AI-Supported Decision-Making in Educational Policy and Scientific Administration

This paper outlines how mathematical modeling and artificial intelligence can be used to support scientific administration and educational policy-making. By supplementing the algorithmic capabilities of AI and analytical capabilities of quantitative models, the research facilitates better strategic planning, performance evaluation, and resource management. The proposed approach promotes data-driven, open, and responsive management practices. The article also touches on urgent issues such as model interpretability, ethics, and human-AI collaboration. It underscores the need for responsible innovation to bring about good governance and sustainable development of science and education institutions.