With the deepening opening-up of the Hainan Free Trade Port (HFTP), foreign-related industries such as cross-border trade and foreign investment have developed rapidly. New foreign-related economic disputes and compliance risks keep emerging, creating an urgent market demand for versatile and practice-oriented foreign-related legal talents. The teaching of Foreign-related Economic Law (FREL) in local universities has long prioritized theory over practice. Constrained by venue, case resources and time, traditional moot courts fail to adapt to the practical scenarios unique to HFTP, leading to a disconnect between talent training and job requirements. Centered on improving practical competence, this paper analyzes the pain points in current FREL teaching and the adaptability of the Artificial Intelligence-based Moot Court (AI Moot Court) in light of HFTP’s foreign-related legal development needs. It constructs an AI Moot Court teaching system tailored to HFTP’s characteristics, and designs a tiered training process, supporting resource mechanisms and a multi-dimensional assessment framework. The findings show that AI Moot Court can break through the limitations of traditional teaching, dynamically adapt to new foreign-related legal scenarios in HFTP, and effectively enhance students’ practical foreign-related legal skills. This study provides references for the digital teaching reform of foreign-related law in local universities and the cultivation of legal talents in HFTP.

Xiao Shurui, Mo Haipeng. Integrating AI Moot Court into the Teaching of Foreign-related Economic Law in the Hainan Free Trade Port from the Perspective of Practical Competence Improvement. International Journal of New Developments in Education (2026), Vol. 8, Issue 6: 16-21. https://doi.org/10.25236/IJNDE.2026.080603.

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