Enhancing Environmental Education and Ecological Systems Sustainability in Higher Education through Predictive Artificial Intelligence Modelling

Abstract

Environmental degradation, intensifying climate instability and Artificial Intelligence (AI) utilization of technologies are restructuring the higher education (HE) sector in grooming scholars for sustainable futures. Higher Educational Institutions (HEIs) are saddled with the responsibility of providing environmentally and technologically competent HE capable of addressing the complex socio-ecological challenges as ecosystems approach irrevocable tipping points. Inspite of the rapid developments in AI-driven environment laboratories, sustainability curricula in HEIs are not sufficiently incorporated with predictive ecological intelligence and data-driven learning skills. It is on this premise that the current study investigates how predictive AI modelling, as well as deforestation estimations, biodiversity monitoring, carbon sink modelling and water stress simulations can enhance environmental education research and ecosystems sustainability in HEI spaces. Guided by the Complexity Theory, the Systematic Literature Review methodology with PRISMA protocols was adopted for the study. The findings brought to the fore the enhancement of interdisciplinary engagement and enhanced students’ capacity in addressing complex environmental phenomenon and datasets for sustainability decision-making through the sterling applications of AI-supported learning environments. It is also germane to elucidate the persistent challenges such as inadequate digital infrastructure, limited lecturer competences, associated algorithmic transparency/ethics challenges, as well as unequal access to AI technologies across the Global South HEIs. In essence, this study advances HE by promoting AI literacy, ecological awareness, sustainability competences, and ensures futures-oriented environmental capacity building as entrenched in SDGs 4 and 13.