Environmental degradation has emerged as a critical global challenge with significant implications for public health and sustainable development. Among different population groups, women often experience disproportionate health impacts from environmental hazards due to biological vulnerability, socio-economic factors, and gendered roles that influence exposure to environmental risks. Environmental health education, therefore, plays an important role in promoting awareness, preventive behavior, and informed decision-making. Artificial intelligence (AI) has created new opportunities for analyzing environmental data, predicting health risks, and supporting innovative educational practices. This study explores the potential of integrating artificial intelligence into environmental health education to promote women’s health awareness through a gender sensitive educational framework. Using a conceptual and analytical research design, the study synthesizes existing literature from environmental studies, education, public health, and artificial intelligence research. The finding suggests that AI-based environmental monitoring systems can enhance environmental health literacy by transforming complex environmental data into accessible learning resources and enabling data-driven educational experiences. Integrating such technologies within educational curricula can strengthen preventive health awareness and promote gender inclusive learning environments. Gender-sensitive pedagogical approaches can empower women with knowledge about environmental risks and health protection strategies (Sorensen et al.,2018). The study proposes a link between environmental risk factors, AI-based monitoring systems, environmental health education, and gender-sensitive pedagogy to achieve health literacy and sustainable educational outcomes. Overall, the integration of artificial intelligence into environmental health education offers promising pathways for advancing women’s health awareness and supporting broader goals of sustainable development (Tilbury,2011; Zawacki-Richter et al.,2019).
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Ahmad, Ikhlas ; Khatoon, Rabiya ; Gardia, Alok
430-441
10.5281/zenodo.19391012
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