Deep Learning-Driven Automatic Generation and Compliance Testing of Educational Legal Instruments

抄録

The governance of education systems depends on legal instruments such as policies, regulations, and treaties that protect rights and ensure compliance. Traditionally, the drafting and validation of these instruments are done manually, which is often slow, labor-intensive, and prone to inconsistencies across different contexts. This paper proposes a deep learning-based EduLegal-DL framework for the automatic generation and compliance testing of educational legal instruments. The EduLegal-DL framework uses a transformer-based language model trained on international and national educational legal documents to generate draft instruments. A compliance-checking module, run from BERT-based classifiers, checks the drafts against parameters like inclusivity, equity, and compatibility with UNESCO conventions. Hyperparameter optimization and human feedback through reinforcement learning are used to enhance accuracy and the learning ability of the system so it can more effectively enhance legal phrasing and conduct checks to standard. The results indicate that the system correctly conducts compliance testing at 92% and decreases drafting time significantly in comparison to conventional techniques. The optimized framework also generates more standardized and versatile legal documents in various educational environments. In summary, the EduLegal-DL framework offers an efficient, precise, and scalable means of generating and verifying educational legal documents, with a robust basis to policymakers and institutions.