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The paper was very well improved. It can be accepted.
[# PeerJ Staff Note - this decision was reviewed and approved by Jyotismita Chaki, a PeerJ Section Editor covering this Section #]
Thank you for making efforts in addressing all the raised comments.
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Based on all observations sent by reviewers the paper can be well improved.
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The idea present in this paper titled “Generative AI and future education: A review, theoretical validation and authors perspective on challenges and solutions.” However, the authors are suggested to address the following comments while revising the paper.
The authors suggested adding a section on existing Generative AI technologies to provide a quick overview and later provide a rationale for why specifically focusing on “ChatGpt” or it will be better to look at all the Gen AI models in education as the number of studies are very limited.
The authors are suggested to identify and specify the focus of exiting studies from the perspective of domains such as business, computer science, education, arts and others. It is ambiguous if these areas are not mentioned as the challenges may vary based on the domain and also the severity of challenges may also vary.
Suggested to remove the references that are not related to the theme of the paper such as: Qazi, A., Hussain, F., Rahim, N. A., Hardaker, G., Alghazzawi, D., Shaban, K., & Haruna, K. (2019). Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions. IEEE access, 7, 63837-63851.
Abayomi-Alli, O. O., Damaaeviius, R., Qazi, A., Adedoyin-Olowe, M., & Misra, S. (2022). Data augmentation and deep learning methods in sound classification: A systematic review. Electronics, 11(22), 3795.
The quality of all figures needs to be improved.
Carefully review the paper for typos such as line 712 (missing .)
Deontology is first used at line 558, later on it is defined at 682. It needs to be defined first. Similarly for other theories.
The details on #Codes and % are missing. The authors are suggested to add more details to the codes, what code refer to and how they are assigned. Moreover, what is its (%) refers too. What are these codes and % telling us. A detailed argument needed to be built on these percentages form line 215 to 217. It is merely reported as number only. Moreover, how these code frequency occurrences are generated such as 128. What is 128 referring to.
All these details are presently missing in the paper and adding this will give a better understanding to the authors.
What is practicality rating in Table 7. It is defined nowhere in the manuscript.
How theoretical validation is performed? Who performed theoretical validations and what are their demographics are not reported.
In the table the proposed solutions number referred to which number as it is not defined in Figure 4.
The authors should also assess the quality of the published articles based on which this review is presented. It is very crucial to identify the quality of the articles when the authors are assessing the presented solutions and giving their perspective.
The paper is well-structured, employing clear and professional English throughout, with an appropriate academic tone conducive to its target audience. It provides a comprehensive background of generative AI, particularly focusing on its applications and challenges within the educational sector. The literature is well-referenced, offering sufficient context and grounding the discussion in relevant academic discourse. The paper is of broad and cross-disciplinary interest, aligning with the journal's scope, and presents a compelling reason for its review through its focus on the challenges and strategies of integrating generative AI in education. The introduction clearly outlines the subject and establishes the audience and motivation behind the study, adhering to formal academic standards for clarity and structure.
The content of the article fits well within the aims and scope of the journal, conducting a rigorous investigation into the use of generative AI in education. The methods are described with enough detail to allow for replication, and the survey methodology appears comprehensive, aiming for unbiased coverage of the subject. However, detailed insights into potential gaps in the surveyed literature or biases in source selection were not explicitly addressed. The article is logically organized, facilitating an understanding of the challenges and proposed solutions regarding the use of generative AI in educational settings.
The paper does not assess the impact and novelty directly but encourages meaningful replication and further investigation, which is a strength. The conclusions are strongly linked to the research questions, focusing on the identified challenges and strategies for integrating generative AI in education. It develops a well-supported argument based on the literature reviewed, meeting the goals set out in the introduction. Furthermore, the paper successfully identifies unresolved questions and directions for future research, contributing valuable insights to the ongoing discourse on generative AI in education.
Overall, the paper provides a significant contribution to understanding the role of Generative AI in education, presenting a balanced view of its challenges and opportunities. Its strengths lie in its comprehensive literature review and clear presentation of findings. Future work could benefit from a more detailed analysis of solution effectiveness and broader engagement with raw data to enhance the study's reproducibility and transparency.
Potential gaps in the literature review process or biases in source selection are not explicitly discussed.
The study could benefit from a more detailed examination of the effectiveness and practical implementation of proposed strategies in educational settings.
While the paper discusses future directions, more specific recommendations for empirical research to test the proposed strategies could enhance its contribution to the field.
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