Leveraging AI and data analytics for sustainable education management in health and life sciences: a data-driven framework

Abstract

In an era of rapidly evolving healthcare and life science demands, educational systems must respond with agility, relevance, and sustainability. This paper explores how artificial intelligence (AI) and data analytics can support sustainable education management through better curriculum planning, research alignment, and workforce forecasting. Using simulated data modeled on global education indicators and curriculum repositories, we analyzed tertiary education attainment, curriculum content distribution, and projected workforce demand in digital health and life sciences. Clustering, correlation modeling, and time-series analysis were used to classify countries by educational capacity and identify critical gaps. The results show that countries like China, while improving in access, lag in modern interdisciplinary training. A data-driven framework is proposed to support dynamic curriculum adaptation and informed policy-making. These findings contribute actionable insights for global institutions and policy leaders aiming to build resilient, future-ready education systems in health-related disciplines.

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