AI in Climate Change Education: A Systematic Review of Trends, Knowledge Structures, and Future Directions

Maisuna Kundariati; Zia Aulia Zaidin Putra

doi:10.56207/genbionix.v4i2.972

Authors

Maisuna Kundariati Department of Biology, Universitas Negeri Malang, Indonesia https://orcid.org/0000-0002-8098-2352
Zia Aulia Zaidin Putra Department of Biology, Universitas Negeri Malang, Indonesia https://orcid.org/0009-0009-5558-3542

DOI:

https://doi.org/10.56207/genbionix.v4i2.972

Keywords:

artificial intelligence, climate change education, climate literacy, generative AI, systematic literature review, PRISMA

Abstract

Artificial intelligence (AI), including generative AI, machine learning, and learning analytics, is increasingly being integrated into climate change education (CCE) practice and efforts to improve climate literacy. This study aims to map the knowledge structure, research trends, and future directions at the intersection of AI and climate change education through a systematic literature review (SLR) approach based on the PRISMA 2020 protocol. The search was conducted in the Scopus database on 22 June 2026 using a combination of the terms “climate change education”, “climate literacy”, and similar terms together with “artificial intelligence”, “machine learning”, “generative AI”, “ChatGPT”, “large language model”, and “learning analytics”. From 38 initial documents, 16 English-language journal articles met the inclusion criteria and were analyzed thematically. The results reveal six main knowledge clusters: (1) generative AI and climate literacy, (2) misinformation and science communication, (3) immersive learning based on virtual reality, (4) learning engagement and learning analytics, (5) machine-learning-based awareness assessment, and (6) sustainability, justice, and policy. Publication trends show a sharp surge in 2025–2026, in line with the widespread adoption of ChatGPT and other large language models after 2023, with the most-cited article addressing the potential and bias-related risks of generative AI for climate literacy. This review identifies several research gaps, particularly regarding the evaluation of long-term learning impacts, bias audits of AI-generated content, the integration of conversational AI with immersive technologies, and the representation of vocational education contexts and developing countries, and proposes a research agenda to strengthen the role of AI in responsible climate change education.

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