International technical regulation of training future specialists for the application of computer-integrated technologies in higher education

Abstract

The relevance of the study is determined by the ongoing digitalization of higher education, the increasing role of AI technologies in professional training, and the need to ensure their safe, ethical, and effective application. At the same time, the lack of specialized technical regulation in the educational environment, the fragmented nature of the regulatory framework, and the uneven institutional capacity of higher education institutions limit the systematic implementation of AI technologies and the alignment of national practices with international requirements.

The purpose of the article is to conduct a comparative analysis of international and national approaches to the technical regulation of training future specialists for the application of AI technologies in higher education in order to determine the level of their alignment, identify differences, and substantiate directions for improving the regulatory environment.

Methods. The study employs methods of theoretical analysis and generalization of scientific sources, systemic and comparative analysis of regulatory acts, international standards, and national strategic documents, as well as analytical interpretation methods to identify regulatory gaps and prospects for harmonization.

Results. International approaches to AI regulation in higher education have been investigated, and their systemic nature, including formalized procedures for risk assessment, algorithm transparency, and quality control, has been established. National regulation has been analyzed and found to be predominantly framework-based and dependent on general educational and information legislation. Key scientific and practical problems have been identified, including the absence of specialized technical regulations for the educational environment, insufficient formalization of procedures for assessing the quality and safety of AI systems, limited data governance mechanisms, risks of algorithmic bias, fragmented integration of AI into educational programs, and uneven levels of digital readiness among higher education institutions.

Conclusions. It has been proven that the effective implementation of AI technologies in higher education requires a transition from fragmented regulation to a comprehensive system aligned with international standards, including clearly defined procedures for risk assessment, quality assurance, and safety of AI systems.

Prospects for further research are associated with the development of national technical regulations for AI application in higher education, the creation of unified methodologies for assessing the quality and safety of AI systems, and empirical evaluation of their impact on learning outcomes and the transformation of educational practices.