Teachers’ Ecological Transformation in Artificial Intelligence Literacy: A Case Study on the Transition from an Anthropocentric to an Ecocentric Perspective

Comments 1:

1: Were teachers writing diaries or journals? Please check and unify through the paper.

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section “Throughout the article, the term ‘journals’ has been replaced with ‘diaries.’

Comments 2:

When you say you selected teachers using maximum diversity sampling, how large was the total pull of teachers?  How did you use this method to select your sample?

Response 2:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. To implement the maximum diversity sampling strategy, an initial pool of approximately 62 teachers who had participated in the Sustainable Artificial Intelligence Training Program organized by researchers and affiliated institutions was identified. From this pool, participants were purposefully selected to ensure diversity across key characteristics such as teaching fields (preschool, elementary school, science, Turkish, and social studies), years of professional experience, and gender. The selection process aimed to capture a broad range of perspectives rather than focusing solely on statistical representativeness. Teachers meeting the inclusion criteria were first filtered, and then a balanced distribution across the defined diversity dimensions was ensured. As a result, 38 teachers representing maximum diversity across these characteristics were included in the study. This section is between lines 220-230 of the manuscript.

Comments 3:

Each heading and subheading should be numbered. The result section should include subheadings that introduce readers to what is done in each subsection.

Response 3:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. Each heading and subheading has been numbered. The conclusion section includes subheadings that summarize the topics covered in the subsections. This sections is between lines 377-386, 451-453, 504-506, 562-564 and 602-604 of the manuscript.

Comments 4:

It is unclear how MAXQDA 2020 operates in your study. What did you do with the transcripts of diaries and interviews? How the software performed in this specific scenario. Please explain this in greater detail to those who are unfamiliar with the software.

Response 4:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. In this study, MAXQDA 2020 was used as a systematic data organization, coding, and access environment to support the inductive analysis process. First, all interview transcripts and diary entries were imported into the software as separate document groups. Each data source was organized within MAXQDA to enable comparisons across data types. During the initial coding phase, meaningful units were identified through line-by-line reading, and open codes were assigned directly within the software. These codes were created inductively and improved over time by comparing them to each other. MAXQDA’s coding system enabled researchers to visually cluster similar codes and progressively develop higher-level categories and themes. While the software’s “Code System” and “Document System” functions were used to track relationships between codes and data sources, the “Selected Sections” function allowed researchers to review all excerpts associated with a specific code and ensure consistency in interpretation. Additionally, researchers examined code frequencies and distributions across different data sources using MAXQDA's built-in analytical tools, which supported the identification of dominant patterns. Furthermore, MAXQDA facilitated the comparison of independently coded data segments during the inter-coder reliability process. Coded segments were reviewed within the software environment, and disagreements among coders were discussed by directly examining the associated data excerpts. This process provided transparency and traceability regarding how themes were derived from the raw data. This sections is between lines 335- 354 of the manuscript.

Comments 5: Having in mind my previous comment, it would be desirable for your study to have a schematic flow presentation (one figure).

Response 5:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

Figure 1. Schematic flow of the research design and implementation process

Figure 1 illustrates the overall research process, including participant selection, the four-week structured Sustainable AI Training Program, data collection through methodological triangulation, and inductive thematic analysis. This schematic representation provides a holistic overview of how the study progressed from intervention to interpretation.

Comments 6:

The discussion section appears too long. If it follows results, it can also contain subsections as results should. The following is an interesting study on using AI in sustainable education that can supplement your literature: https://doi.org/10.3390/su172210148

Response 6:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. The discussion section has been divided into subsections, similar to the results section. The recommended article has been reviewed, and the necessary citations have been included.

Recent studies on the use of large language models (LLMs) in the context of sustainable education have shown that while these technologies have the potential to transform learn-ing processes, this transformation carries risks without pedagogical guidance and struc-tured use. Indeed, the study conducted within this scope reveals that the use of LLMs in learning processes based on sustainable development enhances students’ critical thinking, independent work, and problem-solving skills, while emphasizing that this effect is largely dependent on structured and guided usage conditions [21]. The same study notes that un-supervised use, however, increases the risks of superficial learning, the production of mis-information, and cognitive laziness. These findings indicate that the use of artificial intel-ligence in sustainable education is not merely a matter of technological integration; rather, it must be addressed in conjunction with pedagogical design, teacher guidance, and structured usage strategies. AND “In this context, it is emphasized that for LLMs to be effectively utilized in sustainable edu-cational processes, it is necessary to develop not only technological capabilities but also structured usage frameworks. Indeed, a recent study noted that the effectiveness of LLM use depends largely on “prompt quality” and structured guidance processes; it was found that guided use enhances students’ critical thinking skills and deepens the learning pro-cess [21]. This situation demonstrates that a sustainable AI approach must not be limited to energy efficiency or carbon footprint considerations alone, but must also incorporate pedagogical structuring processes. This sections is between lines 77-88 and 121-128 of the manuscript.

Comments 7: The paper does not include a conclusion section.

Response 6: Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

6. Conclusion

This study was conducted to examine teachers’ views on integrating the sustainable use of artificial intelligence (AI) into classroom teaching processes, particularly in the context of the tension between human-centered efficiency and environment-centered sustainability. The findings clearly demonstrate that the research objective was achieved and that the study successfully addressed its central research question. First, the results indicate that teachers have undergone a significant shift in their pedagogical reasoning regarding AI use. Participants have begun to adopt a critical decision-making framework focused on the question “Is this approach really necessary?” rather than merely focusing on efficiency, speed, and content production. This shift demonstrates that the educational intervention effectively fostered a deeper awareness of the ethical and ecological dimensions of AI use. Second, the study reveals that awareness of ecological costs—particularly regarding energy consumption, water usage, and carbon footprint—has become an integral part of teachers’ instructional decision-making processes. This finding directly addresses the research objective of examining how sustainable AI perspectives can be integrated into classroom practices. Third, teachers’ practices have evolved from habitual and intensive use toward a more planned, optimized, and minimal use of AI tools. Strategies such as command optimization, reuse of generated content, and prioritizing low-tech alternatives signal a transition toward sustainable digital pedagogy. Overall, the findings indicate that the integration of AI in education should be evaluated not merely as a technical or efficiency-focused process but as a multidimensional pedagogical design issue that encompasses ethical responsibility and ecological sustainability. This study contributes to the literature by demonstrating that sustainable AI use can be implemented at the micro-level of classroom practices through teachers’ awareness and initiative. In conclusion, the study confirms that raising awareness of AI’s environmental costs can trigger a meaningful transformation in teachers’ pedagogical orientations. However, sustaining this transformation requires long-term support through institutional policies, teacher education programs, and continuous professional development initiatives.

This sections is between lines 929-956 of the manuscript.

Comments 1:

1: We would recommend that the authors include, among the very up-to-date references cited in the Introduction, the UNESCO report “Reimagining our Futures Together: A New Social Contract for Education” (2021), as it contains several references to the climate crisis and the digital world that support the rationale of the study.

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. Global policy frameworks have also emphasized the need to reconsider the relationship between education, digital transformation, and ecological sustainability. The report “Reimagining Our Futures Together: A New Social Contract for Education” by UNESCO (2021) conceptualizes the contemporary world as a set of interconnected crises, including climate change, biodiversity loss, unsustainable resource use, and rapid technological transformation [22]. These overlapping challenges highlight that education can no longer be structured solely around efficiency, innovation, or economic productivity; rather, it must be reoriented toward sustainability, collective responsibility, and the common welfare The report further underscores the need to rethink curricula, redefine the role of teachers as transformative agents, and develop educational practices that respond to both ecological and digital challenges simultaneously. From this perspective, the integration of artificial intelligence into educational processes should not be evaluated only in terms of pedagogical effectiveness or technological advancement. Instead, it must be aligned with broader ecological and ethical considerations that address the environmental consequences of digital technologies. Therefore, examining teachers’ practices through the lens of sustainable AI use becomes critical for constructing an education system that responds to both the climate crisis and digital transformation. This section is between lines 89-105 of the manuscript.

Comments 1:

The title contains a misspelled word in “Ecocentric”, it is with a C not S: Teachers' Ecological Transformation in Artificial Intelligence Literacy: A Case Study on the Transition from an Anthropocentric to an Ecosentric Perspective

Response 1:

Thank you for pointing this out. We agree with this comment.

Comments 2:

Theoretical background: Are there any previous studies on teachers’ views regarding the integration of sustainable artificial intelligence use into classroom teaching processes? If so, should they be included in the article, so that the data obtained from the existing literature review can be analysed? If not, should this be mentioned in the article?

Response 2:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. There are studies in the literature that examine teachers’ perceptions and experiences regarding the integration of artificial intelligence into classroom practices. For instance, Alavizadeh et al., (2022) observed that educators generally regard AI tools as beneficial resources for improving instructional efficiency, personalization, and content creation, while also voicing concerns about pedagogical control and ethical ramifications [19]. Similarly, in their systematic review of AI use in K–12 education, Crompton and Burke (2023) noted that existing research primarily focuses on learning outcomes, performance improvement, and instructional effectiveness, showing limited interest in broader socio-ethical dimensions [20]. Additionally, studies by Le et al. (2026) [18] and Ding et al. (2024) [1] indicate that teacher professional development programs tend to address AI literacy primarily in terms of technical proficiency and instructional integration skills. However, these studies largely exclude teachers’ classroom practices and perceptions regarding the ecological dimension of AI use—specifically issues related to energy consumption, carbon footprint, and environmental sustainability. This section is between lines 64-77 of the manuscript.

Comments 3:

I do not understand the following sentence (perhaps a word is missing): In the current “Anthropocene” era, the issue of “ecological colonialism”—the indirect harm technology inflicts on non-human beings and ecosystems—is as important a pedagogical and ethical debate topic as algorithmic discrimination [35,36].

Response 3:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. Lines 138–141 of the article, which read “In the current “Anthropocene” era, the issue of “ecological colonialism”—the indirect harm technology inflicts on non-human beings and ecosystems—is as important a pedagogical and ethical debate topic as algorithmic discrimination”, have been omitted. Instead, lines 140–147, which read In the “Anthropocene” era, it is important to acknowledge that digital technologies—including artificial intelligence—can cause indirect environmental harm beyond the benefits intended for humans. This phenomenon is conceptualized as “ecological colonialism,” referring to the exploitation of natural resources, energy consumption, and ecological degradation that disproportionately affect non-human beings and ecosystems. From this perspective, ecological colonialism should be addressed as a pedagogical and ethical issue in education, just as significant as widely discussed topics such as algorithmic discrimination.”have been added.

Comments 4:

Check the following situation: “Research manuscripts reporting large datasets that are deposited in a publicly available database should specify where the data have been deposited and provide the relevant accession numbers. If the accession numbers have not yet been obtained at the time of submission, please state that they will be provided during review. They must be provided prior to publication.”

Response 4:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section. Data Availability Statement: Due to the qualitative nature of the data (including interview tran-scripts and participant diaries) and ethical considerations regarding participant confidentiality, the dataset is not publicly available. However, the corresponding author can provide the data pre-sented in the article in anonymized form upon reasonable request. This sections is between lines 1017- 1020 of the manuscript.

Comments 5: -What ethical considerations were taken into account when conducting the interviews/the study?

Response 5:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

The research process strictly adhered to various ethical principles, in addition to obtaining approval from the ethics committee. Participation in the study was entirely voluntary, and informed consent was obtained from all participants prior to data collection. Participants were clearly informed about the purpose of the study, the procedures to be followed, and their right to withdraw from it at any stage without facing any adverse consequences. To protect privacy and confidentiality, no personally identifiable information was gathered, and all data was coded and kept safe. Additionally, audio recordings and written data were used solely for research purposes and were not shared with third parties. During the reporting process, care was taken to present findings in a manner that prevents the identi-fication of participants and protects their professional and personal integrity. This sections is between lines 241-251 of the manuscript.

Comments 6:

Were the interviews conducted after the four-week educational intervention? Who implemented the programme – the researchers?

Response 6:

Thank you for pointing this out. We agree with this comment. Therefore, we have added the sections. The training program was designed and implemented directly by the researchers. Throughout the four-week process, the researchers actively facilitated theoretical sessions, guided practice-based activities, and monitored participants’ reflections through their di-aries. This direct involvement enabled the researchers to ensure consistency in content de-livery and to closely observe participants’ developmental processes. AND “In addition, all semi-structured interviews were conducted following completion of the four-week educational intervention program. Consequently, the interviews allowed par-ticipants to reflect in depth on their experiences, the transformations they underwent, and their pedagogical decision-making processes following the structured training. This sections is between lines 154-159 and 287-291of the manuscript.

Comments 7: How were the selected teachers recruited? Was participation voluntary, or were they selected?

Response 7: Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

Participants were selected through an invitation process conducted in collaboration with school administrations affiliated with the Ministry of National Education. First, teachers who met the study’s participation criteria were invited to participate. Participation in the study was entirely voluntary. Participants were purposefully selected based on predefined criteria to obtain information-rich cases. This sections is between lines 228-233 of the manuscript.

Comments 8: Why was data not collected from the participants prior to the training programme?

Response 8: Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

In addition, all semi-structured interviews were conducted following completion of the four-week educational intervention program. Consequently, the interviews allowed par-ticipants to reflect in depth on their experiences, the transformations they underwent, and their pedagogical decision-making processes following the structured training. This sections is between lines 287-291 of the manuscript.

Comments 9: It might be useful to include a section dedicated to conclusions, to summarise the study and reflect on whether the research question was answered and the objectives achieved; this section could also include, for example, the limitations and recommendations.

Response 9: Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

6. Conclusion

This study was conducted to examine teachers’ views on integrating the sustainable use of artificial intelligence (AI) into classroom teaching processes, particularly in the context of the tension between human-centered efficiency and environment-centered sustainabil-ity. The findings clearly demonstrate that the research objective was achieved and that the study successfully addressed its central research question. First, the results indicate that teachers have undergone a significant shift in their pedagogical reasoning regarding AI use. Participants have begun to adopt a critical decision-making framework focused on the question “Is this approach really necessary?” rather than merely focusing on efficien-cy, speed, and content production. This shift demonstrates that the educational interven-tion effectively fostered a deeper awareness of the ethical and ecological dimensions of AI use. Second, the study reveals that awareness of ecological costs—particularly regarding energy consumption, water usage, and carbon footprint—has become an integral part of teachers’ instructional decision-making processes. This finding directly addresses the re-search objective of examining how sustainable AI perspectives can be integrated into classroom practices. Third, teachers’ practices have evolved from habitual and intensive use toward a more planned, optimized, and minimal use of AI tools. Strategies such as command optimization, reuse of generated content, and prioritizing low-tech alternatives signal a transition toward sustainable digital pedagogy. Overall, the findings indicate that the integration of AI in education should be evaluated not merely as a technical or effi-ciency-focused process but as a multidimensional pedagogical design issue that encom-passes ethical responsibility and ecological sustainability. This study contributes to the literature by demonstrating that sustainable AI use can be implemented at the micro-level of classroom practices through teachers’ awareness and initiative. In conclusion, the study confirms that raising awareness of AI’s environmental costs can trigger a mean-ingful transformation in teachers’ pedagogical orientations. However, sustaining this transformation requires long-term support through institutional policies, teacher educa-tion programs, and continuous professional development initiatives.  This sections is between lines 927-954 of the manuscript.

Comments 9: The transition from Anthropocentric to an Ecosentric Perspective could be more explicit.

Response 9: Thank you for pointing this out. We agree with this comment. Therefore, we have added the section.

However, the shift from an anthropocentric to an ecocentric perspective should be under-stood not as a simple shift in ethical priorities but as a multilayered process of pedagogical transformation. Within an anthropocentric framework, artificial intelligence is primarily evaluated in terms of its contribution to human learning efficiency, performance, and in-structional effectiveness [39]. An ecocentric perspective expands this evaluative framework by integrating the ecological ramifications of technological utilization, including energy consumption, resource extraction, and environmental degradation [38]. This shift entails a fundamental restructuring of pedagogical reasoning, marking a shift from the question “How can AI improve learning outcomes?” to “Is the use of AI justified given its ecological cost and necessity?” [43]. In this sense, this shift is not merely about incorporating envi-ronmental concerns alongside existing criteria; it is transformative. For it redefines what constitutes “responsible” and “effective” pedagogy. Therefore, the transition from anthro-pocentric to ecocentric can be conceptualized as a shift from technology integration fo-cused on efficiency toward a responsibility-oriented pedagogical stance where teachers critically balance pedagogical benefits with ecological sustainability. This sections is between lines 153-167 of the manuscript.

Response to Comments on the Quality of English Language

Point 1: No changes were requested for the English text.