International Trends and Influencing Factors in the Integration of Artificial Intelligence in Education with the Application of Qualitative Methods

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Author,

Thank you for the submitted manuscript and for the valuable research conducted. The manuscript you submitted aims to address existing knowledge gaps regarding the development of academic perspectives and practical applications in AI-supported qualitative research. During the review process, several unanswered questions and observations arose, which, if addressed and expanded upon, could substantially improve the quality of the manuscript. Please find below my detailed comments:

 

There are five references in the bibliography published between 2014 and 2019. I consider it important to highlight this, as the first research question partially addresses this period. While the study is based on more than 600 sources from the 2014–2024 timeframe, the detailed analyses and citations are limited to only five works covering the first half of the past decade, which I do not consider to be an adequate level of coverage in its current form.

Section 2:

The entire second section exhibits a lack of scholarly depth.

Regarding Figure 1, I recommend using the figure from reference [5] as a basis, not as a verbatim copy, but rather expanded to include other relevant domains and a more precise specification of how AI is manifested in each area. Accordingly, a more detailed elaboration of the individual domains is necessary, as they are currently only superficially described.

For example:

• Personalized learning: Beyond outlining the benefits offered in education, it is important to specify the forms in which it can be realized (e.g., manual leveling, programmed leveling, dynamically adaptive systems).
• Automated assessment: It is crucial to emphasize that AI primarily adds value in the evaluation of essay-based tests. Simpler systems such as Moodle can perform automated grading based on pre-defined parameters without requiring artificial intelligence. Therefore, it is necessary to clarify the specific areas and forms in which AI is applied (e.g., evaluation of essay responses through neural networks and natural language processing). It would also be useful to mention the most common platforms or development environments (e.g., libraries supporting these solutions).
• Curriculum design: This can be interpreted as overlapping with personalized learning. If curriculum content is designed individually and tailored to the learner, it falls under personalization. In this case, AI’s role may manifest in big data analysis, clustering, processing student feedback, and providing feedback to instructors. Predictive models can also be included here, as forecasts may influence the determination of learning groups or even curriculum organization. It would be valuable to incorporate studies that have tested predictive models in practical educational environments and presented their results. This raises the question: for which educational problems do researchers recommend specific models—for instance, what types of neural networks?

Such concrete details are notably absent from the manuscript.

Similarly cited:

„Additionally, unequal access to AI technologies risks widening the educational gap, disproportionately affecting students and schools with fewer resources [16].“ ; „Effective AI integration requires major investments in hardware, software, and teacher training—resources that not all institutions can afford, raising questions about long-term viability [26].“

• Based on the findings presented in the review article, it would be advisable to specify which particular AI technologies are affected by issues of unequal access. Given that there are numerous freely available AI software tools or those accessible through educational licenses, as well as lower-cost alternatives even on the hardware side, generalizing this observation may be excessive. It is recommended to distinguish between complex systems requiring significant investment and more widely accessible, simpler AI tools in order to provide a more precise depiction of the economic and technological challenges involved in AI integration.

„Finally, resistance to change remains a limiting factor in AI adoption. Teachers, administrators, and students may hesitate to embrace AI tools due to lack of awareness or fears that technology may displace traditional teaching methods [27].“

• The study’s finding that resistance to change—manifesting among teachers, administrators, and students alike—significantly influences the acceptance of AI tools is well-founded. However, it is recommended to conduct a more thorough and systematic review of the relevant literature, particularly studies that explore the attitudes of different stakeholder groups (students, teachers). It would also be valuable to examine how these attitudes vary across countries, continents, educational levels, and academic disciplines, as well as to identify areas where positive or negative reception of AI tool adoption is evident. Such an analysis would provide a more comprehensive and nuanced understanding of the acceptance of AI technologies in education.

„Another area of application is the cleaning of qualitative databases, as illustrated by [29], where even novice researchers use AI tools to transform unstructured data into analyzable formats, streamlining data preparation and fostering pedagogical opportunities for developing computational thinking and digital competencies in qualitative contexts.“

• Due to the review nature of the study, it would be justified to specify which particular AI tools are being discussed, and whether the literature includes other investigations employing different but functionally similar tools whose results could be compared. It is especially important to assess which AI solutions have proven to be more effective or reliable within this field.

„AI can identify trends, frequencies, and semantic patterns that largely align with human coding results.“

• It would be advisable to specify the type of AI technology under discussion, as the capabilities and limitations of different methods can vary significantly.

 

Section 3:

In addition to the general filtering parameters applied by the authors (2014–2024, English language, etc.), a search conducted during the review process using the keyword “artificial intelligence in education” yielded 12 indexed studies published in 2016 alone, compared to the 5 studies examined by the authors. This discrepancy indicates that the selection of keywords is a critical factor in uncovering relevant literature. Further refinement of the keywords could be guided by the categories presented in Figure 1. Therefore, it is recommended to more precisely specify the role of AI in education and to perform a more comprehensive and detailed review incorporating these specific keywords

 

Section 4:

„This work, alongside others such as [46] on AI education policy and [47] on teacher interactions with ChatGPT, highlights a trend toward understanding AI as a pedagogical complement rather than a replacement for traditional teaching.„

• It is important to emphasize that ChatGPT is a generative artificial intelligence. Therefore, a detailed examination of the thematic focus of the studies is warranted, particularly mapping the proportion of research addressing generative AI as opposed to other AI-supported educational tools, such as intelligent tutoring robots, AI-enhanced augmented or virtual reality, personalized learning solutions, and automated assessment systems, all of which are mentioned in Figure 1. Furthermore, it is recommended to analyze the nature of student and teacher feedback reported in studies on each AI tool and, based on this, to assess whether the findings can be generalized to all forms of AI in education or are specific to certain types of applications.

Line 351:  „3.3. Liderazgo y colaboración internacional en el estudio de la IA en educación con métodos cualitativos (RQ3)“ - The title is mistakenly shown in Spanish.

Figure 4: The line graphs representing India and Indonesia use colors that are too similar, making it difficult to distinguish between them.

In subsection 3.3, it would be advisable to provide a more detailed presentation of the key conclusions drawn from the relevant studies and their impact on the field. For example: “[60], in a 2022 article published in the Journal of Information Technology Education Research, conducted a systematic review on the use of AI in English language teaching.” It is important to specify which AI platforms were examined and to highlight the main findings. This approach would not only enhance the depth of the subsection but also better substantiate the relevance and applicability of the research outcomes.

In Figure 6, the study authors are listed by surname, given name, and year, whereas in the accompanying text, citations appear in the numbered [number] format. This inconsistency complicates the easy and quick interpretation of the figure, as readers must scroll to the reference list to identify the corresponding sources. It is recommended to supplement the figure with the appropriate [number] citations to ensure consistency between the figure and the textual citation style, thereby facilitating a smoother comprehension process.

It is recommended to conduct a more detailed analysis of which educational levels are the focus of studies examining the impact of artificial intelligence on education. It can be assumed that higher education predominates among the research, as partly supported by Figure 7. However, it would be valuable to assess the frequency with which studies address primary and secondary education, with particular attention to support for preparation for the secondary school leaving examination (maturity exam) and facilitation of workforce entry already at the high school level. Furthermore, it would be beneficial to explore which specific AI technologies can assist students at different educational stages, significantly in early education.

The manuscript provides a valuable overview of the role of AI in education, structured around the authors’ research questions. However, the study exhibits several significant limitations. Notably, the authors did not consider relevant studies indexed in the Web of Science database, which may substantially affect the comprehensiveness and representativeness of the review. Furthermore, there is a recurring sense of professional superficiality that undermines the depth of the research.

It is also important to highlight that the manuscript submitted to Informatics lacks a concrete overview and analysis of AI technologies, models, and algorithms, while placing predominant emphasis on pedagogical impacts. To restore balance, it is recommended to revise the literature review by incorporating a more detailed presentation of technological aspects and, where necessary, expanding the cited literature.

Author Response

Title: International trends and influencing factors in the integration of artificial intelligence in education with the application of qualitative methods.

Section: Social Informatics and Digital Humanities.

Author Response to Reviewer 1: Revisions and Clarifications Incorporated

Dear Author,

Thank you for the submitted manuscript and for the valuable research conducted. The manuscript you submitted aims to address existing knowledge gaps regarding the development of academic perspectives and practical applications in AI-supported qualitative research. During the review process, several unanswered questions and observations arose, which, if addressed and expanded upon, could substantially improve the quality of the manuscript. Please find below my detailed comments:

There are five references in the bibliography published between 2014 and 2019. I consider it important to highlight this, as the first research question partially addresses this period. While the study is based on more than 600 sources from the 2014–2024 timeframe, the detailed analyses and citations are limited to only five works covering the first half of the past decade, which I do not consider to be an adequate level of coverage in its current form.

Thank you very much for your comment regarding the limited presence of references corresponding to the 2014–2019 period in the original version of the manuscript. We especially value your feedback, as it has allowed us to reflect on the need to offer a more balanced and contextualized view of the historical development of the field.

In response to your suggestion, we have incorporated a new initial section within the theoretical framework section, entitled “Foundations and Early Developments in the Use of AI in Qualitative Educational Research (2014–2019).” This section aims to contextualize the initial developments in the use of artificial intelligence in qualitative educational research, highlighting the first applications of natural language processing, text mining, and machine learning for the analysis of unstructured data.

In addition, relevant references from that period have been added, which marked methodological and epistemological milestones in the integration of AI tools in qualitative research, especially in educational settings. We believe this addition not only adequately addresses your observation but also enhances the theoretical and chronological soundness of the manuscript.

We once again appreciate your comment, which has substantially contributed to enriching the structure and depth of the work.

Section 2:

The entire second section exhibits a lack of scholarly depth.

Regarding Figure 1, I recommend using the figure from reference [5] as a basis, not as a verbatim copy, but rather expanded to include other relevant domains and a more precise specification of how AI is manifested in each area. Accordingly, a more detailed elaboration of the individual domains is necessary, as they are currently only superficially described.

For example:

• Personalized learning: Beyond outlining the benefits offered in education, it is important to specify the forms in which it can be realized (e.g., manual leveling, programmed leveling, dynamically adaptive systems).

We deeply appreciate this observation, which has allowed us to strengthen the applicability of the proposed model. We have included an additional section detailing the main implementation methods for personalized learning in educational software environments.

• Automated assessment: It is crucial to emphasize that AI primarily adds value in the evaluation of essay-based tests. Simpler systems such as Moodle can perform automated grading based on pre-defined parameters without requiring artificial intelligence. Therefore, it is necessary to clarify the specific areas and forms in which AI is applied (e.g., evaluation of essay responses through neural networks and natural language processing). It would also be useful to mention the most common platforms or development environments (e.g., libraries supporting these solutions).

We sincerely appreciate this valuable comment, which has allowed us to refine and improve the focus of the section dedicated to automated assessment in the context of the use of artificial intelligence in educational software. Indeed, we fully agree that the distinctive contribution of artificial intelligence lies not in closed or parameterized marking tasks (as Moodle can perform), but in the automatic evaluation of open texts, especially essay responses, where natural language processing (NLP) tools and neural network-based models come into play.

In the new version of the manuscript, this section has been expanded to specify that AI allows for going beyond simple scoring, facilitating personalized feedback, holistic assessment, and analysis of discursive coherence—aspects impossible to achieve with systems based solely on predefined rules. Examples have also been given of common environments and libraries used in this field, such as e-rater® (ETS), WriteToLearn, or WriteWise in Spanish, as well as development frameworks such as spaCy, NLTK, GPT, or Hugging Face Transformers, which are widely used in automated assessment models.

Furthermore, a section has been included that clarifies the pedagogical value of these tools as a complement to—and not a substitute for—teacher judgment, aligned with the findings of the systematic review (e.g., Palermo & Wilson, 2020; Shermis, 2020).

 

We are convinced that this improvement brings clarity and conceptual precision to the text and reinforces its validity as a rigorous contribution to the discussion on the real possibilities of AI in automated educational assessment.

• Curriculum design: This can be interpreted as overlapping with personalized learning. If curriculum content is designed individually and tailored to the learner, it falls under personalization. In this case, AI’s role may manifest in big data analysis, clustering, processing student feedback, and providing feedback to instructors. Predictive models can also be included here, as forecasts may influence the determination of learning groups or even curriculum organization. It would be valuable to incorporate studies that have tested predictive models in practical educational environments and presented their results. This raises the question: for which educational problems do researchers recommend specific models—for instance, what types of neural networks?

We sincerely appreciate your comment, which has allowed us to enrich and refine the section on artificial intelligence-mediated curriculum design. We agree that there is an intersection between personalized learning and adaptive curriculum design, but we also believe that AI provides specific value in pre-classroom settings, through tools that allow for the analysis of large volumes of student data, the grouping of learning profiles using clustering techniques, and the generation of predictions for planning differentiated learning paths.

We have expanded this section in the manuscript by incorporating examples of the use of predictive models (such as recurrent neural networks or decision trees) that allow for anticipating dropout risks, planning cognitive load, and adjusting content to the student's pace. In addition, common platforms and libraries such as TensorFlow, Keras, Scikit-learn, and PyCaret have been cited, as used in real-life adaptive curriculum design environments, as evidenced by recent studies in higher and secondary education contexts.

It is also clarified that these tools do not replace teaching, but rather offer systematic feedback to improve instructional design based on empirical data. This approach aligns with the principles advocated by UNESCO (2024), which call for human-centered, ethical, and contextualized design.

Such concrete details are notably absent from the manuscript.

Similarly cited:

„Additionally, unequal access to AI technologies risks widening the educational gap, disproportionately affecting students and schools with fewer resources [16].“ ; „Effective AI integration requires major investments in hardware, software, and teacher training—resources that not all institutions can afford, raising questions about long-term viability [26].“

• Based on the findings presented in the review article, it would be advisable to specify which particular AI technologies are affected by issues of unequal access. Given that there are numerous freely available AI software tools or those accessible through educational licenses, as well as lower-cost alternatives even on the hardware side, generalizing this observation may be excessive. It is recommended to distinguish between complex systems requiring significant investment and more widely accessible, simpler AI tools in order to provide a more precise depiction of the economic and technological challenges involved in AI integration.

We appreciate this insightful comment and agree that greater specificity is needed to clarify which AI technologies are most affected by issues of unequal access. In response, we have revised the manuscript to distinguish more clearly between different types of AI tools used in educational settings, thus avoiding overgeneralizations and providing a more accurate depiction of the associated economic and technological challenges.

Specifically, we now differentiate between complex AI systems—such as learning analytics platforms, adaptive learning environments, and intelligent tutoring systems—which typically require significant investment in infrastructure, technical support, and teacher training. These technologies often remain inaccessible to under-resourced schools and institutions.

In contrast, we also highlight the growing availability of more accessible AI tools, including virtual assistants, generative AI applications for writing support, and mobile learning platforms. These tools are often easier to implement, requiring minimal infrastructure and frequently available through open-source platforms or educational licenses.

By including this distinction in the manuscript, we aim to clarify which types of AI technologies are more susceptible to issues of unequal access, addressing the concern raised and offering a more nuanced understanding of the practical challenges in AI integration across diverse educational contexts.

„Finally, resistance to change remains a limiting factor in AI adoption. Teachers, administrators, and students may hesitate to embrace AI tools due to lack of awareness or fears that technology may displace traditional teaching methods [27].“

• The study’s finding that resistance to change—manifesting among teachers, administrators, and students alike—significantly influences the acceptance of AI tools is well-founded. However, it is recommended to conduct a more thorough and systematic review of the relevant literature, particularly studies that explore the attitudes of different stakeholder groups (students, teachers). It would also be valuable to examine how these attitudes vary across countries, continents, educational levels, and academic disciplines, as well as to identify areas where positive or negative reception of AI tool adoption is evident. Such an analysis would provide a more comprehensive and nuanced understanding of the acceptance of AI technologies in education.

Thank you very much for the suggestion regarding the need for a more systematic exploration of the literature on stakeholder attitudes toward AI adoption. In response, we have expanded our review to incorporate a broader range of empirical studies that examine how resistance to change manifests differently among teachers, students, and administrators. This includes recent research highlighting the influence of generational, cultural, and disciplinary differences in shaping these attitudes. For instance, while students often display greater openness to AI due to their familiarity with digital tools, educators may express more scepticism, especially concerning ethical implications and the perceived threat to traditional pedagogical roles. Moreover, we have addressed geographic variability by including findings from studies conducted in diverse educational systems across Europe, Asia, and Latin America, which reveal distinct patterns of acceptance and resistance. This enriched review allows for a more nuanced understanding of how contextual factors such as educational level, institutional support, and cultural values mediate the adoption of AI tools in education.

„Another area of application is the cleaning of qualitative databases, as illustrated by [29], where even novice researchers use AI tools to transform unstructured data into analyzable formats, streamlining data preparation and fostering pedagogical opportunities for developing computational thinking and digital competencies in qualitative contexts.“

• Due to the review nature of the study, it would be justified to specify which particular AI tools are being discussed, and whether the literature includes other investigations employing different but functionally similar tools whose results could be compared. It is especially important to assess which AI solutions have proven to be more effective or reliable within this field.

We sincerely thank the reviewer for highlighting this point. In response, we acknowledge the importance of specifying the particular AI tools used for cleaning qualitative databases and transforming unstructured data into analyzable formats. The literature highlights several tools that have been effectively employed for this purpose. For instance, NVivo offers functionalities for organizing and cleaning qualitative datasets through automated categorization and text search capabilities. ChatGPT and similar large language models (LLMs) have been increasingly used by novice researchers to standardize responses, remove redundancies, and generate summaries, facilitating the pre-processing of raw qualitative data. Tools like AQUA and QDA Miner Lite also assist in structuring and preparing qualitative datasets through semi-automated coding and classification. While comparative studies are still emerging, initial findings suggest that tools with integrated AI-supported functionalities, such as NVivo and AQUA, offer greater reliability for structured tasks, while generative models like ChatGPT provide flexibility but require more oversight. Including these distinctions allows for a more nuanced understanding of tool efficacy and supports future comparative research in the field.

„AI can identify trends, frequencies, and semantic patterns that largely align with human coding results.“

• It would be advisable to specify the type of AI technology under discussion, as the capabilities and limitations of different methods can vary significantly.

Thank you for your thoughtful comment. In response, we have incorporated a detailed discussion of specific AI tools that support the cleaning and structuring of qualitative databases, highlighting their ability to identify trends, frequencies, and semantic patterns—often with results comparable to human coding. Tools such as BiLSTM, EX-CODE, and pattern mining algorithms have been included to exemplify functionally similar approaches, along with an assessment of their accuracy and interpretability in educational and qualitative research contexts. This addition enhances the review’s analytical depth and aligns with the recommendation to evaluate which AI solutions have proven more effective and reliable in this field.

Section 3:

In addition to the general filtering parameters applied by the authors (2014–2024, English language, etc.), a search conducted during the review process using the keyword “artificial intelligence in education” yielded 12 indexed studies published in 2016 alone, compared to the 5 studies examined by the authors. This discrepancy indicates that the selection of keywords is a critical factor in uncovering relevant literature. Further refinement of the keywords could be guided by the categories presented in Figure 1. Therefore, it is recommended to more precisely specify the role of AI in education and to perform a more comprehensive and detailed review incorporating these specific keywords

We appreciate your valuable comment regarding the discrepancy in the number of studies identified for 2016. We agree that keyword selection is a critical aspect of any systematic review, especially in a field as dynamic and multidimensional as artificial intelligence applied to education.

In our study, the search strategy was initially designed with a broad and generalist approach, using terms such as "artificial intelligence" AND "education" combined with methodological descriptors ("qualitative research") and limitations based on language (English) and period (2014–2024). This approach yielded a manageable set of relevant studies for data analysis, but we recognize that it may have left out some relevant works that use more specific terminology or refer to particular subcategories of AI (e.g., intelligent tutoring systems, natural language processing, generative AI, etc.).

We especially appreciate the suggestion to guide the search strategy according to the categories presented in Figure 1. In future revisions or expansions of the study, we will consider refining the search strategy, incorporating specific keywords associated with each category of educational AI, which will undoubtedly allow for a more exhaustive identification of the relevant literature. This methodological improvement will contribute to greater representativeness and depth of analysis.

Likewise, this limitation will be explained in the study's limitations section, recognizing that the search strategy employed, although adequate for a first approach to the phenomenon, may not have captured all relevant qualitative studies due to the field's terminological variability. This clarification will help contextualize the findings and guide future research toward greater terminological and methodological precision.

Section 4:

„This work, alongside others such as [46] on AI education policy and [47] on teacher interactions with ChatGPT, highlights a trend toward understanding AI as a pedagogical complement rather than a replacement for traditional teaching.„

• It is important to emphasize that ChatGPT is a generative artificial intelligence. Therefore, a detailed examination of the thematic focus of the studies is warranted, particularly mapping the proportion of research addressing generative AI as opposed to other AI-supported educational tools, such as intelligent tutoring robots, AI-enhanced augmented or virtual reality, personalized learning solutions, and automated assessment systems, all of which are mentioned in Figure 1. Furthermore, it is recommended to analyze the nature of student and teacher feedback reported in studies on each AI tool and, based on this, to assess whether the findings can be generalized to all forms of AI in education or are specific to certain types of applications.

We appreciate the suggestion and fully agree on the importance of differentiating between the different types of artificial intelligence applications in education. Consequently, we have incorporated a more detailed thematic analysis that allows us to identify the predominance of generative artificial intelligence—and, in particular, of tools such as ChatGPT—in the reviewed studies. We have also contrasted this approach with other forms of educational AI, such as intelligent tutoring systems, AI-enabled immersive technologies, personalized learning, and automated assessment systems. This differentiation has allowed us to more precisely delineate the scope of the findings, highlighting that much of the available qualitative evidence focuses on generative AI, which limits the possibilities of generalization to other types of tools. This reflection has been incorporated into the manuscript, taking into account the valuable comments received.

Line 351:  „3.3. Liderazgo y colaboración internacional en el estudio de la IA en educación con métodos cualitativos (RQ3)“ - The title is mistakenly shown in Spanish.

Thank you for pointing out this editorial oversight. You are absolutely right — the title of section 3.3 was mistakenly presented in Spanish. In the revised version of the manuscript, we have corrected it to appear in English as: “3.3. International Leadership and Collaboration in the Study of AI in Education Using Qualitative Methods (RQ3)”. We appreciate your attention to detail.

Figure 4: The line graphs representing India and Indonesia use colors that are too similar, making it difficult to distinguish between them.

Thank you for your comment regarding the visualization. We would like to clarify that the figure in question was generated using the Bibliometrix package in R, which automatically assigns colors and does not allow manual configuration by country for this specific type of bibliometric map. We agree that a country-specific color scheme could enhance interpretability, but unfortunately this option is currently limited by the tool's output settings. As an alternative, we considered replacing the figure with a table listing the detailed country data. However, due to the high number of entries generated, such a table would occupy a significant amount of space and potentially disrupt the readability and flow of the manuscript. Nonetheless, we remain open to incorporating a summary table in the supplementary materials if the editorial team considers it appropriate. We appreciate your understanding and your valuable suggestion.

In subsection 3.3, it would be advisable to provide a more detailed presentation of the key conclusions drawn from the relevant studies and their impact on the field. For example: “[60], in a 2022 article published in the Journal of Information Technology Education Research, conducted a systematic review on the use of AI in English language teaching.” It is important to specify which AI platforms were examined and to highlight the main findings. This approach would not only enhance the depth of the subsection but also better substantiate the relevance and applicability of the research outcomes.

Thank you for your insightful comment. In the revised version of the manuscript, we have included a direct reference to the systematic review by Sharadgah and Sa’di (2022) in subsection 3.3. As suggested, we have expanded the analysis by describing the most frequently used AI platforms identified in that study. Specifically, we mention the use of chatbots, speech recognition systems, machine translation engines, and intelligent tutoring systems, highlighting their impact on learner autonomy, pronunciation improvement, and motivation in English language learning. This addition provides greater depth to the section and reinforces the practical relevance of the findings discussed in our review. We appreciate your suggestion, which has helped enhance the clarity and substance of the manuscript.

In Figure 6, the study authors are listed by surname, given name, and year, whereas in the accompanying text, citations appear in the numbered [number] format. This inconsistency complicates the easy and quick interpretation of the figure, as readers must scroll to the reference list to identify the corresponding sources. It is recommended to supplement the figure with the appropriate [number] citations to ensure consistency between the figure and the textual citation style, thereby facilitating a smoother comprehension process.

Thank you for this helpful observation. In response, we have revised Figure 6 to include the appropriate [number] citations corresponding to the reference list, ensuring consistency with the in-text citation style. This adjustment improves clarity and allows for easier and more efficient interpretation of the figure by readers.

It is recommended to conduct a more detailed analysis of which educational levels are the focus of studies examining the impact of artificial intelligence on education. It can be assumed that higher education predominates among the research, as partly supported by Figure 7. However, it would be valuable to assess the frequency with which studies address primary and secondary education, with particular attention to support for preparation for the secondary school leaving examination (maturity exam) and facilitation of workforce entry already at the high school level. Furthermore, it would be beneficial to explore which specific AI technologies can assist students at different educational stages, significantly in early education.

We sincerely thank you for your very pertinent and valuable suggestion. In the revised version of the manuscript, we have incorporated a more detailed analysis of the educational levels addressed in the reviewed studies. While a predominance of research focused on higher education was previously indicated, the relative frequency of studies in early childhood education is now presented more explicitly, addressing the potential gap in the scarcity of studies on undergraduate education and teacher training and secondary education.

The manuscript provides a valuable overview of the role of AI in education, structured around the authors’ research questions. However, the study exhibits several significant limitations. Notably, the authors did not consider relevant studies indexed in the Web of Science database, which may substantially affect the comprehensiveness and representativeness of the review. Furthermore, there is a recurring sense of professional superficiality that undermines the depth of the research.

We deeply appreciate your assessment of the overall structure of the manuscript and its relevance to the topic addressed. Regarding your observation about not including the Web of Science database, we would like to clarify that its inclusion was considered during the methodological design phase of the study. However, after conducting initial comparative searches, we observed that a significant proportion of the documents retrieved in Web of Science were already indexed in Scopus, generating duplication without contributing substantial changes to the corpus analyzed.

The decision to use Scopus exclusively is based on its broad coverage in the fields of education and applied social sciences, as well as its integration with bibliometric analysis tools such as Bibliometrix, which optimize the visualization and processing of large volumes of data. Scopus has been used in numerous bibliometric studies in education due to its greater number of indexed journals compared to Web of Science, including publications with a pedagogical, technological, and multidisciplinary focus relevant to the subject of study.

It should be noted that this work is part of an exploratory bibliometric review, the main purpose of which is to offer a panoramic and quantitative overview of the development of the field, identifying trends, key authors, collaboration patterns, and emerging indicators. Therefore, this is not a systematic review or an in-depth analysis of the content of the documents, but rather a preliminary diagnostic phase, which seeks to inform and guide future, more specific qualitative or systematic research focused on particular approaches, contexts, or educational levels.

However, we appreciate your comment, as it has allowed us to more clearly explain this methodological decision in the limitations section, reinforcing the transparency and focus of the study.

It is also important to highlight that the manuscript submitted to Informatics lacks a concrete overview and analysis of AI technologies, models, and algorithms, while placing predominant emphasis on pedagogical impacts. To restore balance, it is recommended to revise the literature review by incorporating a more detailed presentation of technological aspects and, where necessary, expanding the cited literature.

Thank you very much for your valuable feedback. We appreciate your observation regarding the current emphasis on the pedagogical impacts of AI in the manuscript. We agree that providing a more concrete overview and analysis of AI technologies, models, and algorithms will enhance the balance and depth of the literature review. Accordingly, we will revise the manuscript to include a more detailed presentation of the technological aspects of AI, expanding the discussion and incorporating additional relevant literature to support this section. This revision will contribute to a more comprehensive and well-rounded analysis in line with the journal’s expectations.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The area of study on AI in education, especially with a focus on qualitative methods, is highly relevant and timely given the rapid advancements and integration of AI in various fields. The exponential growth in publications since 2020 underscores the increasing interest in this area. The paper is logically organized with clear sections for introduction, theoretical framework, materials and methods, and results. The research questions are well-defined and directly addressed in the results section. The following issues need to be addressed before the article can be considered for publication:

1. Although the figures are helpful, Figure 3 ("Documents by year") is labelled as "Figure 3" again on page 7 which is confusing. The data overview on page 7 is also labelled as "Figure 3" on page 6.
2. The current "Results" section directly presents findings without a distinct "Discussion" section where the results could be interpreted, linked back to the theoretical framework, and compared with existing literature beyond what is already mentioned. As such, the discussion section should be provide deeper insights and elaborate on the implications of the findings.
3. Even though the conclusion touches upon the importance of qualitative methods, expanding on specific future research directions based on the identified gaps and trends would be more appropriate for the paper.
4. Minor grammatical improvements and refinement of sentence structure (as some sentences are overly too long) could ensure the readability and flow of the paper.

Author Response

Title: International trends and influencing factors in the integration of artificial intelligence in education with the application of qualitative methods.

Section: Social Informatics and Digital Humanities.

Author Response to Reviewer 2: Revisions and Clarifications Incorporated

The area of study on AI in education, especially with a focus on qualitative methods, is highly relevant and timely given the rapid advancements and integration of AI in various fields. The exponential growth in publications since 2020 underscores the increasing interest in this area. The paper is logically organized with clear sections for introduction, theoretical framework, materials and methods, and results. The research questions are well-defined and directly addressed in the results section. The following issues need to be addressed before the article can be considered for publication:

Thank you very much for your thoughtful and constructive feedback. We truly appreciate your recognition of the relevance and timeliness of our study, as well as your positive remarks regarding the structure, clarity, and alignment between the research questions and results. Your comments are very encouraging and affirming for our research team.

We also acknowledge the issues you have identified and will address each of them carefully in our revised manuscript to strengthen the quality and rigor of the paper. Once again, thank you for your valuable time and insightful review.

Although the figures are helpful, Figure 3 ("Documents by year") is labelled as "Figure 3" again on page 7 which is confusing. The data overview on page 7 is also labelled as "Figure 3" on page 6.

Thank you very much for your valuable observation regarding the figure labeling. We appreciate your attention to detail, and we agree that the repeated use of "Figure 3" could be confusing for the reader.

In response to your comment, we have thoroughly reviewed and corrected all figure titles and citations throughout the manuscript to ensure they are properly numbered and consistently referenced in the text. This revision helps improve the clarity and overall organization of the document.

We sincerely thank you again for your constructive feedback.

The current "Results" section directly presents findings without a distinct "Discussion" section where the results could be interpreted, linked back to the theoretical framework, and compared with existing literature beyond what is already mentioned. As such, the discussion section should be provide deeper insights and elaborate on the implications of the findings. Even though the conclusion touches upon the importance of qualitative methods, expanding on specific future research directions based on the identified gaps and trends would be more appropriate for the paper.

Thank you very much for your detailed and thoughtful feedback. We truly appreciate your comments and suggestions regarding the structure and depth of the discussion.

In response to your observation, we would like to clarify that the Discussion section is indeed included in the manuscript, placed immediately following the Results section as a separate and clearly labeled part. In this section, we interpret the findings in relation to the research questions and engage with the existing literature, including the key studies identified during the review. We have made sure to discuss how our results align with, differ from, or extend prior work in the field, particularly in the context of qualitative research and AI in education.

Furthermore, in light of your suggestion, we have revisited and enriched the section on future research directions, providing a more detailed and nuanced perspective based on the gaps and emerging trends identified in the literature. This refinement aims to strengthen the relevance and impact of our work for both researchers and practitioners.

We are grateful for your valuable insights, which have helped us improve the clarity and academic contribution of the manuscript.

Minor grammatical improvements and refinement of sentence structure (as some sentences are overly too long) could ensure the readability and flow of the paper.

Thank you very much for your helpful observation regarding the need for minor grammatical improvements and refinement of sentence structure. We fully agree that enhancing the clarity and flow of the manuscript is essential to ensure better readability.

To address this, we will request professional language editing through the MDPI Author Services to ensure that all grammatical issues and overly long sentences are corrected appropriately. We appreciate your recommendation and are committed to improving the overall quality of the manuscript.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have taken into account the suggestions provided in the review and have implemented the necessary corrections where possible.

The references in the literature review have been extended, particularly to better cover the first half of the examined decade.
Section 2 has been expanded with more detailed content, significantly improving the depth of the manuscript.
The Spanish text has been corrected.
The figures have been improved as much as feasible. I support the authors’ suggestion to complement Figure 5 with a summary table to be included in the supplementary material.
I accept the authors’ reasoning for limiting the scope to the Scopus database.

I appreciate the authors’ efforts and recommend the acceptance of the manuscript. I also encourage the authors to pursue further research that goes beyond the current study’s limitations.