Integrating AI in environmental education: a quality-focused framework for higher education institutions in emerging markets

Abstract

This paper aims to propose a five-stage, quality-focused framework to guide higher education institutions in emerging markets through the integration of artificial intelligence (AI) into environmental education. It addresses a gap that existing frameworks have not filled: how institutions operating under resource constraints can adopt AI tools while preserving the experiential, place-based and community-connected foundations on which environmental pedagogy depends. Drawing on experiential learning theory, education for sustainable development principles and comparative quality assurance research, this conceptual study synthesises literature across AI in education, environmental pedagogy and institutional quality systems. The framework identifies three fundamental tensions that institutions must methodically manage: direct experience versus technological mediation, local ecological knowledge versus standardised data systems and sustainability goals versus technology resource demands. Embedding quality assurance throughout all implementation stages proves essential for preserving environmental education’s experiential foundations while harnessing AI capabilities in resource-constrained settings. The study theorises “technological–ecological balance” as a design principle, extends experiential learning theory to incorporate AI-mediated ecological observations and establishes evaluation criteria for authentic engagement in technology-mediated learning. It provides actionable guidance for institutions, policymakers and corporate partners pursuing environmental management innovations in higher education.