Challenges and Opportunities for Leveraging Generative AI for Sustainability Education: A Critical Review

The integration of generative artificial intelligence (Gen-AI) into sustainability education is justified by its potential to introduce sustainability perspectives through transformative learning. By encouraging individuals to critically reflect and challenge their prior beliefs and assumptions, Gen-AI can deepen their understanding of sustainability concepts and inspire long-term commitment to sustainable practices. While the broader educational potential of Gen-AI has been widely explored, previous research tends to overlook its specific benefits and implications within the context of sustainability education. This paper addresses this gap by exploring both the opportunities and challenges of employing Gen-AI in the context of sustainability education through a critical review of diverse outputs. A thematic analysis of the outputs reveals a complex interplay between the opportunities and challenges. While Gen-AI offers access to information, personalised learning, fosters creativity, and decision-making support, the associated challenges, such as unequal access, overreliance on use, unreliable outputs, and environmental cost, may undermine the opportunities and the broader efforts to foster sustainability. The originality of this paper lies in providing critical insights for institutions, educators, and policymakers seeking to harness generative AI to advance sustainability education, an area pivotal to the pursuit of a just and sustainable future.

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Publication Details

Journal
Sustainability
Published
2025-11-26
DOI
https://doi.org/10.3390/su172310623
Citations
2
Primary Topic
Sustainability in Higher Education
Type
article
Field-Weighted Citation Impact
17.85

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article

Challenges and Opportunities for Leveraging Generative AI for Sustainability Education: A Critical Review

Iryna Kushnir, Marcellus Forh Mbah, Tsamarah Rana Nugraha
2 citations
Sustainability
Sustainability in Higher Education
17.85
article

Challenges and Opportunities for Leveraging Generative AI for Sustainability Education: A Critical Review

Iryna Kushnir, Marcellus Forh Mbah, Tsamarah Rana Nugraha
article en
2 citations

Abstract

The integration of generative artificial intelligence (Gen-AI) into sustainability education is justified by its potential to introduce sustainability perspectives through transformative learning. By encouraging individuals to critically reflect and challenge their prior beliefs and assumptions, Gen-AI can deepen their understanding of sustainability concepts and inspire long-term commitment to sustainable practices. While the broader educational potential of Gen-AI has been widely explored, previous research tends to overlook its specific benefits and implications within the context of sustainability education. This paper addresses this gap by exploring both the opportunities and challenges of employing Gen-AI in the context of sustainability education through a critical review of diverse outputs. A thematic analysis of the outputs reveals a complex interplay between the opportunities and challenges. While Gen-AI offers access to information, personalised learning, fosters creativity, and decision-making support, the associated challenges, such as unequal access, overreliance on use, unreliable outputs, and environmental cost, may undermine the opportunities and the broader efforts to foster sustainability. The originality of this paper lies in providing critical insights for institutions, educators, and policymakers seeking to harness generative AI to advance sustainability education, an area pivotal to the pursuit of a just and sustainable future.

SustainabilityVol. 17(23)
University of Manchester (GB), Space Research Institute (UA), Nottingham Trent University (GB)
Research England
Openalex Percentile: Top 1%
Sustainability in Higher Education
17.85
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Challenges and Opportunities for Leveraging Generative AI for Sustainability Education: A Critical Review — Iryna Kushnir, Marcellus Forh Mbah, et al. · Sustainability (2025) | TGRS Research Map | TGRS