Pedagogical Applications of Artificial Intelligence for Environmental Disciplines: A Systematic Review for Higher Education

Abstract

The integration of artificial intelligence (AI) with teaching activities has become a hotspot in education, offering transformative potential for improving learning outcomes. This review aims to examine the pedagogical integration of AI within environmental disciplines for higher education, analyzing its practical scenarios, merits and drawbacks. Literature was retrieved from Web of Science and Scopus, with 10 qualified studies selected covering environmental science and engineering disciplines. Building upon this synthesis, supplemented with analogous cases from related disciplines (e.g., basic chemistry and engineering courses) that are also transferable to environmental education, the expanded discussion identified typical AI applications covering virtual assistants, adaptive learning, virtual experiments and intelligent evaluation. Though limited by insufficient empirical data and publication bias, findings suggest that AI holds transformative potential for environmental education, especially in enhancing personalized learning experiences, boosting teaching efficiency, increasing student engagement, and enabling data-driven instructional decision-making. However, limitations such as insufficient data quality, ethical and academic integrity concerns, uneven technical accessibility, and lack of long-term empirical validation were also consistently reported. More rigorous, longitudinal, and discipline-specific research is required to validate the observed benefits. Corresponding suggestions are put forward to better balance generic technological advancement and deep integration with discipline-specific teaching practice.