A Systematic Review of Artificial Intelligence in Higher Education Institutions (HEIs): Functionalities, Challenges, and Best Practices

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This systematic review provides a broad overview of AI in higher education, covering functionalities, challenges, and best practices. However, several critical improvements are needed before publication:

1. Clarity and Structure: The manuscript lacks a precise narrative flow. Sections feel fragmented, and transitions between ideas are often abrupt. The discussion section, in particular, reads like a summary rather than a critical synthesis. Reorganising content to follow a logical progression (e.g., from functionalities to challenges to best practices) with clear subheadings would improve readability.

2. Theoretical Grounding: While empirical studies are cited, the review lacks a strong theoretical framework. The introduction and discussion should better situate the findings within established educational theories (e.g., constructivism, self-regulated learning) and AI ethics frameworks.

3. Critical Analysis: The discussion is mainly descriptive. There is limited critical engagement with contradictions in the literature (e.g., AI promoting engagement vs. fostering laziness) or deeper exploration of why certain challenges persist (e.g., digital divide, ethical concerns). The conclusion should offer more nuanced implications rather than general statements.

4. Language and Expression: The English requires polishing for academic clarity. Sentences are often long and convoluted, and there are occasional grammatical errors (e.g., subject-verb agreement, awkward phrasing). Professional editing is recommended.

5. Visual Aids: Figures 3 and 4 are referenced but not provided in the submitted PDF. These should be included. Additionally, Table 6 (“Best Practices”) could be better integrated into the narrative rather than presented as a standalone list.

6. Research Questions: While stated, they are not explicitly revisited in the results or discussion. Each section should clearly link back to the research questions to strengthen coherence.

7. Implications Section: This is too brief and generic. You can expand to include specific, actionable recommendations for educators, institutions, and policymakers.

Author Response

Comments 1: Clarity and Structure: The manuscript lacks a precise narrative flow. Sections feel fragmented, and transitions between ideas are often abrupt. The discussion section, in particular, reads like a summary rather than a critical synthesis. Reorganising content to follow a logical progression (e.g., from functionalities to challenges to best practices) with clear subheadings would improve readability.

Response 1: We thank the reviewer for this important observation regarding the manuscript's narrative flow. We acknowledge that earlier versions of the paper presented the discussion in a descriptive manner, which led to fragmentation and abrupt transitions between ideas. In response, we have substantially revised the manuscript to improve coherence and logical progression. The Discussion section has been reorganized to follow a clear narrative structure, progressing systematically from publication trends to the core functionalities of AI in higher education, then to pedagogical implications, institutional applications, and finally, the associated challenges and ethical considerations. Clear subheadings have been introduced, and transitional sentences have been added at the beginning and end of key sections to explicitly link concepts and guide the reader through the argument [The manuscript has been updated]. 

Comments 2: Theoretical Grounding: While empirical studies are cited, the review lacks a strong theoretical framework. The introduction and discussion should better situate the findings within established educational theories (e.g., constructivism, self-regulated learning) and AI ethics frameworks.

Response 2: We thank the reviewer for highlighting the need for stronger theoretical grounding. We acknowledge that the earlier version of the manuscript placed greater emphasis on empirical findings, with limited explicit integration of educational and ethical theory. In response, we have revised both the Introduction and Discussion sections to more clearly situate the review within established educational theories and frameworks of AI ethics. Specifically, we have framed the pedagogical applications of AI in HEIs through constructivist learning theory, emphasising how AI-supported tools facilitate active knowledge construction, collaboration, and learner–learner interaction. In addition, self-regulated learning theory has been incorporated to interpret AI functionalities such as adaptive learning, immediate feedback, and personalised support, highlighting their role in fostering learner autonomy, metacognition, and goal-directed learning [The manuscript has been updated]. 

Comments 3: Critical Analysis: The discussion is mainly descriptive. There is limited critical engagement with contradictions in the literature (e.g., AI promoting engagement vs. fostering laziness) or deeper exploration of why certain challenges persist (e.g., digital divide, ethical concerns). The conclusion should offer more nuanced implications rather than general statements

Response 3: We appreciate the reviewer’s valuable observation. We acknowledge that earlier versions of the manuscript leaned toward descriptive synthesis and did not sufficiently interrogate tensions, contradictions, and persistent challenges identified in the literature. We have substantially revised the Discussion section to adopt a more critical and analytical stance. Rather than merely reporting benefits and challenges, the revised discussion explicitly examines contradictory findings, such as evidence that AI tools enhance student engagement and efficiency while simultaneously raising concerns about reduced cognitive effort, surface learning, and over-reliance on automated outputs. These tensions are interpreted through the lens of learning theory, particularly self-regulated learning, to explain why AI can either support or undermine learning, depending on the pedagogical design, scaffolding, and alignment of assessment. The revised manuscript also provides a more in-depth examination of persistent challenges, including the digital divide and ethical concerns. We critically analyse why these issues endure despite technological advances, highlighting structural inequalities in infrastructure, funding, and digital literacy, as well as institutional lag in policy development and governance. Ethical concerns are now situated within established AI ethics frameworks, clarifying how issues such as bias, transparency, and privacy are not merely technical limitations but systemic and socio-technical challenges. The conclusion has been strengthened to move beyond general statements by offering nuanced implications for policy, practice, and future research. These include the need for differentiated AI governance strategies, pedagogically informed AI integration, targeted capacity building for educators and students, and context-sensitive implementation approaches, particularly in resource-constrained HEIs. Rather than presenting AI as inherently beneficial or harmful, the revised conclusion emphasises that educational outcomes depend on how AI tools are designed, regulated, and embedded within institutional and pedagogical contexts. We believe these revisions significantly enhance the manuscript’s analytical depth, theoretical contribution, and relevance to both researchers and higher education decision-makers [The manuscript has been updated]. 

Comments 4: Language and Expression: The English requires polishing for academic clarity. Sentences are often long and convoluted, and there are occasional grammatical errors (e.g., subject-verb agreement, awkward phrasing). Professional editing is recommended.

Response 4: We thank the reviewer for highlighting concerns regarding language clarity and expression. We acknowledge that earlier versions of the manuscript contained long and occasionally convoluted sentences, as well as instances of grammatical inconsistency and awkward phrasing. In response, the manuscript has undergone a comprehensive language revision to improve academic clarity, coherence, and readability. Sentences have been shortened and restructured where necessary, grammatical errors have been corrected, and terminology has been standardised throughout the text. Particular attention was paid to subject–verb agreement, punctuation, and the clarity of complex arguments to ensure that ideas are communicated precisely and concisely. Additionally, the revised manuscript has been carefully proofread to align with formal academic writing conventions. Where appropriate, redundant expressions were removed and transitions refined to enhance the overall flow of the text. These revisions have significantly improved the quality of the language and presentation of the manuscript. 

Comments 5: Visual Aids: Figures 3 and 4 are referenced but not provided in the submitted PDF. These should be included. Additionally, Table 6 (“Best Practices”) could be better integrated into the narrative rather than presented as a standalone list

Response 5:

We thank the reviewer for noting the omission of Figures 3 and 4 and for the constructive suggestion regarding the integration of Table 6. We acknowledge that Figures 3 and 4 were referenced in the text but were inadvertently omitted from the submitted PDF. This has now been corrected, and both figures have been included in the revised manuscript, with appropriate cross-references in the text. We also acknowledge that, in the earlier version, Table 6 was presented primarily as a standalone list with limited analytical interpretation. To address this, we reorganised the table using theory-aligned categories and incorporated a dedicated narrative that critically analyses the observed patterns and their pedagogical implications [The manuscript has been updated]. 

Comments 6: Research Questions: While stated, they are not explicitly revisited in the results or discussion. Each section should clearly link back to the research questions to strengthen coherence.

 

Response 6: We appreciate the reviewer’s comment. In the revised Results section, we have explicitly linked findings to the research questions: Section 5.3 highlights AI functionalities that enhance student learning, addressing RQ1; Section 5.4 details challenges such as access, equity, and academic integrity, addressing RQ2; and Section 5.5 presents best practices in pedagogical, cognitive, and institutional domains, addressing RQ3. These revisions ensure that each part of the Results clearly corresponds to the research questions, improving the coherence and clarity of the review [The manuscript has been updated]. 

  7: Implications Section: This is too brief and generic. You can expand to include specific, actionable recommendations for educators, institutions, and policymakers.

Response 7: We appreciate the reviewer’s suggestion. The Implications section has been revised to provide more specific and actionable recommendations. For educators, we highlight strategies such as embedding AI in reflective and problem-solving activities, providing AI-assisted feedback while maintaining human oversight, and encouraging students to critically evaluate the outputs of AI. For institutions, we recommend investing in digital infrastructure, professional development, clear AI policies, and equitable access to technology. For policymakers and AI developers, we emphasize the importance of establishing ethical standards, ensuring transparency and fairness, and monitoring algorithmic bias to promote responsible development. These revisions aim to provide practical guidance for effectively integrating AI in higher education while addressing associated risks [The manuscript has been updated]. 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article presents a comprehensive review of the literature on the integration of artificial intelligence in higher education institutions. It addresses key issues related to the use of artificial intelligence by both lecturers and students in academic settings. The review has the potential to serve as a valuable database for researchers seeking to identify studies relevant to their specific fields of interest.

I would like to offer several comments:

In the methodology section, it is stated that the initial search yielded 220,387 sources, which were subsequently reduced to 35 articles after applying the inclusion criteria. However, the filtering process is not sufficiently clear. It is unclear whether this process was conducted manually by the researchers or supported by an artificial intelligence tool. If artificial intelligence was used, the methodology should explicitly specify how it was employed: Which tool was used for filtering? How were the criteria operationalized within the tool? How were the filtering outcomes validated?

In addition, it is important to include a dedicated section addressing the reliability of the study. This section should clearly describe the filtering procedure and discuss the limitations of the study.

In the discussion section, it is difficult to clearly distinguish between the findings of the present study and evidence drawn from other studies that support these findings. As a result, the discussion section appears to extend the findings section rather than interpret it. It is therefore recommended to clarify whether specific statements are derived from the study’s own findings or from previously published research.

Another issue concerns the citation style used in the manuscript. In the body of the text, references are cited using a Vancouver-style numbering system, whereas in the reference list the sources are not numbered accordingly. This inconsistency makes it difficult for readers to identify the sources cited in the text.

 

Comments on the Quality of English Language

Regarding language quality, the article is written in generally good English; however, several spelling errors should be corrected. For example, in line 96, the abbreviation “HEL” appears instead of the correct “HEI.”

Author Response

Comment 1: In the methodology section, it is stated that the initial search yielded 220,387 sources, which were subsequently reduced to 35 articles after applying the inclusion criteria. However, the filtering process is not sufficiently clear. It is unclear whether this process was conducted manually by the researchers or supported by an artificial intelligence tool. If artificial intelligence were used, the methodology should explicitly specify how it was employed, including which tool was used for filtering. How were the criteria operationalized within the tool? How were the filtering outcomes validated?

Response 1: We thank the reviewer for this important observation regarding the transparency of the filtering process. We acknowledge that the initial version of the manuscript did not sufficiently clarify how the 220,387 records were reduced to the final 35 included studies. To address this concern, we have revised the methodology section to provide a clear, detailed description of the multi-stage screening and selection process. Specifically, we now explain that all records were first imported into CADIMA, which was used solely as a systematic review management and documentation platform. Duplicate records were removed using database and CADIMA tools, followed by manual screening of titles and abstracts by the researchers based on predefined inclusion and exclusion criteria. Full-text screening was then conducted manually to confirm eligibility. We have also explicitly stated that no generative AI tools were used to make inclusion or exclusion decisions; all screening judgments were performed by the research team, with discrepancies resolved through discussion [The manuscript has been updated].

Comment 2: In addition, it is important to include a dedicated section addressing the reliability of the study. This section should clearly describe the filtering procedure and discuss the limitations of the study.

 

Response 2: We appreciate the reviewer’s valuable suggestion. In response, we have added a new dedicated subsection entitled “Reliability and Limitations of the Study” to the methodology section. This subsection explicitly describes the multi-stage filtering procedure, including duplicate removal, manual title and abstract screening, and full-text eligibility assessment based on predefined inclusion and exclusion criteria. We also clarify the role of CADIMA as a review management and documentation platform and explicitly state that no generative artificial intelligence tools were used in the study selection or decision-making process. Furthermore, the new section discusses key limitations of the study, including restrictions related to language, publication type, database coverage, potential reviewer subjectivity, and the rapidly evolving nature of AI in higher education. These additions will improve the transparency, reliability, and interpretability of the review, as recommended by the reviewer. The manuscript has been updated.

Comment 3: In the discussion section, it is difficult to clearly distinguish between the findings of the present study and evidence drawn from other studies that support these findings. As a result, the discussion section appears to extend the findings section rather than interpret it. It is therefore recommended to clarify whether specific statements are derived from the study’s own findings or from previously published research.

Response 3: We appreciate the reviewer’s insightful comment. We acknowledge that the initial version of the discussion section did not sufficiently distinguish between the findings of the present systematic review and evidence drawn from previously published studies. In response, we have substantially revised the discussion to improve clarity and analytical depth. Specifically, we now explicitly differentiate between results derived from this review and supporting or contrasting evidence from prior literature by clearly attributing statements to either the present study’s findings or external sources through explicit phrasing and appropriate citations. We have also restructured the discussion to focus on interpretation, comparison, and implications of the findings rather than reiterating descriptive results. These revisions ensure that the discussion section provides a meaningful interpretation of the findings, situates them within the broader literature, and addresses the reviewer’s concerns.

 

Comment 4: Another issue concerns the citation style used in the manuscript. In the body of the text, references are cited using a Vancouver-style numbering system, whereas in the reference list the sources are not numbered accordingly. This inconsistency makes it difficult for readers to identify the sources cited in the text.

 

Response 4: We thank the reviewer for bringing this inconsistency in the citation style to our attention. We acknowledge that the reference list in the initial manuscript was not fully aligned with the Vancouver-style numbering used in the body of the text. In response, we have carefully revised the manuscript to ensure full consistency between in-text citations and the reference list. All references are now numbered sequentially according to their first appearance in the text, and the reference list has been reformatted to follow the Vancouver citation style throughout. The manuscript has been updated.

 

Comment 5: Regarding language quality, the article is written in generally good English; however, several spelling errors should be corrected. For example, in line 96, the abbreviation “HEL” appears instead of the correct “HEI.”

Response 5: We appreciate the reviewer’s attention to this issue. The manuscript has been carefully proofread to correct spelling and typographical errors. Specifically, the abbreviation “HEL” has been corrected to “HEI.” Additional minor language edits have been made throughout the manuscript to further improve clarity and consistency.

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Author(s),

The manuscript entitled “A systematic review of artificial intelligence in higher education institutions (HEIs): functionalities, challenges and best practices” addresses a highly timely and relevant topic. By systematically synthesizing the literature on AI functionalities, challenges, and best practices in higher education, the study makes a meaningful contribution to the growing body of research in this field. The scope of the review and the effort to integrate diverse studies are commendable. With several revisions, the manuscript has strong potential to further strengthen its theoretical grounding and analytical depth.

The following comments are provided to support the improvement of the manuscript:

Abstract

• The opening sentences of the abstract should more clearly emphasize the background of the topic and the existing research gap.
• After establishing this context, the aim of the study should be stated explicitly to guide the reader more effectively.

Introduction

• The introduction would benefit from the inclusion of at least one explicit theoretical framework explaining AI integration in higher education. Currently, the theoretical perspective guiding the review remains unclear.
• The concept of artificial intelligence is used in a highly general manner. It is recommended that the authors clarify how AI is conceptualized in the present study (e.g., generative AI, learning analytics, automation, decision-support systems).
• The statement “AI can now provide differentiation that was previously impossible for teachers, enabling personalized instruction and feedback while identifying knowledge gaps and adapting lessons based on student responses and expressions” requires stronger empirical support. The following recent sources may be considered:
  https://doi.org/10.51383/ijonmes.2024.361
• The sentence “The digital divide creates disparities in access to technology and resources, disproportionately affecting students, especially those from low-income backgrounds and marginalized communities” should be supported by up-to-date evidence. The following reference is suggested:
  https://doi.org/10.46328/ijces.199
• Under the “Statement of the Problem” section, the study rationale would be strengthened by briefly discussing previous systematic reviews in the field and identifying their limitations. The novelty and distinct contribution of the current review should be clearly articulated.

Discussion

• The discussion section is largely written as a summary of findings. It is recommended that this section be revised to move beyond descriptive reporting toward a more analytical and interpretative synthesis.
• The findings are not sufficiently connected to commonly used theoretical approaches in the literature, which limits the conceptual depth of the discussion.
• Although the reviewed studies span different countries, disciplines, and educational levels, the influence of these contextual differences is not addressed.
• The reviewed period (2014–2025) includes both early AI applications and the recent generative AI era (post-ChatGPT); however, this transformation is not explicitly discussed.
• Despite being a systematic review, the discussion does not adequately address the methodological strengths and weaknesses of the included studies. Incorporating such an evaluation would strengthen the critical perspective of the manuscript.

Author Response

Comment 1: Abstract

• The opening sentences of the abstract should more clearly emphasize the background of the topic and the existing research gap.
• After establishing this context, the aim of the study should be stated explicitly to guide the reader more effectively

Response 1: We appreciate the reviewer’s insightful comment. In response, we have revised the opening sentences of the abstract to better contextualize the increasing adoption of AI in HEIs and to clearly highlight the existing research gap, namely the lack of a comprehensive and up-to-date synthesis of evidence on best practices, key functionalities, and challenges associated with AI integration in HEIs. Additionally, we have explicitly stated the aim of the study in the abstract to clearly guide readers regarding the purpose and contribution of this systematic review. The manuscript has been updated

Comment 2: Introduction

• The introduction would benefit from the inclusion of at least one explicit theoretical framework explaining AI integration in higher education. Currently, the theoretical perspective guiding the review remains unclear.
• The concept of artificial intelligence is used in a highly general manner. It is recommended that the authors clarify how AI is conceptualized in the present study (e.g., generative AI, learning analytics, automation, decision-support systems).
• The statement “AI can now provide differentiation that was previously impossible for teachers, enabling personalized instruction and feedback while identifying knowledge gaps and adapting lessons based on student responses and expressions” requires stronger empirical support. The following recent sources may be considered:
  https://doi.org/10.51383/ijonmes.2024.361
• The sentence “The digital divide creates disparities in access to technology and resources, disproportionately affecting students, especially those from low-income backgrounds and marginalized communities” should be supported by up-to-date evidence. The following reference is suggested:
  https://doi.org/10.46328/ijces.199

Response 2:

• We appreciate this constructive suggestion. We have revised the introduction to clearly define how AI is conceptualized in this study. Specifically, AI is now described as an umbrella term encompassing adaptive and intelligent tutoring systems, learning analytics and predictive analytics, automated assessment and feedback tools, decision-support systems, and selected forms of generative AI used for educational support. We also clarify that the review focuses on the educational and institutional applications of AI, rather than on the technical development of algorithms. This clarification improves conceptual precision and ensures consistency throughout the manuscript.
• We thank the reviewer for highlighting this issue and for suggesting a relevant recent source. In response, we have revised the statement to adopt a more evidence-based and cautious tone, and have strengthened it by explicitly citing recent empirical research that demonstrates how AI-supported adaptive systems and analytics enable personalized feedback, identify knowledge gaps, and provide differentiated instructional pathways.
• We appreciate this recommendation. The introduction has been revised to support the discussion of the digital divide with recent empirical evidence demonstrating how disparities in access to digital infrastructure, devices, and connectivity continue to disproportionately affect students from low-income and marginalized backgrounds. The suggested reference (DOI: 10.46328/ijces . 199) has been added to strengthen the empirical foundation of this argument.

  Comment 3: The “Statement of the Problem” section, the study rationale would be strengthened by briefly discussing previous systematic reviews in the field and identifying their limitations. The novelty and distinct contribution of the current review should be clearly articulated.

   

   

  Response 3: We appreciate the reviewer’s valuable suggestion. In response, we have revised the statement of the problem section to explicitly situate the present study within the context of existing systematic reviews on artificial intelligence in higher education. The revised text now briefly discusses how prior reviews have largely focused on specific AI tools, isolated educational functions, or narrow timeframes, and have often provided limited synthesis of best practices, ethical considerations, and equity-related challenges across diverse higher education contexts. Also, we have clearly articulated the novelty and distinct contribution of the current review. Specifically, the revised section emphasizes that this study provides a comprehensive and up-to-date synthesis of empirical evidence published between 2014 and 2024, simultaneously examining AI functionalities, best practices, and implementation challenges in HEIs. The contribution of the review is further distinguished by its integration of pedagogical theory and AI ethics perspectives, with particular attention to issues of digital inequality and the responsible use of AI. The manuscript has been updated

  Comment 4: Discussion

  • The discussion section is largely written as a summary of findings. It is recommended that this section be revised to move beyond descriptive reporting toward a more analytical and interpretative synthesis.
  • The findings are not sufficiently connected to commonly used theoretical approaches in the literature, which limits the conceptual depth of the discussion.
  • Although the reviewed studies span different countries, disciplines, and educational levels, the influence of these contextual differences is not addressed.
  • The reviewed period (2014–2025) includes both early AI applications and the recent generative AI era (post-ChatGPT); however, this transformation is not explicitly discussed.
  • Despite being a systematic review, the discussion does not adequately address the methodological strengths and weaknesses of the included studies. Incorporating such an evaluation would strengthen the critical perspective of the manuscript.

  Responses 4:

  • We thank the reviewer for this important observation. In response, we have revised the Discussion section to move beyond descriptive reporting by providing a more analytical and interpretative synthesis of the findings. The revised discussion now critically examines why and how AI functionalities produce different educational outcomes, emphasizing the conditional nature of AI effectiveness based on pedagogical design, institutional readiness, and ethical governance. Interpretative links between findings across studies have been strengthened to highlight patterns, contradictions, and implications rather than restating results.
  • We appreciate this valuable comment. To enhance conceptual depth, the Discussion section has been explicitly restructured around key theoretical perspectives, including constructivist learning theory, self-regulated learning theory, and AI ethics frameworks. The findings are now interpreted through these lenses to explain how AI tools support knowledge construction, learner autonomy, feedback processes, and ethical decision-making in HEIs.
  • We thank the reviewer for highlighting this limitation. The revised Discussion now explicitly addresses how contextual factors such as geographical setting, disciplinary focus, institutional capacity, and educational level influence the adoption and effectiveness of AI in HEIs. Differences in digital infrastructure, policy environments, pedagogical traditions, and student preparedness are now discussed as key factors that shape AI outcomes. This addition acknowledges the heterogeneity of the reviewed studies and avoids overgeneralization of the findings.
  • We appreciate this insightful observation. In response, we have explicitly distinguished between early AI applications (e.g., intelligent tutoring systems, learning analytics, automation) and the recent generative AI era, particularly following the emergence of ChatGPT. The revised Discussion now reflects on how this shift has transformed pedagogical practices, academic integrity concerns, and ethical debates. This temporal analysis highlights the evolution of the nature, scale, and implications of AI use in HEIs across the review period (2014–2025).
  • We thank the reviewer for this suggestion. To strengthen the critical perspective of the manuscript, we have added a dedicated discussion on the methodological characteristics, strengths, and limitations of the included studies. The revised section now addresses issues such as reliance on self-reported data, the dominance of cross-sectional designs, limited longitudinal evidence, and uneven theoretical foundations. At the same time, methodological strengths such as the growing use of mixed-methods approaches and empirically grounded evaluations are acknowledged. This critical appraisal enhances the rigor and transparency of the review.
  • The manuscript has been updated.

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript offers a broad and contemporary perspective on one of the most pressing issues in academia today: the integration of Artificial Intelligence within higher education institutions. Through a systematic review of 35 high-quality papers, the authors successfully mapped the field's key functionalities, ethical challenges, and best practices.

The literature review in this article provides a comprehensive foundation that bridges the historical context with contemporary developments, effectively cross-referencing theoretical frameworks with findings from diverse empirical studies in the field.

The methodology is presented clearly, demonstrating transparency in the article selection and screening processes.

There is no substantive discussion of two critical issues identified in the findings: first, the gap between improved academic performance and actual conceptual understanding; and second, the paradox between stimulating creativity and fostering student laziness.

 

Author Response

Comment 1: The manuscript offers a broad and contemporary perspective on one of the most pressing issues in academia today: the integration of Artificial Intelligence within higher education institutions. Through a systematic review of 35 high-quality papers, the authors successfully mapped the field's key functionalities, ethical challenges, and best practices.

 

Response 1: We sincerely thank the reviewer for the positive evaluation and for recognising the breadth, quality, and relevance of our review. We are pleased that the mapping of AI functionalities, ethical challenges, and best practices in HEIs has been found comprehensive and useful. The constructive feedback is encouraging and reinforces the significance of our contribution to the ongoing discourse on AI integration in higher education.

 

Comment 2: The literature review in this article provides a comprehensive foundation that bridges the historical context with contemporary developments, effectively cross-referencing theoretical frameworks with findings from diverse empirical studies in the field.

 

Response 2: We sincerely thank the reviewer for this encouraging feedback. We are pleased that the literature review’s integration of historical context, contemporary developments, and theoretical frameworks with empirical evidence has been found coherent and informative. We aimed to provide a strong foundation that situates AI applications in HEIs within both historical and theoretical perspectives, and the reviewer’s recognition affirms the value of this approach.

Comment 3: The methodology is presented clearly, demonstrating transparency in the article selection and screening processes.

 

Response 3: We thank the reviewer for acknowledging the clarity and transparency of our methodology. We aimed to ensure a rigorous and reproducible systematic review by clearly documenting the article selection, screening, and inclusion processes. The reviewer’s recognition confirms that these methodological steps effectively communicate the study’s rigor and reliability.

 

Comment 4: There is no substantive discussion of two critical issues identified in the findings: first, the gap between improved academic performance and actual conceptual understanding; and second, the paradox between stimulating creativity and fostering student laziness.

 Response 4: We appreciate the reviewer’s attention to these important issues. In response, we have revised the Discussion section to explicitly address both points:

• Performance vs. conceptual understanding: We now analyse evidence showing that while AI tools (including generative AI) can enhance efficiency and task completion, gains in deep conceptual understanding are often limited without structured pedagogical scaffolding [64,80,89]. This distinction clarifies that AI supports learning outcomes only when embedded within carefully designed instructional strategies.
• Creativity vs. student laziness paradox: We also discuss how AI can stimulate creativity and idea generation, particularly in research and writing tasks, while simultaneously posing risks of over-reliance or cognitive offloading. This dual effect is considered in relation to self-regulated learning theory, emphasising the need for guidance, reflective tasks, and critical engagement to prevent reduced initiative or passive learning [102].
• The manuscript has been updated.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

• Ensure all figures and tables are correctly numbered and referenced in the text.

• Perform a final proofread to catch any lingering grammatical or formatting inconsistencies.

• Confirm that all citations in the reference list match in-text citations in style and completeness.

Author Response

Response to Reviewer 1
1. Summary
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
2. Questions for General Evaluation	Reviewer’s Evaluation	Response and Revisions
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	Yes	Thank you for the comment.
Are the research design, questions, hypotheses and methods clearly stated?	Yes	Thank you for the comment.
Are the arguments and discussion of findings coherent, balanced and compelling?	Can be improved	The discussion of findings was presented according to the research questions.
For empirical research, are the results clearly presented?	Yes	Thank you for the comment.
Is the article adequately referenced?	Yes	Thank you for the comment.
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	Can be improved	The conclusions were supported by literature on page 26.
3. Point-by-point response to Comments and Suggestions for Authors
Comments 1: Ensure all figures and tables are correctly numbered and referenced in the text.
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have correctly numbered all the figures and tables both on the captions and in the text.
Comments 2: Perform a final proofread to catch any lingering grammatical or formatting inconsistencies.
Response 2: Agree. We have proofread the document and also engaged a language editor to assist with grammatical and formatting inconsistencies.   Comments 3: Confirm that all citations in the reference list match in-text citations in style and completeness. Response 2: Agree. We have revised the in text citation and the reference list to ensure that they match and
4. Response to Comments on the Quality of English Language
Point 1: The English could be improved to more clearly express the research.
Response 1: Thank you for the comment. An English language editor was engaged to assist with grammar and formatting.

 

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have substantially improved the manuscript. I have only one remaining recommendation concerning Table 6. In the “Percentage” column, it would be more appropriate to report the percentage of articles in which the best-practice tasks listed in each row are mentioned, rather than the percentage of the total number of AI-enabled best-practice tasks.

Upon clicking on one of the references, an error message appeared.

DOI Not Found

10.3390/educsci13090856/

This DOI cannot be found in the DOI System.

It includes a trailing slash character which may be wrong. Click here to resolve the DOI without the slash.

It would be advisable to check that all links in the reference list are functioning properly.

Author Response

Response to Reviewer 2
1. Summary
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
2. Questions for General Evaluation	Reviewer’s Evaluation	Response and Revisions
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	Yes	Thank you for the comment.
Are the research design, questions, hypotheses and methods clearly stated?	Yes	Thank you for the comment.
Are the arguments and discussion of findings coherent, balanced and compelling?	Yes	Thank you for the comment.
For empirical research, are the results clearly presented?	Can be improved	The results were presented according to the research questions from pages 18-24.
Is the article adequately referenced?	Must be improved	The references were corrected.
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	Yes	Thank you for the comment.
3. Point-by-point response to Comments and Suggestions for Authors
Comments 1: The authors have substantially improved the manuscript. I have only one remaining recommendation concerning Table 6. In the “Percentage” column, it would be more appropriate to report the percentage of articles in which the best-practice tasks listed in each row are mentioned, rather than the percentage of the total number of AI-enabled best-practice tasks.
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have….
Comments 2: Upon clicking on one of the references, an error message appeared. DOI Not Found 10.3390/educsci13090856/ This DOI cannot be found in the DOI System. It includes a trailing slash character which may be wrong. Click here to resolve the DOI without the slash.
Response 2: Agree. We have, accordingly modified the link and removed the trailing slash and it is now opening.   Comments 3: It would be advisable to check that all links in the reference list are functioning properly.   Response 2: Agree. We have checked all the DOIs and links and they are now working.
4. Response to Comments on the Quality of English Language
Point 1: The English could be improved to more clearly express the research.
Response 1: English language editing was done to improve the expression in the article.

 

 

 

 

 

 

Reviewer 3 Report

Comments and Suggestions for Authors

I have seen that the author(s) have successfully made the revisions I requested. The article can be published in this form.

Author Response

Response to Reviewer 3
1. Summary
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
2. Questions for General Evaluation	Reviewer’s Evaluation	Response and Revisions
Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?	Can be improved	The theoretical lens is provided in the introduction on pages 2, 5, and 24.
Are the research design, questions, hypotheses and methods clearly stated?	Yes	Thank you for the comment.
Are the arguments and discussion of findings coherent, balanced and compelling?	Can be improved	The discussion is linked to the research questions of the study. The sub headings were reduced to 3 to suit the questions from page 24 to 25.
For empirical research, are the results clearly presented?	Yes	Thank you for the comment.
Is the article adequately referenced?	Can be improved	The references were improved.
Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?	Can be improved	Sources were provided on page 26 to link with literature.
3. Point-by-point response to Comments and Suggestions for Authors
Comments 1: I have seen that the author(s) have successfully made the revisions I requested. The article can be published in this form.
Response 1: Thank you for pointing this out. We are grateful for the work you have done in improving our article.
4. Response to Comments on the Quality of English Language
Point 1: The English could be improved to more clearly express the research.
Response 1: English language editing was done to improve the expression.
5. Additional clarifications: The reviewer has recommended publishing in this form and we are grateful for the comment.