Sustainable Innovation: Harnessing AI and Living Intelligence to Transform Higher Education

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The abstract provides a long introduction about what AI can do. Then, it bridges that introduction with the “challenges” AI technologies pose. Then, the “problem” that the author wants to examine is “the transformative impact of AI and living intelligence on higher education, emphasizing their applications, benefits, and challenges.”

-there appears to be a misalignment between the perceived intent (problem) and the gap (challenges) the author wants to address. What is the “problem”?

 

2. Introduction – -which one is the problem?!

problem 1: “This paper explores the transformative potential of AI and living intelligence in higher education, emphasizing their applications, benefits, and associated challenges.”

Problem 2: “… this paper aims to contribute to ongoing discussions about leveraging AI for educational excellence and equity.”

 

3. The proposed outline of the paper: “First reviews key studies on AI in higher education (Section 2). It then examines specific applications of AI and living intelligence, such as personalized learning and adaptive support (Section 3). Challenges like ethical concerns and data privacy are discussed in Section

4. The paper concludes with insights on fostering collaboration and sustainability through AI (Sections 5 and 6).”

- The outline does not deal sufficiently with the problem (whichever is above).

-Assuming that the problem is “exploring the transformative potential…” this outline does not serve it.

 

-Ultimately, the question is: What transformative potential is the paper seeing now? And on what grounds is the author saying these are transformative potential? (the discussion missed it, I think)

Author Response

Response to Reviewer’s Comments

1. Clarifying the Problem Statement

We appreciate the reviewer’s observation regarding the misalignment between the perceived intent and the gap the paper seeks to address. This paper examines the misalignment between AI’s transformative potential and the practical challenges of its responsible implementation in higher education. While AI and living intelligence offer groundbreaking opportunities to enhance learning, assessment, and institutional operations, their adoption is hindered by ethical concerns, data security risks, regulatory challenges, and the need for institutional adaptation.

To clarify, the problem statement now explicitly states:
"This paper explores the transformative potential of AI and living intelligence in higher education, specifically examining the misalignment between AI’s potential benefits and the challenges of responsible implementation, including ethical concerns, equity issues, and institutional readiness."

This revision ensures that the paper does not merely discuss AI’s applications but critically evaluates the gap between AI’s theoretical promise and its real-world constraints, providing a balanced discussion on opportunities and barriers.

2. Addressing the Introduction’s Problem Statement

The reviewer correctly pointed out the ambiguity in the introduction’s problem framing. To resolve this, we have aligned the introduction with the refined problem statement by merging the two perspectives into a cohesive argument. Instead of presenting two competing problem formulations, we now emphasize:

• The transformative potential of AI and living intelligence in enhancing learning experiences, academic administration, and student support.
• The practical barriers preventing seamless AI integration, including ethical risks, institutional resistance, data privacy, and sustainability concerns.
• The need for structured implementation strategies that balance innovation with academic integrity and inclusivity.

By restructuring the introduction, we ensure that the problem statement is not lost in the discussion of AI’s capabilities but instead serves as the focal point guiding the paper’s analysis.

3. Strengthening the Paper’s Outline to Align with the Problem

The reviewer’s concern about the outline not sufficiently addressing the problem is valid. We have revised the outline to explicitly connect each section to the overarching research problem:

• Section 2: Literature Review – Examines previous studies on AI in higher education, highlighting both its potential and limitations. The review now emphasizes documented gaps in AI implementation, such as ethical dilemmas, bias, and equity challenges, providing a foundation for the paper’s argument.
• Section 3: Applications of AI and Living Intelligence – Goes beyond listing AI capabilities and now explicitly discusses how these applications both address and exacerbate the implementation challenges. This section is reframed to analyze the feasibility of AI solutions rather than simply outlining their functions.
• Section 4: Challenges of AI in Higher Education – This section has been expanded and more explicitly integrated into the argument. It does not simply list ethical and technical concerns but now explores how these challenges contribute to the misalignment between AI’s promise and its practical execution.
• Section 5: Human-AI Collaboration and Sustainable AI Integration – Responds directly to the paper’s problem statement by proposing solutions to bridge the gap between AI potential and real-world implementation constraints. This section includes recommendations for governance frameworks, ethical policies, and interdisciplinary collaboration strategies.
• Section 6: Discussion & Conclusion – Explicitly evaluates the current state of AI’s transformative potential, backed by case studies and research findings. The discussion now directly addresses the reviewer’s question: “On what grounds is the author saying these are transformative potential?” The conclusion further reinforces the need for strategic, ethical, and sustainable AI adoption.

4. Justifying the Transformative Potential of AI in Higher Education

To ensure the discussion is grounded in evidence, we have strengthened the justification for AI’s transformative potential. Rather than making broad claims, the paper now provides:

• Empirical studies and case examples demonstrating AI’s ability to improve learning outcomes, efficiency, and equity.
• Comparative analysis showing how AI implementations in other industries (e.g., healthcare and finance) have addressed similar integration challenges.
• Critical assessment of AI adoption rates, institutional resistance, and long-term feasibility, making it clear that AI’s impact depends on structured policy interventions rather than just technological advancement.

By reinforcing these elements, the paper presents a more substantiated argument on what constitutes AI’s transformative potential and how institutions must strategically navigate its implementation.

Final Adjustments and Justification

• These modifications ensure that the paper:
  Clearly defines the problem as the misalignment between AI’s potential and the barriers to responsible adoption.
• Structures the discussion to critically assess both AI’s benefits and the constraints hindering its effectiveness.
• Aligns each section of the paper with the central problem statement, making it clear how AI’s transformative potential is being analyzed and justified.
• Moves beyond descriptive analysis and provides practical recommendations for overcoming AI implementation challenges in higher education.

These revisions directly address the reviewer’s concerns and ensure that the paper provides a cohesive, well-argued examination of AI’s role in higher education

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks. I enjoyed reading this article. This is a meaningful and informative article that demonstrates the future direction of education with AI. The author has well-collected and analyzed the literature regarding AI and living intelligence to transform higher education. However, I have a couple of concerns.

My general perception about this article is that it seems more like a literature review than a well-developed research paper.

First, the author did a good job of summarizing the literature review in Section 2. However, there are only a couple of resources listed in Sections 3 and 4, which makes these sections appear more like an essay than a research paper. Especially in Section 4, the author presents a framework with six innovative extensions without any references, which I believe is unacceptable in an academic article.

Second, the author lists four challenges in Section 5 and discusses possible solutions to address these challenges in the same section. However, to me, the discussion section seems very shallow and lacks in-depth analysis to solve the challenges listed in Section 4. I think this section should be supported by well-researched literature.

Finally, the author summarizes all 14 articles on pages 4-6, which I believe is unnecessary because the author has already mentioned the key points of these articles earlier in the paper.

Well-done. Thanks 

Author Response

Response to Reviewer 2’s Comments

Thank you for your thoughtful feedback and for recognizing the importance of this study in shaping the future direction of AI in higher education. We have carefully considered your concerns and have made substantial revisions to enhance the depth and rigor of the paper.

1. Strengthening Research Depth in Sections 3 and 4

We acknowledge your concern that Sections 3 and 4 previously appeared more like an essay than a research paper due to the limited number of citations. In response, we have significantly expanded these sections by incorporating additional scholarly references to support the discussion on AI applications and the proposed framework of innovative extensions.

• In Section 3, we have strengthened discussions on personalized learning, co-creative educational methods, and adaptive student support by integrating recent studies on AI-driven learning models, faculty training, and adaptive technologies.
• In Section 4, where we initially presented six innovative extensions of AI without sufficient references, we have now included citations from peer-reviewed literature that validate each proposed extension. This ensures that our framework is grounded in established research rather than speculative discussion.

These modifications ensure that Sections 3 and 4 are well-supported by literature, aligning them with academic standards.

2. Expanding the Discussion and Deepening Analysis in Section 5

Your observation that the discussion section was "shallow" and lacked an in-depth analysis of the challenges listed in Section 4 was well taken. To address this:

• We have restructured Section 5 to provide a more critical and analytical discussion of AI-related challenges in higher education, such as academic integrity, algorithmic bias, and sustainability concerns.
• We have supplemented our discussion with empirical studies and case-based analyses, ensuring that our proposed solutions are supported by real-world applications and scholarly perspectives.
• We have included references to studies on ethical AI frameworks, faculty training programs, and interdisciplinary collaboration models that institutions are currently implementing to mitigate AI’s risks.

These enhancements ensure that the discussion section offers both theoretical and practical insights into solving the challenges identified earlier in the paper.

3. Refining the Literature Review and Avoiding Redundancy

We understand your concern regarding the redundancy of summarizing all 14 articles separately on pages 4-6, given that their key points were already discussed throughout the paper. To improve the flow and reduce redundancy:

• We have condensed the literature summaries and integrated them within relevant sections instead of listing them separately.
• We have ensured that each referenced study contributes to the argument and analysis rather than appearing as a detached review.
• Our revised literature synthesis highlights thematic connections, making it easier for readers to understand how each study contributes to the paper’s objectives.

These changes make the literature review more cohesive and ensure that it directly supports the paper's key discussions.

We sincerely appreciate your constructive feedback, which has been instrumental in strengthening this paper. With the expanded citations in Sections 3 and 4, deeper analysis in Section 5, and a more integrated literature review, we believe that the paper now presents a well-developed research study rather than just a literature review. These revisions reinforce the transformative role of AI in higher education while ensuring academic rigor and credibility.

Thank you again for your insightful review and for helping us enhance the quality of this paper.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

To the author/s,

Thank you for your thorough and well-considered revisions. I appreciate your effort in refining the problem statement, restructuring the introduction, and aligning the paper’s outline with the central research question. The clarified problem statement now provides a strong foundation for the discussion, ensuring that the paper critically evaluates AI’s transformative potential and the challenges of responsible implementation.

The restructuring of the introduction and the outline improvements effectively address my concerns, particularly by making explicit connections between AI’s capabilities and the barriers to its integration. The additional empirical evidence and case studies strengthen the justification for AI’s transformative role in higher education, moving beyond broad claims to a well-supported argument.

Overall, these revisions significantly enhance the paper's clarity, coherence, and analytical depth. I have no further concerns now and am pleased with the improvements made.