Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility

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AI offers adaptive tutoring systems, data-driven insights, and customized learning experiences, which can significantly improve the educational process. However, the rapid adoption of AI technologies also raises important ethical concerns that must be addressed to ensure responsible implementation. This paper provides an overview of AI’s potential in education, while highlighting key ethical issues such as data privacy, algorithmic bias, transparency, and equitable access to AI-powered tools. Through an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in educational contexts. A collaborative effort among educators, policymakers, and technology developers is essential to build ethical standards that balance innovation with fairness, accountability, and inclusivity. Ultimately, this paper offers insights and practical recommendations for fostering a responsible AI-driven educational environment that benefits all students while safeguarding their rights." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-299", "name": "Navigating the ethical landscape of AI integration in education: Balancing..." } } ] } Home Browse Navigating the ethical landscape of AI integration in education: Balancing... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Azman Ö and Tümkaya S. Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.12688/f1000research.160011.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Review Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] Özlem Azman https://orcid.org/0000-0001-8737-0992 1 , Songül Tümkaya 2 Özlem Azman https://orcid.org/0000-0001-8737-0992 1 , Songül Tümkaya 2 PUBLISHED 14 Mar 2025 Author details Author details 1 Faculty of Education, Cukurova Universitesi, Adana, Turkey 2 Faculty of Education, Cukurova University, Adana, Turkey Özlem Azman Roles: Conceptualization, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Songül Tümkaya Roles: Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract The integration of Artificial Intelligence (AI) in education presents transformative opportunities to personalize learning, enhance teaching methods, and improve student outcomes. AI offers adaptive tutoring systems, data-driven insights, and customized learning experiences, which can significantly improve the educational process. However, the rapid adoption of AI technologies also raises important ethical concerns that must be addressed to ensure responsible implementation. This paper provides an overview of AI’s potential in education, while highlighting key ethical issues such as data privacy, algorithmic bias, transparency, and equitable access to AI-powered tools. Through an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in educational contexts. A collaborative effort among educators, policymakers, and technology developers is essential to build ethical standards that balance innovation with fairness, accountability, and inclusivity. Ultimately, this paper offers insights and practical recommendations for fostering a responsible AI-driven educational environment that benefits all students while safeguarding their rights. READ ALL READ LESS Keywords AI Ethics, AI and Education, Artificial Intelligence (AI) Corresponding Author(s) Özlem Azman ( [email protected] ) Close Corresponding author: Özlem Azman Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Azman Ö and Tümkaya S. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Azman Ö and Tümkaya S. Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.12688/f1000research.160011.1 ) First published: 14 Mar 2025, 14 :299 ( https://doi.org/10.12688/f1000research.160011.1 ) Latest published: 14 Mar 2025, 14 :299 ( https://doi.org/10.12688/f1000research.160011.1 ) Introduction The educational landscape is undergoing significant transformation due to the influence of Artificial Intelligence (AI), bringing forth both remarkable innovations and complex ethical challenges. With the increasing integration of AI technologies in educational settings, it becomes crucial to balance their transformative potential with ethical considerations. This article aims to examine the dual impact of AI in education, highlighting its potential advancements and addressing the ethical concerns associated with its implementation. AI’s influence in education extends beyond simple automation, offering personalized education strategies, adaptable tutoring technologies, and decision-making based on data analysis ( Brown, 2020 ; Siemens, 2013 ). These advancements promise to revolutionize teaching methodologies, enhance student engagement, and improve learning outcomes ( Luckin et al., 2016 ). For example, AI-powered platforms like intelligent tutoring systems offer tailored evaluations and support to students, fostering individualized learning ( VanLehn, 2011 ). Additionally, AI can analyze large volumes of educational data to uncover patterns and insights that inform curriculum development and teaching strategies ( Siemens & Long, 2011 ). Table 1 provides an overview of AI applications in education, detailing how various technologies enhance learning through personalized instruction, adaptive assessments, intelligent tutoring, administrative efficiency, and data-driven insights. Table 1. AI applications in education. Application Description Example Personalized Learning Platforms Customizes curriculum based on student performance Knewton, DreamBox Adaptive Assessment Tools Adjusts difficulty of questions based on student responses ALEKS, Smart Sparrow Intelligent Tutoring Systems Provides one-on-one tutoring experiences, offering hints and feedback Carnegie Learning's Cognitive Tutor Administrative Automation Automates tasks such as grading, scheduling, and student inquiries Chatbots, Virtual Assistants Learning Analytics Analyzes data to identify patterns, inform strategies, and predict outcomes Early warning systems for at-risk students Despite these transformative benefits, ethical issues such as privacy, transparency, and equity must be addressed ( Williamson, 2017 ; Tene & Polonetsky, 2013 ). The implementation of AI in education raises concerns about data security and student privacy due to the large quantities of sensitive information being collected and analyzed ( Binns, 2018 ). In addition to existing concerns, algorithmic bias presents a risk where AI technologies might reinforce entrenched inequalities or give rise to new forms of discrimination, highlighting the need for careful consideration in AI development ( Noble, 2018 ). The opacity of AI decision-making processes often makes it difficult for educators and students to understand how conclusions are reached ( Burrell, 2016 ). Giray, Jacob, and Gumalin (2024) emphasized significant ethical concerns associated with AI, particularly in relation to data privacy, informed consent, and inherent biases. This article explores how educators, policymakers, and stakeholders can navigate these challenges. By examining current practices, emerging trends, and ethical frameworks, the aim is to offer insights into fostering responsible AI integration in education. This approach not only mitigates potential risks but also maximizes AI’s positive impact on educational practices and student learning experiences. To address these concerns, developing and implementing ethical guidelines and policies governing AI use in education is crucial. The comprehensive guidelines for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems ( IEEE, 2016 ). Additionally, building a culture that supports openness and ethical responsibility in the creation of artificial intelligence systems and application can build trust among educators, students, and other stakeholders ( Floridi et al., 2018 ). Promoting ethical AI practices ensures that the advantages of AI in education are implemented while safeguarding the rights and interests of all involved. The promise of AI in education AI in educational settings offers a variety of applications, such as personalized learning platforms, adaptive assessment tools, and intelligent tutoring systems ( Holstein & McLaren, 2019 ; Baker & Siemens, 2015 ). These technologies Utilize machine learning techniques to process large datasets, offering adaptive learning experiences that cater to the distinct needs and learning styles of each student. Personalized learning platforms, such as those developed by Knewton and DreamBox, adjust the curriculum alligned with student performance, offering customized pathways that enhance learning efficiency ( Pane et al., 2017 ). Iterative evaluation tools dynamically alter question difficulty according to student answer patterns, ensuring that assessments accurately reflect student capabilities and knowledge ( Conati, 2002 ). Smart tutoring platforms, such as Carnegie Learning’s Cognitive Tutor, simulate human tutor interactions by providing one-on-one tutoring experiences, offering hints and feedback as students work through problems ( Koedinger & Corbett, 2006 ). Beyond these applications, AI also plays a significant role in reducing administrative burdens through the automation of tasks like grading and scheduling helps educators focus on teaching and engaging with students ( Holmes & Tuomi, 2022 ). Artificial Intelligence-powered chatbots and virtual assistants provide automated support and information to users. handle student inquiries, providing instant responses and support, which enhances the overall student experience ( Woolf et al., 2013 ). Furthermore, AI assists in identifying at-risk students early by analyzing patterns in attendance, participation, and academic performance, enabling timely interventions ( Arnold & Pistilli, 2012 ). AI holds the promise to revolutionize education through offering personalized learning experiences, boosting administrative efficiency, and supporting one significant benefit of AI in education is the use of data-informed decision-making strategies to facilitate personalized learning which allows to create effective learning experiences, educational content must be adapted to better suit the specific requirements of each student. AI-driven systems adapt the pace and style of teaching based on the learner’s progress and preferences, cultivating a dynamic and impactful setting for effective education ( Luckin et al., 2016 ). AI-driven adaptive learning systems tailor task complexity according to student performance, presenting more difficult questions to advanced learners and giving extra help to those who require it ( Kulik & Fletcher, 2016 ). For instance, platforms like DreamBox and Knewton employ AI algorithms to evaluate students’ interactions and deliver customized recommendations, improving learning outcomes through tailored instructional strategies ( Pane et al., 2017 ). Moreover, AI significantly enhances administrative tasks in educational institutions. By automating routine tasks, like grading, scheduling, and student record management, educators can liberate important time, enabling them to devote more attention to teaching and engaging with students ( Holmes & Tuomi 2022 ). AI systems efficiently handle tons of data, managing administrative burdens and increasing overall efficiency. AI’s power to work with large amounts of datasets enables data-based decisions in education. Tracking student learning progress, a field that leverages AI to interpret educational data, provides clarity on student achievements, identifies students who need more support, and informs policy decisions ( Siemens & Long, 2011 ). By tracking and analyzing student data, educators achieve a better grasp of patterns in student learning and outcomes, leading to more informed and effective educational strategies ( Ifenthaler & Widanapathirana, 2014 ). The integration of AI in education extends beyond academic support to fostering soft skills and social-emotional learning. AI-driven tools facilitate the development of critical thinking, problem-solving, and collaboration skills by creating interactive and immersive learning experiences ( Zawacki-Richter et al., 2019 ). For instance, AI-supported Virtual Reality (VR) and Augmented Reality (AR) solutions offer students with hands-on learning opportunities in a controlled environment, enhancing engagement and retention ( Liu et al., 2017 ). However, unlocking AI’s full potential in education necessitates overcoming a range of complex challenges. A critical component of this process is the need to guarantee equitable access to AI technologies to mitigate the risk of deepening existing educational disparities ( Romero & Ventura 2016 ). Furthermore, the integration of AI tools into teaching practices requires a deliberate and continuous effort in professional development for educators, focusing on both advanced technological skills and innovative pedagogical approaches ( Chen et al., 2020 ). By overcoming these obstacles and harnessing AI’s full potential, the education sector has the opportunity to pioneer new teaching methodologies and innovative learning experiences, ultimately achieving better educational outcomes and preparing students for future endeavors. Enhancing education through AI-supported learning methods AI-supported learning methods offer numerous advantages over traditional educational approaches. One significant benefit is the facilitation of real-time feedback mechanisms, allowing students to receive immediate responses and personalized guidance ( VanLehn, 2011 ). This immediacy helps students correct misconceptions promptly and reinforces learning through continuous interaction. Additionally, AI-powered systems can adaptively adjust content and pacing based on student performance, optimizing learning efficiency and retention ( Siemens & Long, 2011 ). Studies have shown that adaptive learning technologies can significantly improve student outcomes, particularly in subjects requiring cumulative knowledge, such as mathematics and science ( Beck & Gong, 2013 ). Moreover, AI can enhance engagement by incorporating gamification elements and interactive simulations into the learning process ( Chen et al., 2020 ). The application of AI in the creation of educational games and immersive learning experiences can transform traditional learning environments into more enjoyable and interactive settings, leading to increased student motivation and participation ( Johnson et al., 2016 ). AI-supported learning methods also promote inclusivity by providing personalized learning experiences for students with diverse needs and abilities. For instance, AI can assist students with disabilities by offering customized support, such as speech-to-text services for students with hearing impairments or tailored reading materials for students with dyslexia ( Luckin et al., 2016 ). By providing tailored learning experiences, this approach ensures that all students, irrespective of their unique challenges, receive equitable access to high-quality educational resources and instruction. The strategic integration of AI in educational contexts holds promise for improving student outcomes by enhancing engagement, motivation, and academic achievement ( Baker, 2016 ). The integration of AI technologies into educational environments enables the creation of personalized learning pathways that facilitate self-paced progress, thereby enhancing students’ ability to achieve effective mastery of subjects according to their individual learning needs ( Pane et al., 2017 ). There is robust evidence that individualized learning frameworks enhance student satisfaction and reduce dropout rates, especially within the context of higher education ( Zawacki-Richter et al., 2019 ). AI technologies also hold the potential to facilitate teachers in developing more effective instructional strategies and personalized interventions for struggling students ( Holstein & McLaren, 2019 ). For instance, AI can analyze classroom data to identify which teaching methods are most effective for different types of learners, allowing teachers to tailor their instruction accordingly ( Siemens, 2013 ). AI can also provide teachers with detailed reports on student performance, highlighting areas where students might require additional support or further enrichment opportunities ( Ifenthaler & Widanapathirana, 2014 ). Furthermore, the application of AI in data analytics can unveil critical insights into learning patterns and educational trends, informing evidence-based decision-making at institutional levels ( Siemens, 2013 ). Educational institutions can use these insights to design more effective curricula, allocate resources more efficiently, and implement policies that enhance overall educational quality. For example, predictive analytics can help universities anticipate enrollment trends and adjust their offerings to meet future demand ( Arnold & Pistilli, 2012 ). Recent developments in educational technologies highlighting the convergence of AI with cutting-edge innovations like virtual reality (VR), augmented reality (AR), and gamification, These advancements aim to foster immersive learning experiences that engage students and enhance knowledge retention ( Luckin et al., 2016 ). Moreover, AI-driven analytics tools offer insights into learning analytics and educational data mining, enabling educators to adopt data-informed approaches to enhance teaching strategies and elevate student performance ( Siemens & Long, 2011 ). In conclusion, AI’s contribution to education is evolving rapidly, offering transformative opportunities to innovate teaching, and learning practices. Nevertheless, while the prospective advantages are considerable, it is crucial to adress the ethical dimensions and secure responsible deployment to maximize educational equity and accessibility. Addressing issues such as data privacy, algorithmic bias, democratic access to AI technologies is essential to guarantee that the advantages of AI are allocated fairly across all student populations ( Williamson, 2017 ). By fostering a collaborative approach involving educators, policymakers, and technology developers, we have the opportunity to cultivate a more inclusive and effective educational framework that supports diverse learners and enhances overall educational effectiveness. Ethical challenges in AI integration As Artificial Intelligence (AI) becomes increasingly embedded in educational practices, it raises complex ethical issues that demand critical scrutiny and the development of effective ethical guidelines and necessitate careful consideration. This section examines the ethical dilemmas associated with the deployment of AI in educational settings, addressing concerns such as data privacy, student safety, and the transparency of learning processes. It also discusses the standards and policies required for fair and transparent AI-supported decision-making processes. Privacy and security of data As AI tools are integrated into educational practices, the resulting collection and analysis of large datasets highlight critical issues concerning data privacy and security. AI systems require access to detailed information about students’ academic performance, personal characteristics, and even behavioral patterns to provide personalized learning experiences ( Binns, 2018 ). This level of data collection poses risks of unauthorized access, breaches of security, and the potential for sensitive information to be misused. Ensuring robust data protection measures is crucial to safeguarding student privacy. Educational institutions must implement stringent security protocols to design and apply protocols that prevent data breaches and unauthorized access. Additionally, it is essential to formulate explicit policies regarding data usage, ensuring that students and their guardians are fully informed about how their data is being gathered, archived, and utilized ( Tene & Polonetsky, 2013 ). Protecting data privacy, ensuring student safety, and maintaining the transparency of AI-driven learning processes are critical considerations. Educators and policymakers must establish robust protocols for data collection, storage, and usage to safeguard sensitive information and uphold student trust ( Selwyn, 2020 ). Transparent communication about the purposes and implications of AI innovations in the educational sphere is essential to promote accountability and mitigate potential risks ( Barocas & Selbst, 2016 ). Algorithmic bias and fairness Algorithmic bias is a critical ethical issue in the deployment of AI systems in education. When AI algorithms are constructed from comprehensive datasets, they may unintentionally perpetuate inherent biases in the data ( Noble, 2018 ), leading to discriminatory practices against specific population of students, perpetuating existing inequalities or even creating new forms of discrimination. For example, AI algorithms used in student assessment and grading may inadvertently perpetuate biases if not properly monitored and calibrated ( Lipton, 2018 ). To counteract this threat, it is essential to uphold that AI algorithms are designed and tested with fairness in mind. This entails employing diverse and representative datasets, performing regular bias audits, and developing mechanisms to identify and correct biased outcomes ( Binns, 2018 ). Transparency and accountability To ensure that educators, students, and other stakeholders trust AI systems, it is essential for these systems to be transparent in their decision-making processes, given that they often function as ‘black boxes’ ( Burrell, 2016 ). The opacity of these systems can hinder ethical accountability and make it difficult to address any errors or biases that may arise. To promote transparency, AI developers and educational institutions should be dedicated to fostering the explainability of AI systems. This means designing algorithms that can provide clear and understandable explanations for their decisions and actions ( Floridi et al., 2018 ). Moreover, establishing accountability frameworks is vital to make sure that there are effective mechanisms available to address grievances and rectify any negative impacts resulting from AI usage ( Williamson, 2017 ). Developing standards and policies that promote fairness and clarity in AI-supported decision-making procedures is imperative. This includes implementing algorithmic accountability measures, ensuring diversity and inclusivity in dataset representation, and empowering stakeholders with the knowledge and tools to navigate AI-driven educational environments responsibly ( Diakopoulos, 2014 ; European Commission, 2018 ). By addressing these ethical challenges, the educational sector can leverage AI technologies to enhance learning outcomes while safeguarding the rights and interests of students. Through collaborative efforts among educators, policymakers, and technology developers, it is possible to create a learning environment that fosters inclusivity and fairness to ensure that the benefits of AI are fully realized while mitigating associated risks. Current implementations and ethical frameworks Implementations of AI in education AI’s integration into educational practices make use of various applications to boost teaching effectiveness and improve student learning outcomes. Personalized learning platforms, adaptive assessment tools, and intelligent tutoring systems are at the forefront of these advancements ( Holstein & McLaren, 2019 ; Baker & Siemens, 2015 ). These technologies leverage machine learning algorithms to assess extensive datasets to identify, generating individualized learning experiences that accommodate the specific needs and preferences of students. Personalized learning platforms, such as those developed by Knewton and DreamBox, modify the curriculum in response to the performance of students, offering customized pathways that enhance learning efficiency ( Pane et al., 2017 ). These platforms analyze students’ interactions to provide personalized recommendations, improving learning outcomes through tailored instructional strategies. Adaptive assessment tools dynamically alter question difficulty according to student responses, ensuring that assessments accurately reflect student capabilities and knowledge ( Conati, 2002 ). Innovative systems for personalized instruction, such as Carnegie Learning’s Cognitive Tutor, simulate human tutor interactions, providing one-on-one tutoring experiences, hints, and feedback as students work through problems ( Koedinger & Corbett, 2006 ). Beyond personalized learning, AI plays a significant role by streamlining administrative tasks such as grading and scheduling, teachers are freed up to focus more on direct instruction and supporting students interaction ( Holmes & Tuomi 2022 ). AI-enabled chatbots and virtual assistant systems handle student inquiries, providing instant responses and support, which enhances the overall student experience ( Woolf et al., 2013 ). Furthermore, AI can assist in identifying at-risk students early by analyzing patterns in attendance, participation, and academic performance, enabling timely interventions ( Arnold & Pistilli, 2012 ). Ethical frameworks for AI infused education The rapid deployment of AI technologies in education necessitates the establishment of ethical frameworks to address the accompanying ethical concerns. Several organizations and initiatives have developed guidelines and standards to ensure responsible AI integration in educational settings. Guidelines for ethical AI deployment are comprehensively addressed by the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, stressing the importance of transparency, accountability, and inclusivity ( IEEE, 2016 ). These guidelines advocate for the development of AI systems crafted to prioritize ethical considerations and responsible practices, ensuring that they respect human rights and promote social good. Similarly, In its 2018 report, the European Commission’s High-Level Expert Group on Artificial Intelligence established detailed ethical guidelines for trustworthy AI, which include a broad range of principles such as human oversight, technical safety, data governance, transparency, diversity, non-discrimination, societal well-being, and accountability ( European Commission, 2018 ). These principles serve as a foundation for developing and implementing AI systems that prioritize ethical considerations. Creating a culture of transparency and accountability in AI development and application is vital for establishing trust with educators, students, and various other stakeholders ( Floridi et al., 2018 ). This involves clear communication about how AI systems work, their potential benefits and risks, and the measures taken to ensure their ethical use. Advancing ethical AI practices helps to maximize the benefits of AI in education while protecting the rights and interests of everyone involved. For educators to integrate AI tools into their teaching practices effectively, institutions must offer comprehensive and continuous professional training, covering ethical issues, data interpretation, and bias management ( Chen et al., 2020 ). Synchronizing technological advancements with ethical considerations Achieving a harmonious balance between innovation and ethics is essential for maximizing AI’s effective use in education. To promote ethical AI use, educators can adopt strategies such as participatory design processes that involve stakeholders in decision-making, conducting ethical impact assessments of AI applications, and fostering a culture of ethical awareness and responsibility ( Bietz et al., 2010 ; Van den Broek et al., 2024 ). These approaches ensure that AI technologies align with educational values and goals while minimizing unintended consequences. Educational institutions should prioritize ethical rules, including transparency in decision-making, equality, and accountability in governance, as well as inclusive practices when integrating AI technologies ( Floridi et al., 2018 ). Establishing clear guidelines for data governance, promoting ethical leadership among educators and administrators, and fostering interdisciplinary collaboration are essential steps in upholding ethical standards throughout AI deployment in educational settings ( Williamson, 2017 ). Looking ahead, future studies should focus on advancing ethical frameworks for AI in education, exploring the ethical implications of emerging technologies, and developing adaptive regulatory frameworks that can evolve alongside technological advancements ( European Parliament, 2020 ). By proactively addressing ethical challenges and leveraging innovation responsibly, educational institutions can harness AI’s full potential to foster. Balancing innovation and ethical responsibility Optimizes the educational benefits of AI while managing ethical considerations, a balanced approach is necessary, balancing progressive innovation with ethical guidelines is important to ensure fairness and equity. This involves adopting a comprehensive approach that includes stakeholder involvement, continuous evaluation, and adherence to ethical principles. Strategies for Ethical AI Integration. Participatory design processes Involving a broad spectrum of primary stakeholders, such as teachers, learners, parents, and policy advisors, need to be included in the development and implementation of AI systems ensures that these technologies meet the diverse needs of the educational community. This collaborative approach helps build trust and promotes the creation of AI tools that are in alignment with educational values and objectives. According to Bietz et al. (2010) , such inclusive design processes can help identify and mitigate potential biases and ethical issues early on. Van den Broek et al. (2024) highlights that participatory design boosts the usability and relevance of AI tools while simultaneously increasing user acceptance and satisfaction. Ethical impact assessments Thorough ethical impact assessments of AI applications can help identify potential risks and unintended consequences. These assessments should consider aspects such considerations as algorithmic bias, data privacy, algorithmic bias, and the broader social implications of AI deployment in education. Floridi and Cowls (2019) suggest that ethical impact assessments should be an ongoing process, evolving in conjunction with AI systems and their practical implementations, Tene and Polonetsky (2013) argue that proactively identifying risks can lead to better-designed systems that protect user interests and maintain public trust. Continuous professional development Providing ongoing training and support for educators is essential to incorporate AI technologies effectively into the teaching process. This includes equipping teachers with the knowledge and skills to interpret AI-generated insights, address biases, and ensure equitable access to AI-enhanced educational resources. Chen et al. (2020) underscores the importance of continuous programs that develop technical abilities, foster ethical consciousness, and encourage critical examination of AI. By staying informed about the most recent innovations in AI and their implications, educators can better navigate the complexities of AI integration in education. Transparent communication Maintaining transparency about how AI systems work, their potential benefits and risks, and the measures taken to ensure their ethical use is crucial for building trust among stakeholders. Clear communication fosters accountability and helps mitigate potential ethical concerns. Barocas and Selbst (2016) note that transparency involves not only disclosing technical details but also explaining the decision-making processes and the rationale behind AI-driven outcomes. Transparent communication can demystify AI technologies and empower stakeholders to make informed decisions. Adjustable regulatory frameworks It is imperative to develop regulatory frameworks that evolve in alignment with technological advancements for ensuring the accountable use of AI in education. These frameworks should prioritize key values in ethical norms: transparency, fairness, accountability, and inclusivity The European Parliament (2020) recommends that regulatory frameworks should balance flexibility for new developments with the need for clear and consistent guideline for ethical AI deployment. By establishing a robust regulatory environment, policymakers can create a foundation for sustainable and responsible AI integration in education. Inclusive data practices Confirming that the data utilized for AI training systems in education is representative and inclusive of diverse populations is crucial. This involves actively working to avoid data biases that could perpetuate inequalities. Implementing stringent data governance practices and regularly auditing datasets can help maintain fairness and accuracy ( Mehrabi et al., 2021 ). Ensuring inclusivity requires a comprehensive approach that includes collecting data from diverse student populations, addressing potential biases in the data collection process, and continuously updating datasets to reflect changing demographics and educational needs. This not only helps in providing equitable educational outcomes but also promotes a more inclusive and supportive learning environment. For example, a study by Buolamwini and Gebru (2018) highlighted the disparities in facial recognition technologies, emphasizing the need for diverse data sets to avoid bias and inaccuracies in AI applications. By prioritizing inclusivity, AI systems can provide more equitable educational outcomes for all students, ensuring that no group is disproportionately disadvantaged. Ethical review boards Establishing ethical review boards within educational institutions is essential for ensuring ongoing oversight of AI projects. These boards, composed of ethicists, educators, technologists, and student representatives, play a crucial role in evaluating AI initiatives from multiple perspectives, thus identifying potential ethical issues before they escalate and ensuring alignment with institutional values and societal norms ( Morley et al., 2019 ). The review process begins with researchers or developers submitting AI project proposals to the ethical review board. The board conducts a preliminary screening to ensure proposals meet basic ethical standards and institutional guidelines. Proposals that pass this initial screening undergo a detailed ethical assessment, focusing on aspects Considerations include aspects like algorithmic bias, data privacy, and the possible effects on society. Stakeholder consultation is a vital part of the process, where the board gathers diverse perspectives from educators, students, and technologists on the ethical implications of the proposed AI project. Following this, the board deliberates on the findings, identifying potential ethical issues and recommending necessary modifications ( Matthias, 2021 ). The project team then receives feedback and recommendations from the review board, addressing any ethical concerns and suggesting changes to the proposal. The revised proposal, incorporating the board’s feedback, is submitted for final approval. If all ethical concerns are adequately addressed, the proposal is approved for implementation. Even after approval, the board continues to monitor the AI project during and after its implementation to ensure ongoing compliance with ethical standards. This ongoing oversight helps promote a culture of ethical consciousness and accountability in the institution. The following flowchart ( Figure 1 ) is a conceptual flowchart for the AI ethical review process, illustrating the steps taken to ensure ethical compliance in AI projects: Figure 1. AI ethical review process. Student empowerment and digital literacy Empowering students with digital literacy skills is essential for them to navigate and critically engage with AI technologies. Educational programs should focus on teaching, providing students with knowledge about the ethical aspects of AI, data privacy, and algorithmic decision-making. By fostering a deeper understanding of AI, students can become more informed and responsible users and advocates for ethical AI practices ( Livingstone, 2018 ). This involves integrating digital literacy into the curriculum at all educational levels, ensuring that students understand both the technical and ethical aspects of AI. Programs like Code.org and AI4All are examples of initiatives that aim to increase AI literacy among students, providing them with the skills needed to engage with AI responsibly. Additionally, hands-on projects and real-world applications can help students understand the practical implications of AI, preparing them to address ethical challenges in their future careers ( Holmes, Bialik, & Fadel, 2023 ). Case studies and best practices Documenting and sharing case studies of successful and ethically sound AI implementations in education can serve as valuable resources for other institutions. Highlighting best practices and lessons learned can guide educators and policymakers in making informed decisions about AI integration. These case studies can also provide practical insights into overcoming common challenges and pitfalls ( Zawacki-Richter et al., 2019 ). For example, the AI-powered tutoring system implemented at Georgia State University has been widely recognized for improving student retention rates while adhering to ethical standards. Detailed documentation of such initiatives, including the processes and frameworks used, can serve as a blueprint for other institutions. Furthermore, platforms like ISTE, or the International Society for Technology in Education, supports educators with innovative technology practices provide resources and forums for sharing best practices and case studies, fostering a collaborative approach to ethical AI integration in education ( ISTE, 2017 ). International collaboration and standards Promoting international collaboration and developing global principles for AI in education can serve harmonize ethical practices across different regions and cultures. By working together, countries can share knowledge, resources, and regulatory frameworks to address common ethical challenges. For example, international bodies such as UNESCO can play a pivotal role in facilitating such collaboration and standardization efforts ( UNESCO, 2019 ). Developing global standards ensures that ethical considerations are uniformly addressed, regardless of regional differences. Moreover, international collaboration can lead to the creation of a global repository of best practices and guidelines, helping institutions worldwide implement AI in education responsibly and ethically. Collaborative efforts can also foster cross-cultural understanding and ensure that AI systems accommodate cultural diversity, align with ethical values, ongoing assessment and improvement are required. Effectively balancing innovation and ethical responsibility in AI’s role in education requires a multi-faceted approach. Stakeholders must engage in participatory design, conduct ethical impact assessments, provide continuous professional development, maintain transparent communication, develop adaptive regulatory frameworks, ensure inclusive data practices, establish ethical review boards, empower students with digital literacy, document and share case studies, and promote international collaboration. By integrating these strategies, the educational community can deploy AI to elevate educational outcomes while safeguarding ethical standards and public trust. This holistic strategy guarantees that AI technologies are not only innovative but also fair, transparent, and aligned with the values of the educational community. To effectively balance innovation and ethical responsibility, various stakeholders in the education sector need to take proactive steps. Educators and administrators should implement ethical guidelines that include protocols for data governance, algorithmic accountability, and student privacy ( Floridi et al., 2018 ). Promoting ethical leadership among educators and administrators by encouraging a culture of responsibility and ethical awareness is also essential ( Williamson, 2017 ). Additionally, engaging in continuous professional development programs helps educators keep informed about new AI breakthroughs and their ethical implications ( Chen et al., 2020 ). Policymakers should develop comprehensive policies that overcome the ethical challenges brought by AI in education, ensuring these policies are adaptable to future technological developments ( European Commission, 2018 ). Making AI technologies accessible to all students, no matter their socioeconomic situation, is vital to stopping the digital divide from widening. Technology developers must ensure that ethical implications in the strategizing and creating of AI systems. This necessitates addressing possible biases, protecting personal data and promoting transparency ( Diakopoulos, 2014 ). Collaboration with educators and other stakeholders is necessary to develop AI tools aligned with educational aims and ideals ( Bietz et al., 2010 ). Researchers should focus on advancing ethical frameworks for AI in education, exploring the ethical implications of emerging technologies, and developing adaptive regulatory frameworks ( European Parliament, 2020 ). Continuously evaluating the impact of AI on educational practices and outcomes is vital to inform evidence-based policy decisions and ethical guidelines ( Siemens & Long, 2011 ). Conclusion The adoption of AI in educational settings creates transformative potential to improve teaching methods and learning outcomes. It personalizes learning experiences, elevates student participation, and provides teachers with critical insights into student performance. Despite its benefits, it also brings up significant ethical dilemmas that must be meticulously addressed to make sure that AI is deployed responsibly, and its benefits are maximized while minimizing potential risks. As Yahaya et al. (2023) assert, the ongoing AI revolution is reshaping business and societal landscapes, underscoring the importance of developing theoretical frameworks for understanding ethical responsibility. Holmes et al. (2021) explored the ethical implications of AI, particularly focusing on issues like liability, biased decision-making, and data privacy. These concerns have been widely discussed by both scholars and global organizations, leading to the development of various ethical frameworks. A prominent example is the Montréal Declaration for Responsible Development of Artificial Intelligence (2018) , which promotes human-centered principles such as fairness, respect for autonomy, and responsibility. In the realm of AI in education (AIED), similar ethical challenges arise, especially regarding the handling of student data, the potential for bias, and the protection of privacy. Additionally, there are deeper ethical considerations specific to education, including the role of pedagogy, student agency, and equitable access to learning opportunities ( Holstein et al., 2019 ; Tarran, 2018 ). Adopting a balanced approach that emphasizes ethical responsibility is essential for harnessing AI’s potential to foster equitable and inclusive learning environments. This involves integrating ethical standarts like transparency, accountability, fairness, and inclusivity in the creation and application of AI technologies. For example, transparency in AI algorithms and decision-making mechanisms can contribute to cultivate trust among students, parents, and educators ( Floridi & Cowls, 2019 ). Fairness involves ensuring that AI systems do not perpetuate biases or inequalities, which is crucial for promoting equity in education ( Barocas & Selbst, 2016 ). Continuous collaboration among educators, policymakers, technology developers, and researchers is essential to verify the responsible application of AI technologies effectively. Such collaboration can lead to the creation of comprehensive ethical frameworks and guidelines to oversee the use of AI in education use in education. Participatory design processes involving diverse stakeholders can help identify potential ethical issues early and develop solutions that address the needs and concerns of all parties ( Bietz et al., 2010 ). By emphasizing ethical values like transparency, accountability, fairness, and inclusivity, educational institutions can navigate the challenges posed by AI integration. This involves establishing clear guidelines for data governance, promoting ethical leadership among educators and administrators, and fostering interdisciplinary collaboration ( Williamson, 2017 ). These measures can play a role in making sure AI technologies are applied in ways that enhance educational outcomes without compromising ethical standards. Looking ahead, future research should prioritize advancing ethical protocols or norms for AI in education, exploring the ethical implications of emerging technologies, and developing adaptive regulatory frameworks that can evolve alongside technological advancements ( European Parliament, 2020 ). Future investigations should focus on how AI technologies affect student learning over extended periods, privacy, and equity is essential for informing policy decisions and ensuring that AI applications in education are both effective and ethically sound ( Holmes, Bialik, & Fadel, 2023 ). The education sector can navigate the challenges posed by AI integration by fostering a culture of ethical awareness and responsibility. This comprehensive approach ensures That the advantages of AI are leveraged while safeguarding the rights and interests of all stakeholders involved. By promoting ethical practices and continuous collaboration, we can achieve a harmonious balance between innovation and ethical considerations, ultimately enhancing the quality and accessibility of education for all students. Ensuring that all students benefit from advanced educational resources and opportunities is a shared responsibility that requires commitment from all sectors involved in education. 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Palgrave Macmillan; 2020; pp. 101–124. Publisher Full Text Zawacki-Richter O, Marín VI, Bond M, et al. : Systematic review of research on Artificial Intelligence Applications in higher education – where are the educators? Int. J. Educ. Technol. High. Educ. 2019; 16 (1): 1–27. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 14 Mar 2025 ADD YOUR COMMENT Comment Author details Author details 1 Faculty of Education, Cukurova Universitesi, Adana, Turkey 2 Faculty of Education, Cukurova University, Adana, Turkey Özlem Azman Roles: Conceptualization, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Songül Tümkaya Roles: Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 14 Mar 2025, 14:299 https://doi.org/10.12688/f1000research.160011.1 Copyright © 2025 Azman Ö and Tümkaya S. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Azman Ö and Tümkaya S. Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.12688/f1000research.160011.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 14 Mar 2025 Views 0 Cite How to cite this report: Opesemowo OAG. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r371275 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-371275 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Nov 2025 Oluwaseyi Aina Gbolade Opesemowo , University of Johannesburg, Johannesburg, South Africa Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.175814.r371275 I am pleased to have reviewed this manuscript titled “Navigating the Ethical Landscape of AI Integration in Education: Balancing Innovation and Responsibility” This assessment combines structural, conceptual, and methodological critique anchored in peer review best practices and aligned with academic ... Continue reading READ ALL I am pleased to have reviewed this manuscript titled “Navigating the Ethical Landscape of AI Integration in Education: Balancing Innovation and Responsibility” This assessment combines structural, conceptual, and methodological critique anchored in peer review best practices and aligned with academic standards for review and conceptual analysis papers. Abstract Strengths The title is well-formulated, concise, and clearly communicates the dual focus on AI ethics and education . The abstract effectively summarises the main argument, highlighting both opportunities and ethical risks of AI in education (privacy, bias, transparency, equity). The emphasis on balancing innovation and responsibility is a timely and relevant topic in global educational discourse. Weaknesses The abstract is descriptive rather than analytical; it summarises known issues but does not indicate how the paper contributes new insights or frameworks. The absence of methodological framing (e.g., narrative review, conceptual synthesis) undermines transparency. Phrases such as “AI offers adaptive tutoring systems and personalised learning” are generic, making the abstract sound like a policy brief rather than a scholarly article. The authors should specify the type of analysis (theoretical, integrative, or comparative) conducted and summarise the key findings or arguments, rather than listing general goals. Failure to address any research gap. Introduction Strengths The introduction outlines the global relevance of AI in transforming education through personalised systems and adaptive learning tools. The section includes relevant early literature (Siemens, 2013; Luckin et al., 2016) and establishes the ethical concerns around bias, privacy, and transparency. Weaknesses The section reads more like a literature overview than a problem-driven introduction. The problem statement is implicit and lacks a focused research question. The sources cited are outdated, essentially predating the era of large language models (LLMs) and generative AI, which have radically altered ethical discussions in 2023–2025. The introduction would benefit from integrating recent debates on generative AI tools (e.g., ChatGPT, Claude, Gemini) in educational contexts. There is no explicit rationale or objective statement—the section concludes descriptively rather than asserting the paper’s unique purpose or contribution. The Promise of AI in Education Strengths The section systematically categorises AI applications, including personalised learning platforms, adaptive assessment tools, intelligent tutoring systems, administrative automation, and learning analytics (Table 1). It includes illustrative examples (Knewton, ALEKS, DreamBox, Cognitive Tutor), demonstrating familiarity with established educational AI tools. Weaknesses The discussion is excessively repetitive and lacks critical depth; most claims are descriptive summaries of existing literature. Assertions about AI’s benefits (“AI holds the promise to revolutionise education”) are overstated and unqualified by empirical evidence or contrasting perspectives. Citations like Siemens (2013) and Luckin et al. (2016) are historically foundational but insufficient to reflect the post-2023 AI landscape. The section should discuss the ethical implications arising from specific applications, such as adaptive learning data biases or privacy issues in tutoring systems. The absence of data or synthesised meta-analytic findings reduces scholarly credibility. Ethical Challenges in AI Integration Strengths The section identifies core ethical issues, including data privacy and security, algorithmic bias, and transparency/accountability, which are consistent with standard frameworks (IEEE, EU AI Ethics Guidelines). The structure is logical, and the examples (e.g., unauthorised data access, black-box algorithms) illustrate conceptual understanding. The authors correctly emphasise the need for institutional policies and bias audits. Weaknesses This section repeats ideas verbatim from earlier paragraphs without introducing new insight. Most discussions are descriptive restatements of existing ethical guidelines (Floridi et al., 2018; Williamson, 2017) rather than critical syntheses or comparative evaluations. The section lacks context-specific examples (e.g., AI in secondary vs. higher education, cross-national data frameworks). No mention of AI literacy, digital autonomy, or emotional AI, which are contemporary ethical themes. Overall, this section contributes little beyond restating well-established principles. Current Implementations and Ethical Frameworks Strengths The section documents recognised frameworks such as IEEE 2016, EU Commission 2018, and Floridi’s AI4People framework, lending authority to the ethical grounding. References to teacher professional development and data governance are appropriately highlighted. Weaknesses The paper lacks methodological structure for reviewing frameworks—no criteria or analytical dimensions are used (e.g., comparative evaluation, adoption challenges). The section merely enumerates guidelines without providing critical analysis or integrating them into educational practice. It overlooks recent frameworks (2022–2025), such as UNESCO’s Recommendation on the Ethics of AI (2021) and the OECD AI Principles (2022) . There is no evaluation matrix comparing ethical frameworks by focus, scope, or applicability to education; such a table would add scholarly rigour. Balancing Innovation and Ethical Responsibility Strengths The authors attempt to move from diagnosis to prescription, suggesting actionable strategies: participatory design, ethical impact assessments, continuous professional development, transparent communication, and regulatory frameworks. The proposed creation of ethical review boards and student digital literacy programs aligns with best practices in AI governance. The section is well-organised and flows logically, demonstrating applied awareness of AI ethics. Weaknesses These recommendations are generic and not tailored to the realities of educational systems or stakeholders. The section lacks implementation details (e.g., how to operationalise participatory design in K–12 schools or universities). The authors do not provide any new model, taxonomy, or framework—as reviewers noted, the paper reads as a collection of widely accepted principles rather than a scholarly contribution. Figure 1 (AI Ethical Review Process) is conceptually relevant but underdeveloped, lacking sufficient explanatory commentary and theoretical linkage. Conclusion Strengths The conclusion reiterates the central argument that AI in education offers benefits but must be guided by transparency, fairness, and accountability. The emphasis on collaboration among educators, policymakers, developers, and researchers reflects a holistic understanding of AI ethics governance. Weaknesses The conclusion is overly repetitive, restating prior sections rather than synthesising them. It lacks critical reflection or originality—no new conceptual model, evaluative insight, or forward-looking argument is provided. The authors claim to offer “practical recommendations,” but none are clearly operationalised or prioritised. The final paragraph merges ethical rhetoric with aspirational statements, lacking analytical closure or future research direction. References Consequently, the study was skewed towards other continents, leaving the African continent behind. Due to this, the study may be biased rather than providing a balanced perspective. The study could also benefit from these papers. [Reference 1] [Reference 2] Summary of Peer Review No methodological grounding or structure for the review process. Outdated and unbalanced literature (pre-ChatGPT era). Overgeneralization and lack of critical depth. No original contribution (no new framework or conceptual model). Potential AI-generated prose is indicated by stylistic uniformity and generic phrasing. These critiques align fully with this assessment. The article remains conceptually relevant, but it is academically superficial. Final Recommendation: Major Revision Required Revisions (for Resubmission) Declare Methodology – Specify whether the review is narrative, scoping, or conceptual. Include inclusion/exclusion criteria and search strategy. Update Literature – Integrate recent works (2023–2025) on generative AI, LLM ethics, and classroom deployment (e.g., UNESCO 2023, Chiu & Lim 2024, Holmes et al. 2024). Add Analytical Framework – Propose or adapt an ethical model specific to AI in education (e.g., “Transparency–Accountability–Inclusivity–Responsibility [TAIR] Framework”). Include Comparative Evaluation – Critically assess at least three ethical frameworks or case studies (IEEE vs. EU AI Act vs. UNESCO 2023). Strengthen Discussion – Add context-specific examples (K–12, higher education, developing contexts) and contrast opportunities with ethical risks. Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes References 1. Opesemowo O: Artificial Intelligence in Education, Bridging Community Gap: A Phenomenological Approach. International Journal of New Education . 2024. Publisher Full Text 2. Opesemowo O: Artificial Intelligence in Mathematics Education. 1-18 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI in Education, Educational Assessment, Psychometric, SDG 4 I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Opesemowo OAG. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r371275 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-371275 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Vickers D. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393518 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393518 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Aug 2025 Darby Vickers , University of San Diego, San Diego, California, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.175814.r393518 The vast majority of the literature cited in this review is from before the release of ChatGPT-3.5 and similar transformer models. This means that much of the literature is out of date, because many of the new applications of AI ... Continue reading READ ALL The vast majority of the literature cited in this review is from before the release of ChatGPT-3.5 and similar transformer models. This means that much of the literature is out of date, because many of the new applications of AI in education use LLMs and other sorts of genAI. It is fine for the authors to cite some older sources, but the vast majority of citations are far too old. Moreover, there are several places where the authors cite a single study to back up a controversial claim (when there are studies that say the opposite) and they fail to acknowledge that there is contrary evidence. For example, they cite Pane et al. 2017 to talk about the promise of personalized learning, when there is an explosion of studies on both sides of this issue that are more recent. Moreover, there doesn't seem to be anything new coming out of this article. The recommendations are bland and generic. There is no deep engagement with the ethical issues. It is not adding anything to the literature either by making an original argument or by doing a comprehensive survey of the current literature. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Ethics, ethics of artificial intelligence, philosophy of education I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Vickers D. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393518 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393518 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Rawas S. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393516 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393516 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Jul 2025 Soha Rawas , Beirut Arab University, Beirut, Lebanon Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.175814.r393516 Full Report Summary This article discusses the ethical implications of integrating AI into education and advocates for the application of ethical frameworks such as IEEE and EU guidelines. It addresses critical themes like data privacy, algorithmic bias, ... Continue reading READ ALL Full Report Summary This article discusses the ethical implications of integrating AI into education and advocates for the application of ethical frameworks such as IEEE and EU guidelines. It addresses critical themes like data privacy, algorithmic bias, transparency, and inclusive practices, and provides conceptual recommendations such as participatory design and ethical review boards. Major Issues Requiring Attention 1. Lack of Methodological Grounding – Not Scientifically Sound The manuscript presents itself as a review, yet lacks any declared methodology, selection process, inclusion/exclusion criteria, or structured analysis . It does not follow the standards of a systematic, scoping, or even well-structured narrative review . This omission severely limits its reproducibility and credibility as a scholarly review article. Must be addressed : Clearly define the review method used. Introduce a structure for how sources were selected and analyzed. Without this, the work cannot be considered academically valid for publication. 2. Outdated and Limited Literature Review While several references are relevant, the review omits recent empirical studies and policy frameworks (2022–2024) related to generative AI, LLMs (like ChatGPT), and emerging ethical challenges in actual classroom contexts. The paper also lacks engagement with counterpoints or critical debate, which weakens its contribution. Must be addressed: Integrate up-to-date empirical studies and expand on limitations or real-world complexities of ethical AI deployment. 3. Overgeneralized and Unsupported Claims Many statements are vague, overoptimistic, or lack empirical or theoretical backing (e.g., “AI promotes equity” or “AI builds trust”). Assertions are made without demonstrating critical analysis or referencing rigorous sources. Must be addressed : Reframe claims based on concrete evidence. Avoid broad, unqualified conclusions. Include balanced discussions acknowledging risks and failures of AI in education. 4. Lack of Novel Contribution The paper mostly summarizes well-known ethical concerns and frameworks without offering a new model, taxonomy, or framework. There is no comparative analysis, visual synthesis (except one underdeveloped figure), or original insight. Must be addressed : To be publishable, the authors need to either propose a new ethical model, analyze real-world implementations, or derive a structured framework for educators and policymakers. Final Evaluation This article raises timely ethical concerns but does not meet the academic standards of a peer-reviewed journal in its current form. Without a methodological backbone, critical synthesis, and original insight, it cannot be considered scientifically sound. I recommend rejection unless the article is fundamentally restructured and rewritten. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: rtificial Intelligence in Education, Educational Technology, Human–Computer Interaction, Ethical Implications of AI, Generative AI in Higher Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Rawas S. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393516 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393516 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Karpouzis K. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r374207 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-374207 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Apr 2025 Kostas Karpouzis , Panteion University of Social and Political Sciences, Athens, Greece Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.175814.r374207 While the manuscript deals with tackles an important topic, i.e., the ethical challenges of integrating AI into educational contexts, it suffers significantly from superficial engagement and excessive generalizations. The authors present an overview of well-known ethical issues, such as privacy, ... Continue reading READ ALL While the manuscript deals with tackles an important topic, i.e., the ethical challenges of integrating AI into educational contexts, it suffers significantly from superficial engagement and excessive generalizations. The authors present an overview of well-known ethical issues, such as privacy, transparency, and equity, yet fail to offer novel insights or a nuanced exploration beyond existing frameworks. The extensive reliance on standard references and general statements weakens the manuscript's originality, reducing it to a summary rather than a meaningful contribution to the discourse. Additionally, there is a noticeable absence of empirical evidence or detailed case studies, resulting in a paper that feels theoretical, abstract, and detached from practical realities. Furthermore, the manuscript exhibits stylistic features which are strongly indicative of generative AI usage. Its repetitive phrasing, generic language patterns, and lack of in-depth analytical reflection suggest significant dependence on AI-assisted drafting. While generative AI can be an acceptable tool for initial drafts or ideation, this manuscript would greatly benefit from thorough human revision to enhance specificity, depth, and originality. To be suitable for publication, the authors need to critically reassess the manuscript’s theoretical framework, substantially strengthen the empirical grounding, clearly articulate practical implications, and transparently acknowledge and justify any generative AI assistance utilized during the writing process. In addition, introducing some newer references would increase the relevance of this work to contemporary ethical discussions; as it stands, only two of the references included are from the last five years. Is the topic of the review discussed comprehensively in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Partly Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: artificial intelligence; ai ethics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Karpouzis K. Reviewer Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r374207 ) The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-374207 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 14 Mar 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 1 14 Mar 25 read read read read Kostas Karpouzis , Panteion University of Social and Political Sciences, Athens, Greece Soha Rawas , Beirut Arab University, Beirut, Lebanon Darby Vickers , University of San Diego, San Diego, USA Oluwaseyi Aina Gbolade Opesemowo , University of Johannesburg, Johannesburg, South Africa Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Opesemowo O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 07 Nov 2025 | for Version 1 Oluwaseyi Aina Gbolade Opesemowo , University of Johannesburg, Johannesburg, South Africa 0 Views copyright © 2025 Opesemowo O. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I am pleased to have reviewed this manuscript titled “Navigating the Ethical Landscape of AI Integration in Education: Balancing Innovation and Responsibility” This assessment combines structural, conceptual, and methodological critique anchored in peer review best practices and aligned with academic standards for review and conceptual analysis papers. Abstract Strengths The title is well-formulated, concise, and clearly communicates the dual focus on AI ethics and education . The abstract effectively summarises the main argument, highlighting both opportunities and ethical risks of AI in education (privacy, bias, transparency, equity). The emphasis on balancing innovation and responsibility is a timely and relevant topic in global educational discourse. Weaknesses The abstract is descriptive rather than analytical; it summarises known issues but does not indicate how the paper contributes new insights or frameworks. The absence of methodological framing (e.g., narrative review, conceptual synthesis) undermines transparency. Phrases such as “AI offers adaptive tutoring systems and personalised learning” are generic, making the abstract sound like a policy brief rather than a scholarly article. The authors should specify the type of analysis (theoretical, integrative, or comparative) conducted and summarise the key findings or arguments, rather than listing general goals. Failure to address any research gap. Introduction Strengths The introduction outlines the global relevance of AI in transforming education through personalised systems and adaptive learning tools. The section includes relevant early literature (Siemens, 2013; Luckin et al., 2016) and establishes the ethical concerns around bias, privacy, and transparency. Weaknesses The section reads more like a literature overview than a problem-driven introduction. The problem statement is implicit and lacks a focused research question. The sources cited are outdated, essentially predating the era of large language models (LLMs) and generative AI, which have radically altered ethical discussions in 2023–2025. The introduction would benefit from integrating recent debates on generative AI tools (e.g., ChatGPT, Claude, Gemini) in educational contexts. There is no explicit rationale or objective statement—the section concludes descriptively rather than asserting the paper’s unique purpose or contribution. The Promise of AI in Education Strengths The section systematically categorises AI applications, including personalised learning platforms, adaptive assessment tools, intelligent tutoring systems, administrative automation, and learning analytics (Table 1). It includes illustrative examples (Knewton, ALEKS, DreamBox, Cognitive Tutor), demonstrating familiarity with established educational AI tools. Weaknesses The discussion is excessively repetitive and lacks critical depth; most claims are descriptive summaries of existing literature. Assertions about AI’s benefits (“AI holds the promise to revolutionise education”) are overstated and unqualified by empirical evidence or contrasting perspectives. Citations like Siemens (2013) and Luckin et al. (2016) are historically foundational but insufficient to reflect the post-2023 AI landscape. The section should discuss the ethical implications arising from specific applications, such as adaptive learning data biases or privacy issues in tutoring systems. The absence of data or synthesised meta-analytic findings reduces scholarly credibility. Ethical Challenges in AI Integration Strengths The section identifies core ethical issues, including data privacy and security, algorithmic bias, and transparency/accountability, which are consistent with standard frameworks (IEEE, EU AI Ethics Guidelines). The structure is logical, and the examples (e.g., unauthorised data access, black-box algorithms) illustrate conceptual understanding. The authors correctly emphasise the need for institutional policies and bias audits. Weaknesses This section repeats ideas verbatim from earlier paragraphs without introducing new insight. Most discussions are descriptive restatements of existing ethical guidelines (Floridi et al., 2018; Williamson, 2017) rather than critical syntheses or comparative evaluations. The section lacks context-specific examples (e.g., AI in secondary vs. higher education, cross-national data frameworks). No mention of AI literacy, digital autonomy, or emotional AI, which are contemporary ethical themes. Overall, this section contributes little beyond restating well-established principles. Current Implementations and Ethical Frameworks Strengths The section documents recognised frameworks such as IEEE 2016, EU Commission 2018, and Floridi’s AI4People framework, lending authority to the ethical grounding. References to teacher professional development and data governance are appropriately highlighted. Weaknesses The paper lacks methodological structure for reviewing frameworks—no criteria or analytical dimensions are used (e.g., comparative evaluation, adoption challenges). The section merely enumerates guidelines without providing critical analysis or integrating them into educational practice. It overlooks recent frameworks (2022–2025), such as UNESCO’s Recommendation on the Ethics of AI (2021) and the OECD AI Principles (2022) . There is no evaluation matrix comparing ethical frameworks by focus, scope, or applicability to education; such a table would add scholarly rigour. Balancing Innovation and Ethical Responsibility Strengths The authors attempt to move from diagnosis to prescription, suggesting actionable strategies: participatory design, ethical impact assessments, continuous professional development, transparent communication, and regulatory frameworks. The proposed creation of ethical review boards and student digital literacy programs aligns with best practices in AI governance. The section is well-organised and flows logically, demonstrating applied awareness of AI ethics. Weaknesses These recommendations are generic and not tailored to the realities of educational systems or stakeholders. The section lacks implementation details (e.g., how to operationalise participatory design in K–12 schools or universities). The authors do not provide any new model, taxonomy, or framework—as reviewers noted, the paper reads as a collection of widely accepted principles rather than a scholarly contribution. Figure 1 (AI Ethical Review Process) is conceptually relevant but underdeveloped, lacking sufficient explanatory commentary and theoretical linkage. Conclusion Strengths The conclusion reiterates the central argument that AI in education offers benefits but must be guided by transparency, fairness, and accountability. The emphasis on collaboration among educators, policymakers, developers, and researchers reflects a holistic understanding of AI ethics governance. Weaknesses The conclusion is overly repetitive, restating prior sections rather than synthesising them. It lacks critical reflection or originality—no new conceptual model, evaluative insight, or forward-looking argument is provided. The authors claim to offer “practical recommendations,” but none are clearly operationalised or prioritised. The final paragraph merges ethical rhetoric with aspirational statements, lacking analytical closure or future research direction. References Consequently, the study was skewed towards other continents, leaving the African continent behind. Due to this, the study may be biased rather than providing a balanced perspective. The study could also benefit from these papers. [Reference 1] [Reference 2] Summary of Peer Review No methodological grounding or structure for the review process. Outdated and unbalanced literature (pre-ChatGPT era). Overgeneralization and lack of critical depth. No original contribution (no new framework or conceptual model). Potential AI-generated prose is indicated by stylistic uniformity and generic phrasing. These critiques align fully with this assessment. The article remains conceptually relevant, but it is academically superficial. Final Recommendation: Major Revision Required Revisions (for Resubmission) Declare Methodology – Specify whether the review is narrative, scoping, or conceptual. Include inclusion/exclusion criteria and search strategy. Update Literature – Integrate recent works (2023–2025) on generative AI, LLM ethics, and classroom deployment (e.g., UNESCO 2023, Chiu & Lim 2024, Holmes et al. 2024). Add Analytical Framework – Propose or adapt an ethical model specific to AI in education (e.g., “Transparency–Accountability–Inclusivity–Responsibility [TAIR] Framework”). Include Comparative Evaluation – Critically assess at least three ethical frameworks or case studies (IEEE vs. EU AI Act vs. UNESCO 2023). Strengthen Discussion – Add context-specific examples (K–12, higher education, developing contexts) and contrast opportunities with ethical risks. Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes References 1. Opesemowo O: Artificial Intelligence in Education, Bridging Community Gap: A Phenomenological Approach. International Journal of New Education . 2024. Publisher Full Text 2. Opesemowo O: Artificial Intelligence in Mathematics Education. 1-18 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise AI in Education, Educational Assessment, Psychometric, SDG 4 I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Opesemowo OAG. Peer Review Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r371275) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-371275 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Vickers D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Aug 2025 | for Version 1 Darby Vickers , University of San Diego, San Diego, California, USA 0 Views copyright © 2025 Vickers D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The vast majority of the literature cited in this review is from before the release of ChatGPT-3.5 and similar transformer models. This means that much of the literature is out of date, because many of the new applications of AI in education use LLMs and other sorts of genAI. It is fine for the authors to cite some older sources, but the vast majority of citations are far too old. Moreover, there are several places where the authors cite a single study to back up a controversial claim (when there are studies that say the opposite) and they fail to acknowledge that there is contrary evidence. For example, they cite Pane et al. 2017 to talk about the promise of personalized learning, when there is an explosion of studies on both sides of this issue that are more recent. Moreover, there doesn't seem to be anything new coming out of this article. The recommendations are bland and generic. There is no deep engagement with the ethical issues. It is not adding anything to the literature either by making an original argument or by doing a comprehensive survey of the current literature. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Ethics, ethics of artificial intelligence, philosophy of education I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Vickers D. Peer Review Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393518) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393518 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Rawas S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Jul 2025 | for Version 1 Soha Rawas , Beirut Arab University, Beirut, Lebanon 0 Views copyright © 2025 Rawas S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Full Report Summary This article discusses the ethical implications of integrating AI into education and advocates for the application of ethical frameworks such as IEEE and EU guidelines. It addresses critical themes like data privacy, algorithmic bias, transparency, and inclusive practices, and provides conceptual recommendations such as participatory design and ethical review boards. Major Issues Requiring Attention 1. Lack of Methodological Grounding – Not Scientifically Sound The manuscript presents itself as a review, yet lacks any declared methodology, selection process, inclusion/exclusion criteria, or structured analysis . It does not follow the standards of a systematic, scoping, or even well-structured narrative review . This omission severely limits its reproducibility and credibility as a scholarly review article. Must be addressed : Clearly define the review method used. Introduce a structure for how sources were selected and analyzed. Without this, the work cannot be considered academically valid for publication. 2. Outdated and Limited Literature Review While several references are relevant, the review omits recent empirical studies and policy frameworks (2022–2024) related to generative AI, LLMs (like ChatGPT), and emerging ethical challenges in actual classroom contexts. The paper also lacks engagement with counterpoints or critical debate, which weakens its contribution. Must be addressed: Integrate up-to-date empirical studies and expand on limitations or real-world complexities of ethical AI deployment. 3. Overgeneralized and Unsupported Claims Many statements are vague, overoptimistic, or lack empirical or theoretical backing (e.g., “AI promotes equity” or “AI builds trust”). Assertions are made without demonstrating critical analysis or referencing rigorous sources. Must be addressed : Reframe claims based on concrete evidence. Avoid broad, unqualified conclusions. Include balanced discussions acknowledging risks and failures of AI in education. 4. Lack of Novel Contribution The paper mostly summarizes well-known ethical concerns and frameworks without offering a new model, taxonomy, or framework. There is no comparative analysis, visual synthesis (except one underdeveloped figure), or original insight. Must be addressed : To be publishable, the authors need to either propose a new ethical model, analyze real-world implementations, or derive a structured framework for educators and policymakers. Final Evaluation This article raises timely ethical concerns but does not meet the academic standards of a peer-reviewed journal in its current form. Without a methodological backbone, critical synthesis, and original insight, it cannot be considered scientifically sound. I recommend rejection unless the article is fundamentally restructured and rewritten. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? Partly Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise rtificial Intelligence in Education, Educational Technology, Human–Computer Interaction, Ethical Implications of AI, Generative AI in Higher Education I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Rawas S. Peer Review Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r393516) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-299/v1#referee-response-393516 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Karpouzis K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Apr 2025 | for Version 1 Kostas Karpouzis , Panteion University of Social and Political Sciences, Athens, Greece 0 Views copyright © 2025 Karpouzis K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions While the manuscript deals with tackles an important topic, i.e., the ethical challenges of integrating AI into educational contexts, it suffers significantly from superficial engagement and excessive generalizations. The authors present an overview of well-known ethical issues, such as privacy, transparency, and equity, yet fail to offer novel insights or a nuanced exploration beyond existing frameworks. The extensive reliance on standard references and general statements weakens the manuscript's originality, reducing it to a summary rather than a meaningful contribution to the discourse. Additionally, there is a noticeable absence of empirical evidence or detailed case studies, resulting in a paper that feels theoretical, abstract, and detached from practical realities. Furthermore, the manuscript exhibits stylistic features which are strongly indicative of generative AI usage. Its repetitive phrasing, generic language patterns, and lack of in-depth analytical reflection suggest significant dependence on AI-assisted drafting. While generative AI can be an acceptable tool for initial drafts or ideation, this manuscript would greatly benefit from thorough human revision to enhance specificity, depth, and originality. To be suitable for publication, the authors need to critically reassess the manuscript’s theoretical framework, substantially strengthen the empirical grounding, clearly articulate practical implications, and transparently acknowledge and justify any generative AI assistance utilized during the writing process. In addition, introducing some newer references would increase the relevance of this work to contemporary ethical discussions; as it stands, only two of the references included are from the last five years. Is the topic of the review discussed comprehensively in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Partly Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise artificial intelligence; ai ethics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Karpouzis K. Peer Review Report For: Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility [version 1; peer review: 1 approved with reservations, 3 not approved] . F1000Research 2025, 14 :299 ( https://doi.org/10.5256/f1000research.175814.r374207) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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