference on Information Technology, Comunication Ecosystem and Management (ITCEM) (pp. 197-202). IEEE. DOI: https://doi.org/10.1109/ITCEM65710.2024.00044

[5] Yadav, B. (2025). Artificial Intelligence in Forensic Science: Navigating Ethical Frontiers and Transformative Applications. In Generative AI Techniques for Sustainability in Healthcare Security (pp. 175-194). IGI Global Scientific Publishing. DOI: https://doi.org/10.4018/979-8-3693-6577-9.ch010

[6] Zhao, Z., Bao, Z., Zhang, Z., Deng, J., Cummins, N., Wang, H., ... & Schuller, B. (2019). Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders. IEEE Journal of Selected Topics in Signal Processing, 14(2), 423-434. DOI: https://doi.org/10.1109/JSTSP.2019.2955012

[7] Kapoor, N. R., Kumar, A., Kumar, A., Kumar, A., & Arora, H. C. (2024). Artificial intelligence in civil engineering: An immersive view. In Artificial Intelligence Applications for Sustainable Construction (pp. 1-74). Woodhead Publishing. DOI: https://doi.org/10.1016/B978-0-443-13191-2.00009-2

[8] Chen, G., Chutiman, N., Zhao, T., Panta, C., & Busababodhin, P. (2024). Identification of Earthquake Source Attributes Based on DAPSO-BP Combined Model. Lobachevskii Journal of Mathematics, 45(12), 6271-6285. DOI: https://doi.org/10.1134/S1995080224607537

[9] Zhao, X. W., Tong, X. M., Ning, F. W., Cai, M. L., Han, F., & Li, H. G. (2025). Review of empowering computer-aided engineering with artificial intelligence. Advances in Manufacturing, 1-41. DOI: https://doi.org/10.1007/s40436-025-00545-0

[10] Chiaro, D., Qi, P., Pescapè, A., & Piccialli, F. (2025). Generative AI-Empowered Digital Twin: A Comprehensive Survey With Taxonomy. IEEE Transactions on Industrial Informatics. DOI: https://doi.org/10.1109/TII.2025.3540473

Downloads

Published

2025-05-01 — Updated on 2025-05-01

Issue

Section

Research Article

How to Cite

Practical inspection and exploration of assistive positioning and generative artificial intelligence empowering civil trials. (2025). International Scientific Technical and Economic Research , 23-35. https://doi.org/10.71451/ISTAER2518

Similar Articles

1-10 of 70

You may also start an advanced similarity search for this article.