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  • Marcellus Forh Mbah1,*,
  • Tsamarah Rana Nugraha1 and
  • Iryna Kushnir2

Reviewer 1: Wayan Sintawati Reviewer 2: Anonymous Reviewer 3: Anonymous Reviewer 4: Andreia De Bem Machado

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comment for authors
1. Introduction

  • Technology advancement in the education sector has resulted in a new trend of utilising tools such as generative artificial intelligence (Gen-AI). This sentence may need a reference.
  • Let me ask you about this: the definition of Generative AI as “a tool consisting of algorithms” is too simplistic and potentially misleading. Consider refining this definition to reflect that Gen-AI refers to a class of models (e.g., large language models, diffusion models) that learn patterns from large-scale data to generate novel outputs.
  • You may need to standardise this section, alternating between “Gen-AI,” “generative tools,” and “generative artificial intelligence” without consistency.
  1. Gen-AI for Education
  • The integration of Gen-AI into education is transforming how both students and educators engage with teaching and learning. There is a significant increase in the use of Gen-AI to support students with their learning, while educators are leveraging the tools to streamline their tasks to save more time. This section explores how Gen-AI is being 53 utilized within an educational context, highlighting its benefits as well as the ethical and 54 pedagogical challenges it introduces. These long sentences may need references to support scientific arguments.
  • Several points are repeated across student and educator perspectives (e.g., efficiency, improved quality, and task automation). Consider consolidating overlapping arguments to avoid redundancy and improve the section’s coherence.
  • Some references (e.g., multiple citations grouped together for one broad claim) are not clearly tied to specific arguments. Please ensure each citation directly supports the claim being made.
  • Statements such as “Gen-AI responses have led to assessment integrity issues” or “Gen-AI promotes teaching and learning further by cultivating personalized learning” are strong claims but lack detailed evidence or elaboration. Please support these with concrete examples or qualify them appropriately.
  • The section moves quickly between student-related issues, educator-related issues, and general challenges. Consider restructuring to ensure a clearer separation (e.g., students, educators, system-level challenges) with stronger transitions between them.
  1. Gen-AI for Sustainability Education
  • Hence, 98 sustainability education could be interpreted as a contribution to a continually evolving 99 learning ecosystem where knowledge is co-created and shared within the community 100 without sacrificing the ability of future learners to engage in sustainable learning, where 101 Gen-AI could serve innovative teaching and foster a mindset of continuous learning and 102 flexibility (Nezhyva et al., 2025) Due to the broad definition of sustainability education, 103 the potential of Gen-AI can promote sustainability in several ways. This sentence is too long and may lead to potential failure to be read and understood by readers.
  • The reference to transformative learning theory as a conceptual foundation is mentioned only briefly at the end (lines 146–148). Please elaborate earlier in the section on how this theory frames your analysis of Gen-AI’s role in sustainability education, and clearly connect it to the discussion of opportunities and challenges.
  • Statements such as “Gen-AI can integrate sustainability topics into the curriculum” or “students can develop critical thinking and ethical decision-making” are too general. Please qualify these claims with evidence, examples, or conditions under which such outcomes are more likely to occur.
  1. Conceptual Framework: Transformative Learning
  • The first paragraph seems long-winded. Please bring your reader to the definition of the transformation itself. Please consider removing the first paragraph (lines 154-179).
  • Much of the text is highly descriptive, reiterating definitions and concepts of transformative learning without sufficient critical synthesis. The discussion would benefit from integrating the cited literature more analytically to highlight tensions, debates, or gaps (e.g., whether transformative learning is consistently effective in fostering sustainability, or where it might fall short).
  • Some sentences are lengthy and difficult to follow, which obscures the argument (e.g., lines 196–208 on Gen-AI and habits of mind). Consider breaking these into shorter, clearer statements.
  1. Methodology
  • The articles included were published between 2022 and 2025. What is the main reason to include articles in these years? If you think that ChatGPT was first launched in 2022, you need to consider Generative Adversarial Networks (GANs, 2014) by Ian Goodfellow et al. This is often regarded as the birth point of modern generative AI, as it enabled the creation of images that closely resemble real photographs. Please specify your strong reason.
  • Please define your inclusion and exclusion specifically and clearly.
  1. Findings
  • How do you define the challenges and opportunities in your study? Please strictly define these terms first.
  • Then by showing the analysis of the retained articles (see Table 1) reveals key thematic areas that recur across the studies, thereby offering a comprehensive 301 understanding of the benefits and challenges Gen-AI presents within the context of sus- 302 sustainability education. Do you think this table is enough and related to the challenges and opportunities themselves?
  • How can we believe that we can deep analysis through the results you show from table 1?
  • As my final comment in this Finding section, I think you are totally misleading about challenges and opportunities here because you did not define them at the beginning of this study. As my expertise, challenges refer to advantages or problems that you find in each article, while opportunities refer to future research directions. You may refer to this article DOI: https://doi.org/10.19173/irrodl.v20i4.4037
  1. Discussion and conclusion

Let me first wait and see the revised version, especially in the Findings section. Good luck

Comments for author File: Comments.pdf

Author Response

See attached! 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This article focuses on the application of Generative AI (Gen-AI) in sustainability education, analyzing its challenges and opportunities by integrating the transformative learning theory. The aim of this review is to fill the research gap regarding "the specific values and limitations of Gen-AI in sustainability education". However, the following problems still exist:

  1. The introduction section's review of the research context of "Gen-AI in education" is rather fragmented, It is suggested that the review be reconstructed in accordance with the framework of "time period + research theme".
  2. There is a disconnect between Chapter 2 and Chapter 3. A "transition paragraph" at the end of Chapter 2 should be added to explain and emphasize the connection.
  3. The elaboration on transformative learning theory in Chapter 4 remains merely at the level of concept introduction. It fails to clarify how this theory guides research design and data analysis.
  4. Please supplement more literature cases, as the number of cited references is insufficient (Prioritize studies from the past 3 years and those with high citation counts).
  5. The sample size (10 articles) is relatively small, and it is necessary to add an explanation in the methodology section.
  6. The existing suggestions are relatively general. Operable solutions can be elaborated by categorizing the subjects into "institutions - policymakers - educators", which can also make the structure of the article clearer.
  7. Some long paragraphs (e.g., the 2nd paragraph of Chapter 3 have confusing logical layers.
  8. The consistency of terminology. (e.g., "Gen-AI" and "generative AI"), the table format, and the reference format should be checked.

Author Response

Find attached!

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors
  1. The work addresses a perspective that contributes to a vision in the field of the use of AI in educational sustainability.
  2. The work is adequately organized.
  3. In relation to the methodology, it is functional. However, it is not solid, tending to be presented in a study that makes an analysis of the literature, and based on this, results, conclusions and discussions are presented. There are several methodologies for literature review or bibliometric analysis. As a result of the use of the cited methodology, results, discussion and conclusions that strengthen the methodology are shown.
  4. The references presented are adequate for the study; However, there are papers in the literature that are not being analyzed, even published by the publisher in which the article is being submitted.

Author Response

Attached!

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

The article Challenges and Opportunities for Leveraging Generative AI for Sustainability Education: A Scoping Review makes a relevant contribution to the emerging field of integrating generative AI into sustainability education. The abstract fulfills its purpose by clearly identifying the research problem, objectives, methodology, and final considerations. The introduction effectively situates the reader in the context of digital technologies in education and emphasizes the importance of understanding Gen-AI in sustainability contexts. However, it could be more concise and more directly articulate the link between the identified gap and the choice of a scoping review methodology.

The literature review demonstrates breadth and currency, supported by recent references (2022–2025) that reinforce the relevance of the study. The authors address both the general use of generative AI in education and its specific implications for sustainability education, drawing on solid theoretical frameworks such as transformative learning. Nonetheless, in some sections the review is more descriptive than critical, leaving space for greater problematization of tensions and contradictions in the existing literature, which would strengthen the analysis.The methodology is clear, objective, and appropriate to the study’s purpose. The use of a scoping review, justified by the novelty of the topic, is well described with explicit inclusion and exclusion criteria, even though the final sample of ten studies reflects the limited body of available research. The thematic analysis provides structure and depth, allowing the authors to distinguish opportunities and challenges of Gen-AI in sustainability education. To enhance robustness, the article would benefit from including a PRISMA-style flowchart and a more explicit discussion of the limitations of the study. The results and discussion are well structured, presenting opportunities, such as personalized learning, creativity support, and increased access to information, alongside challenges such as overreliance, inequalities in access, reliability issues, and environmental costs. The conclusions are consistent and point to practical implications for educators and policymakers, as well as avenues for further research. However, this section could be expanded with more concrete methodological recommendations, such as the need for longitudinal and comparative empirical studies. Overall, the article is timely, well-grounded, and deserves publication, with a recommendation of accept with minor revisions.

Author Response

Attached 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

The manuscript presents a relevant and timely discussion on the integration of generative artificial intelligence (Gen-AI) in sustainability education. The topic is significant, particularly as educational institutions worldwide seek innovative solutions to foster sustainability awareness and transformative learning. The authors clearly justify the relevance of Gen-AI by linking it to critical reflection, belief transformation, and long-term commitment to sustainable practices—an angle that strengthens the paper’s contribution to the field.

The paper successfully addresses an identifiable gap in the literature, as previous research has tended to focus on the broader educational applications of Gen-AI rather than its specific role and implications within sustainability education. The thematic analysis is logically structured and offers a balanced overview of both opportunities (e.g., personalised learning, creativity, improved access, and decision-making support) and challenges (e.g., unequal access, overreliance, unreliable outputs, and environmental concerns). This balanced critique enhances the paper’s credibility and practical value.

Overall, the manuscript is well-written, conceptually sound, and supported by relevant literature. It provides meaningful insights for educators, policymakers, and institutions seeking to integrate Gen-AI responsibly in alignment with sustainability principles. This work has the potential to make a valuable contribution to ongoing discussions in this emerging area.

Author Response

Thank you for recommending publication of our manuscript. Your comments were very helpful in refining the content of the paper. 

Reviewer 2 Report

Comments and Suggestions for Authors

It's OK.

Author Response

Thank you for the affirmation that our manuscript can now be considered for publication after attending to your comments which were very helpful.