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The Journey of Challenges and Victories: Exploring the Transformation Action Framework in the GenAI Era from Multifaceted Policies

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Title: The Journey of Challenges and Victories: Exploring the Transformation Action Framework in the GenAI Era from Multifaceted Policies
Language: English
Authors: Chengliang Wang (ORCID 0000-0003-2208-3508), Yufan Chen (ORCID 0009-0002-5568-6399), Zhebing Hu (ORCID 0009-0005-7911-3036), Yuanyuan Li (ORCID 0009-0000-8956-3907), Xiaoqing Gu (ORCID 0000-0001-8256-5408)
Source: Educational Technology Research and Development. 2025 73(5):2951-2993.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 43
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Technology Uses in Education, Influence of Technology, Educational Policy, Policy Analysis, Guidelines, Educational Trends, Teacher Role, Technology Integration, Educational Methods, Efficiency, Student Development
DOI: 10.1007/s11423-025-10535-5
ISSN: 1042-1629
1556-6501
Abstract: Generative Artificial Intelligence (GenAI) stands as a cornerstone of the technological revolution, significantly impacting the global educational landscape. This prompts worldwide governments and educational institutions to craft strategic frameworks. This study aims to analyze GenAI's influence on the education system, particularly focusing on transformations in educational paradigms, modalities, pedagogical logics, and educational contexts. It seeks to establish a transformation action framework for the education system in the GenAI era. Utilizing Meta-ethnography, the research synthesizes, analyzes and interprets 11 policy and guideline documents from UNESCO, OECD, ministries of education and universities, which reveal trends towards personalized and interactive educational forms, shifts in the role of the teacher, and updates in student learning modes. The study explores GenAI's integration into education at macro, meso, and micro levels. At the macro level, the framework identifies how GenAI drives a productivity revolution and reshapes human resource demands, alongside societal attitudes and educational actions adapting to this transformation. At the meso level, it reflects on educational pattern and logic shifts, delving into the evolution of educational modalities, entities, media and content. At the micro level, it deconstructs new teaching and learning scenarios in the GenAI era, closely examining the evolution of the role of the teacher and student learning modes, scrutinizing the core value of education as a fundamental human right and constructing a vision for future education in the GenAI era. The findings underscore the need for comprehensive transformation in the education system to adapt to GenAI-driven changes, updating educational content and methods to enhance teaching efficiency and quality as well as fostering holistic student development. These insights offer theoretical and practical guidance for the educational sector to respond to GenAI-driven technological changes, aiming to equip the education system to overcome challenges, seize opportunities and prepare talents needed for the future society.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497434
Database: ERIC
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  Value: <anid>AN0189592952;etr01oct.25;2025Nov28.05:47;v2.2.500</anid> <title id="AN0189592952-1">The journey of challenges and victories: exploring the transformation action framework in the GenAI era from multifaceted policies </title> <p>Generative Artificial Intelligence (GenAI) stands as a cornerstone of the technological revolution, significantly impacting the global educational landscape. This prompts worldwide governments and educational institutions to craft strategic frameworks. This study aims to analyze GenAI's influence on the education system, particularly focusing on transformations in educational paradigms, modalities, pedagogical logics, and educational contexts. It seeks to establish a transformation action framework for the education system in the GenAI era. Utilizing Meta-ethnography, the research synthesizes, analyzes and interprets 11 policy and guideline documents from UNESCO, OECD, ministries of education and universities, which reveal trends towards personalized and interactive educational forms, shifts in the role of the teacher, and updates in student learning modes. The study explores GenAI's integration into education at macro, meso, and micro levels. At the macro level, the framework identifies how GenAI drives a productivity revolution and reshapes human resource demands, alongside societal attitudes and educational actions adapting to this transformation. At the meso level, it reflects on educational pattern and logic shifts, delving into the evolution of educational modalities, entities, media and content. At the micro level, it deconstructs new teaching and learning scenarios in the GenAI era, closely examining the evolution of the role of the teacher and student learning modes, scrutinizing the core value of education as a fundamental human right and constructing a vision for future education in the GenAI era. The findings underscore the need for comprehensive transformation in the education system to adapt to GenAI-driven changes, updating educational content and methods to enhance teaching efficiency and quality as well as fostering holistic student development. These insights offer theoretical and practical guidance for the educational sector to respond to GenAI-driven technological changes, aiming to equip the education system to overcome challenges, seize opportunities and prepare talents needed for the future society.</p> <p>Keywords: Generative artificial intelligence; Education system; Transformation action framework; Meta-ethnography; Policy analysis; Education Curriculum and Pedagogy Specialist Studies In Education</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0189592952-2">Introduction</hd> <p>As the core of the new generation of technological revolutions, Generative Artificial Intelligence (GenAI) represents a new stage in the application and development of artificial intelligence technology. Although the research on Transformer models and related technologies has been ongoing for decades, accompanied by continuous improvements in computing power, the emergence of GenAI marks its widespread influence and potential, particularly in education, the creative industries, and knowledge work. The"novelty"of this technology is reflected in its expressiveness and general applicability in specific tasks, especially in transformative changes brought about in areas such as content generation, personalized learning, and automated creation (Cai et al., [<reflink idref="bib12" id="ref1">12</reflink>]; Chen et al., [<reflink idref="bib18" id="ref2">18</reflink>]).</p> <p>In particular, GenAI technology brings enormous potential and opportunities for educational reform. As a catalyst for human capital development, GenAI can not only improve educational efficiency and personalized learning experiences but also promote the innovation and updating of teaching content. Therefore, there is an urgent need to actively embrace this technological change, adapt to, and reshape educational models and forms to align with the advancements of GenAI (Wang et al., [<reflink idref="bib84" id="ref3">84</reflink>]). However, this process also requires careful attention to the challenges that the technology may bring. For example, excessive reliance on artificial intelligence may lead to a distancing of teacher-student relationships or create risks related to educational equity and privacy protection. Therefore, while promoting the application of GenAI in education, we must ensure that ethical and social responsibilities are integrated into its adoption process, balancing technological empowerment with the preservation of the essence of education (Álvarez-Álvarez & Falcon, [<reflink idref="bib3" id="ref4">3</reflink>]; Wang et al., [<reflink idref="bib85" id="ref5">85</reflink>]).</p> <p>At present, major countries around the world, educational institutions (such as UNESCO), and higher education organizations (such as universities and research institutes) are formulating strategies and plans for the development of artificial intelligence technologies in an effort to seize the high ground of GenAI-powered education. This series of actions not only highlights the importance of GenAI as a new type of productive force but also signals the transformation and upgrading that is about to take place in the field of education. Specifically, many countries and educational institutions have already started applying GenAI technology in practical educational settings to drive changes in teaching methods. For example, in 2023, UNESCO's release of two guidelines,"GenAI and the Future of Education"and"Guidance for GenAI in Education and Research,"clearly pointed out the potential risks posed by GenAI, such as the exacerbation of the digital divide, infringement of intellectual property, privacy data leakage, and content bias. They also recommended that relevant laws, policies, and human development plans be developed when using GenAI in education (UNESCO, [<reflink idref="bib80" id="ref6">80</reflink>], [<reflink idref="bib79" id="ref7">79</reflink>]).</p> <p>In addition, organizations such as EDUCAUSE in the United States and higher education institutions like Cornell University have provided related recommendations on educational transformation in the GenAI era from different perspectives. Specifically, EDUCAUSE proposed strategies for effectively introducing GenAI into higher education, including data-driven personalized learning and intelligent assessments to support the diverse needs of students (EDUCAUSE, [<reflink idref="bib26" id="ref8">26</reflink>]). Cornell University, through its AI Education Center, has promoted the exploration and practice of AI-based teaching models, particularly achieving remarkable results in language learning and writing tutoring (CU Committee, [<reflink idref="bib22" id="ref9">22</reflink>]). These concrete practical cases demonstrate that GenAI not only contributes to the personalization and efficiency of education but also provides strong technical support for the transformation of educational models.</p> <p>These policy reports and practical cases focus on the application and ethical issues of artificial intelligence in education from multiple angles, introduce the risks and future outlook, and clarify the regulations for GenAI use at the legal level. They attempt to provide policy guidelines for various stakeholders in the educational system (including teachers, students, school administrators, and educational researchers), educational stages (K12 and higher education), and tool designers, guiding the responsible and effective application of GenAI technology in education.</p> <p>Despite the numerous leading research teams that have released policy reports and guidelines on artificial intelligence, covering topics such as how students can effectively use GenAI tools in learning and research, how teachers can apply GenAI tools in classrooms, and whether students should be allowed to use GenAI tools in their coursework, and offering various recommendations and regulations for GenAI usage, there still lacks systematic analysis and integration of these policy documents to construct a comprehensive GenAI-era educational system transformation action framework. It is important to clarify that the"transformation action framework"refers to a systematic guiding framework that spans all levels of the educational system and interdisciplinary fields, aiming to integrate policy, practice, and theory to clearly define how to achieve comprehensive transformation and adaptation in the education sector in the context of GenAI empowerment (Wang et al., [<reflink idref="bib84" id="ref10">84</reflink>]). This framework includes not only the use guidelines for GenAI tools and directions for transforming educational models but also innovations and changes at multiple levels, including education management, teacher roles, and student learning methods. Especially with GenAI empowerment, the underlying logic of education has undergone profound changes (Chen et al., [<reflink idref="bib18" id="ref11">18</reflink>]), and constructing an education system that meets future demands has become an urgent task.</p> <p>Therefore, researching the action framework for the transformation of the educational ecosystem in the GenAI era has become particularly important and necessary. This study explores the transformation directions and ideas for educational models and practices in the GenAI era from multiple perspectives, aiming to construct a comprehensive and detailed action framework for educational system transformation to better respond to technology-driven educational changes. Through this framework, the study provides theoretical guidance and practical references for the adaptation and development of the education sector in the new wave of technological advancements, helping the educational field overcome the challenges brought by new technologies and driving the educational system toward a more inclusive, flexible, and innovative direction.</p> <hd id="AN0189592952-3">Literature review and research necessity</hd> <p>Since the release of GenAI epresented by ChatGPT, the field of education has shown great interest in this disruptive technology. A review of the relevant literature from the past year reveals two main categories.</p> <p>The first category consists of literature that analyzes the value, opportunities, and risks of GenAI for education from various perspectives (Kasneci et al., [<reflink idref="bib42" id="ref12">42</reflink>]; Kikalishvili, [<reflink idref="bib43" id="ref13">43</reflink>]; Lee, [<reflink idref="bib44" id="ref14">44</reflink>]; Mohammad et al., [<reflink idref="bib51" id="ref15">51</reflink>]), deconstructs the basic functions and underlying mechanisms of GenAI-related products in education (Rahimzadeh et al., [<reflink idref="bib59" id="ref16">59</reflink>]), or explores best practices for applying GenAI in education (Adeshola & Adepoju, [<reflink idref="bib1" id="ref17">1</reflink>]; Lodge et al., [<reflink idref="bib47" id="ref18">47</reflink>]). In general, these studies mainly offer theoretical discussions on the core advantages and potential drawbacks of applying new technologies in education.</p> <p>The second category consists of empirical research. For example, qualitative studies explore how GenAI (such as ChatGPT) can assist with language learning tasks, particularly in second-language writing (Yan, [<reflink idref="bib95" id="ref19">95</reflink>]). Additionally, some research investigates how GenAI technology can enhance the effectiveness of science teaching (Cooper, [<reflink idref="bib21" id="ref20">21</reflink>]). Several studies use quantitative methods, such as structural equation modeling and fuzzy-set qualitative comparative analysis, to explore learners' willingness to adopt and use GenAI technology (Foroughi et al., [<reflink idref="bib28" id="ref21">28</reflink>]; Ma et al., [<reflink idref="bib48" id="ref22">48</reflink>]; Saif et al., [<reflink idref="bib62" id="ref23">62</reflink>]; Strzelecki, [<reflink idref="bib72" id="ref24">72</reflink>]; Tang et al., [<reflink idref="bib74" id="ref25">74</reflink>]; Wang et al., [<reflink idref="bib87" id="ref26">87</reflink>]). Furthermore, quasi-experimental designs are increasingly being used to study the application of GenAI in programming practices, with research by Jing et al. ([<reflink idref="bib40" id="ref27">40</reflink>]), Ouh et al. ([<reflink idref="bib55" id="ref28">55</reflink>]), and Popovici ([<reflink idref="bib58" id="ref29">58</reflink>]) validating the unique value of GenAI technology in educational support. In the fields of medical and biological education, GenAI technology has also been shown to significantly enhance learning performance and learners' engagement (Das et al., [<reflink idref="bib23" id="ref30">23</reflink>]; Karabacak et al., [<reflink idref="bib41" id="ref31">41</reflink>]). These studies provide valuable insights into the practical application of GenAI technology in specific educational contexts and demonstrate its tremendous potential in improving learning outcomes and education quality.</p> <p>From the review above, it is clear that research on GenAI in education has become quite abundant, with some scholars even conducting comprehensive reviews of these studies. For example, Chen et al. ([<reflink idref="bib18" id="ref32">18</reflink>]) conducted a systematic literature review of 134 papers, exploring five key research themes in this emerging field and constructing an internal mechanism and logical framework for the educational application of GenAI technology through in-depth analysis of these studies.</p> <p>However, existing research is limited by time constraints. For instance, many of the first category of studies are based on the researchers' individual perceptions of GenAI technology and the envisioned educational applications, which inevitably carry subjective and personal cognitive limitations. The second category of empirical studies often focuses on a single perspective to analyze the role and value of GenAI technology in specific contexts. While these studies offer important practical guidance, their small sample size and empirical limitations make it difficult to gain a comprehensive understanding of the logic and mechanisms behind GenAI's potential to transform education. Although some studies have attempted to construct GenAI-based educational policy frameworks using mixed methods, their data comes from a single university and lacks global perspective and cross-cultural comparative analysis (Chan, [<reflink idref="bib14" id="ref33">14</reflink>]). Moreover, existing research seems to overlook an important element—policy guidelines and research reports issued by major international organizations, ministries of education, and universities. These reports are often written through multiple iterations and extensive validation, providing critical insights into the role of GenAI in transforming education. The lack of analysis of these important texts is a significant gap in this research field.</p> <p>Therefore, this study aims to fill this gap by using Meta-ethnography, a systematic and comprehensive method, to synthesize and organize the action directions provided by authoritative policy documents and guidelines. It aims to construct a transformation action framework for the educational ecosystem in the GenAI era, providing a comprehensive and systematic response to the macro question of how education systems should transform in the face of the intervention of GenAI, a technology that represents a paradigm shift.</p> <hd id="AN0189592952-4">Methodology and data</hd> <p></p> <hd id="AN0189592952-5">Introduction to methodology and explanation of applicability</hd> <p>The main research method used in this study is Meta-ethnography, a qualitative research approach aimed at synthesizing and analyzing the results of multiple related qualitative studies to reveal relationships and interactions between them. Meta-ethnography compares the core concepts and themes from different studies, providing a cross-study perspective to better understand complex social phenomena and educational issues. In this study, Meta-ethnography not only helps analyze educational policies and action frameworks across different cultural and social contexts but also offers theoretical support for addressing the educational transformation in the GenAI era.</p> <p>The policy texts selected for this study encompass multiple perspectives, including those from international organizations (such as UNESCO and OECD), government agencies (such as the U.S. Department of Education), and policy guidelines issued by prestigious universities. The drafting and formulation of these documents involve a diverse group of experts, including policymakers, education researchers, and industry practitioners, each contributing insights based on their backgrounds and standpoints. Additionally, some university-issued guidelines have undergone extensive field research during their development, incorporating feedback from frontline teachers and students, thereby indirectly reflecting the real needs and experiences of education practitioners. By integrating these texts, the meta-ethnographic approach can help uncover the perspectives of various stakeholders on the educational transformation in the GenAI era, forming a more comprehensive analytical framework.</p> <p>The Meta-ethnography method is uniquely applicable, especially in contexts involving diverse, cross-cultural educational policy texts. It requires researchers to conduct in-depth analysis, comparison, and abstract summary of the qualitative data collected, rather than simply aggregating or summarizing the data. In this way, Meta-ethnography can help uncover patterns, themes, and relationships hidden within different educational policies and action frameworks, revealing both universal issues and unique challenges in specific contexts. In the study of educational system transformation, Meta-ethnography can integrate data from different countries, cultures, and policy backgrounds, providing a more comprehensive analytical perspective for developing forward-looking educational policies.</p> <p>This section will first provide an overview of the basic principles of Meta-ethnography and its research methods, and further explain why this approach is particularly suitable for current educational research, especially in constructing the action framework for educational transformation in the GenAI era. In the following part, we will detail how this study integrates various policy and guideline texts through Meta-ethnography and extracts a multi-dimensional educational system transformation framework.</p> <p>To fully leverage the outcomes of qualitative research while avoiding redundancy with existing studies, a comprehensive method is needed that reinterprets and integrates relationships among different qualitative findings, fostering innovation and elevation in the application of qualitative research outcomes. Meta-ethnography has emerged in response to this methodological evolution, serving as a review method catering to such needs (Britten & Pope, [<reflink idref="bib11" id="ref34">11</reflink>]). At its inception, Meta-ethnography emerged from ethnographic studies addressing educational issues. In 1988, while conducting a qualitative synthesis on the reality of"desegregation in urban schools", Noblit and Hare ([<reflink idref="bib53" id="ref35">53</reflink>]) conducted a small-scale interpretive synthesis of 5 ethnographic studies related to this research theme. This qualitative meta-synthesis approach was subsequently defined as Meta-ethnography. They discerned that Meta-ethnography's interpretive synthesis of qualitative research allows educators, administrators and policymakers to view educational phenomena from fresh perspectives, identifying rapid solutions and principles, and offers researchers an analytical framework for further explicating complex phenomena (Pérez-Castejón & Vigo-Arrazola, [<reflink idref="bib57" id="ref36">57</reflink>]).</p> <p>The core objective of this study is to construct a multi-dimensional educational system transformation action framework by comprehensively analyzing existing policies and guideline texts related to educational actions in the GenAI era. In this study, each independent policy or guideline text is treated as a separate qualitative research outcome. This is undoubtedly a bold attempt, but it is also reasonable and appropriate.</p> <p>The use of Meta-ethnography as a research method is highly justified, especially when handling diversified and cross-cultural qualitative research outcomes. First, policy and guideline texts are typically unstructured materials that analyze or interpret social phenomena, which aligns with the characteristics of qualitative research outcomes. Therefore, they meet the requirements of Meta-ethnography for analytical materials (Sattar et al., [<reflink idref="bib63" id="ref37">63</reflink>]). The common goal of these texts is to help the education system cope with the changes brought by GenAI technology. Meta-ethnography can reveal the interrelationships and differences between these texts by comparing the themes, metaphors, and concepts across different studies, thus providing new insights into specific phenomena. Noblit ([<reflink idref="bib52" id="ref38">52</reflink>]) defines Meta-ethnography as a method"for synthesizing related qualitative research, gradually reducing the number of relevant studies, and further clarifying the research phenomena,"emphasizing its comprehensive analysis of phenomena. In this study, although the selected policy and guideline texts vary in form and content, they all focus on addressing the challenges brought by GenAI, making them suitable for effective integration and cross-literature comparison using Meta-ethnography. Unlike traditional meta-analysis methods, Meta-ethnography focuses on the semantic relationships between research subjects rather than the aggregation of quantitative data (Soundy & Heneghan, [<reflink idref="bib71" id="ref39">71</reflink>]). It generates a deeper understanding of phenomena by"homogenizing"the core concepts across different studies, making it especially suitable for analyzing complex phenomena in fields like education and sociology (Noblit & Hare, [<reflink idref="bib53" id="ref40">53</reflink>]). Additionally, these texts in the field of educational policy often reflect implicit cultural differences, and Meta-ethnography can reveal different interpretations and coping strategies for GenAI across cultures, providing a cross-cultural integrated perspective (Britten et al., [<reflink idref="bib10" id="ref41">10</reflink>]). Therefore, Meta-ethnography can not only effectively integrate different perspectives in educational policy but also help identify universal challenges and unique opportunities, thus constructing a more broadly adaptable framework for educational transformation (Bell et al., [<reflink idref="bib8" id="ref42">8</reflink>]; Campbell et al., [<reflink idref="bib13" id="ref43">13</reflink>]). For this reason, Meta-ethnography provides deeper theoretical support and practical guidance for addressing educational issues in diverse contexts.</p> <p>In summary, Meta-ethnography is a very suitable research method to address the research questions of this study. Through the collection, organization, in-depth reading, comparative analysis, concept extraction, and synthesis of these policy and guideline texts (Sattar et al., [<reflink idref="bib63" id="ref44">63</reflink>]), we can construct a comprehensive and detailed educational system transformation action framework to guide the direction and path of educational transformation in the GenAI era.</p> <p>It should be further pointed out that Meta-ethnography emphasizes understanding and interpretation rather than simply summarizing or aggregating data. The goal is to generate deeper insights and understanding of the research field (Soundy & Heneghan, [<reflink idref="bib71" id="ref45">71</reflink>]). Therefore, another reason for applying Meta-ethnography in this study is that it can integrate different cultural and social backgrounds, providing a cross-cultural and cross-contextual comprehensive perspective for the educational field, thereby enhancing the understanding of the diversity and complexity of the educational system transformation action framework (Noblit, [<reflink idref="bib52" id="ref46">52</reflink>]; Walsh et al., [<reflink idref="bib82" id="ref47">82</reflink>]). Additionally, by analyzing the correlations and differences between different policies and guidelines, Meta-ethnography can reveal the common challenges and unique opportunities faced by the education field in the GenAI era, providing theoretical support for developing more forward-looking and adaptive educational policies.</p> <p>Ultimately, the application of Meta-ethnography will promote dialogue between educational researchers, policymakers, and practitioners. This is because the method, through in-depth analysis and comparison of cross-cultural and cross-contextual educational policies, can reveal the commonalities and differences in how different countries and regions are responding to educational transformation in the GenAI era. This cross-literature integration and comparison will not only help researchers recognize the similarities and differences in various cultural and contextual settings but also facilitate interaction and mutual understanding between academia, policy, and educational practice. By mapping out the interrelationships between these policies and action frameworks, Meta-ethnography provides a shared platform for discussion among educational researchers, further fostering interdisciplinary cooperation and strategic dialogue, thereby offering both theoretical and practical support for future educational transformation.</p> <hd id="AN0189592952-6">Data collection and analysis</hd> <p>To ensure the comprehensiveness and representativeness of data collection, the research team conducted a systematic search of policy reports and guidelines related to GenAI and education. The search covered multiple important sources, including international organizations (such as UNESCO, OECD), education ministries and related departments of various countries, and the official websites of the top 300 universities in the QS World University Rankings. In selecting specific countries, the research team focused on those with advanced educational reforms and technology applications, such as the United States, the United Kingdom, Germany, and China, as these countries have significant policy developments and practical experience in the application of GenAI technology in the education field. All literature retrieval was completed before February 26, 2024, and a total of 11 policy and guideline documents closely related to GenAI and education were obtained, as shown in Table 1 (links to each document can be found in Table 4).</p> <p>Table 1 Policy guideline documents included in the analysis</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Rank</p></th><th align="left"><p>Publishing organization</p></th><th align="left"><p>Policy document</p></th><th align="left"><p>Publication date</p></th></tr></thead><tbody><tr><td align="left"><p>D1</p></td><td align="left"><p>UNESCO</p></td><td align="left"><p><italic>Guidance for generative AI in education and research</italic></p></td><td char="." align="char"><p>2023.09</p></td></tr><tr><td align="left"><p>D2</p></td><td align="left"><p>UNESCO</p></td><td align="left"><p><italic>Education in the age of artificial intelligence</italic></p></td><td char="." align="char"><p>2023.10</p></td></tr><tr><td align="left"><p>D3</p></td><td align="left"><p>UNESCO</p></td><td align="left"><p><italic>Generative AI and the future of education</italic></p></td><td char="." align="char"><p>2023.07</p></td></tr><tr><td align="left"><p>D4</p></td><td align="left"><p>OECD</p></td><td align="left"><p><italic>AI and the Future of Skills, Volume 2</italic></p><p><italic>Methods for Evaluating AI Capabilities</italic></p></td><td char="." align="char"><p>2023.11</p></td></tr><tr><td align="left"><p>D5</p></td><td align="left"><p>OECD</p></td><td align="left"><p><italic>Is Education Losing the Race with Technology?</italic></p><p><italic>AI's Progress in Maths and Reading</italic></p></td><td char="." align="char"><p>2023.03</p></td></tr><tr><td align="left"><p>D6</p></td><td align="left"><p>U.S. Department of Education's Office of Educational Technology</p></td><td align="left"><p><italic>Artificial Intelligence and the Future of Teaching and Learning</italic></p></td><td char="." align="char"><p>2023.05</p></td></tr><tr><td align="left"><p>D7</p></td><td align="left"><p>UK Department for Education</p></td><td align="left"><p><italic>Generative AI in education: Educator and expert views</italic></p></td><td char="." align="char"><p>2024.2</p></td></tr><tr><td align="left"><p>D8</p></td><td align="left"><p>EDUCAUSE</p></td><td align="left"><p><italic>2023 EDUCAUSE Horizon Report: Teaching and Learning Edition</italic></p></td><td char="." align="char"><p>2023.05</p></td></tr><tr><td align="left"><p>D9</p></td><td align="left"><p>EDUCAUSE</p></td><td align="left"><p><italic>2023 EDUCAUSE Horizon Action Plan: Generative AI</italic></p></td><td char="." align="char"><p>2023.09</p></td></tr><tr><td align="left"><p>D10</p></td><td align="left"><p>Cornell University</p></td><td align="left"><p><italic>Generative Artificial Intelligence for Education and Pedagogy</italic></p></td><td char="." align="char"><p>2023.07</p></td></tr><tr><td align="left"><p>D11</p></td><td align="left"><p>Cambridge University</p></td><td align="left"><p><italic>English language education in the era of generative AI: our perspective</italic></p></td><td char="." align="char"><p>2023.05</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>UNESCO</emph> United Nations Educational, Scientific and Cultural Organization, <emph>OECD</emph> Organization for Economic Cooperation and Development</p> <p>During the data collection process, the research team first defined the search scope and clarified the keywords, including"Generative AI,""Education,""Policy,""Guidelines,"and"Ethics."The team then strictly screened the literature to ensure that the selected documents were directly related to the application, impact, and ethics of GenAI in the education field and met the timeliness requirement, with publication dates within the past three years. After screening and preliminary reading, 11 core policy documents were selected, covering the strategies and responses of different countries and international organizations to the education system in the GenAI era.</p> <p>Meta-ethnography does not have specific requirements for sample size, but the team reviewed some Meta-ethnography studies in the education field and found that most studies had sample sizes ranging from 10 to 20 texts (e.g., Jamal et al., [<reflink idref="bib35" id="ref48">35</reflink>]; Pérez-Castejón & Vigo-Arrazola, [<reflink idref="bib57" id="ref49">57</reflink>]; Tondeur et al., [<reflink idref="bib76" id="ref50">76</reflink>]). Based on this, the team concluded that the number of texts analyzed in this study falls within an appropriate range.</p> <p>After completing the literature search, the research team conducted a detailed reading of the 11 core policy documents and organized the background, purpose, and core themes of each document (see Table 5). These policy documents mainly focus on the application, impact, and ethical issues of GenAI in education and aim to provide guidance for the transformation of education systems. Based on this, the research team developed an initial framework covering various dimensions of GenAI in education, providing theoretical support for subsequent analysis.</p> <p>The team then conducted a cross-literature synthesis of the themes and viewpoints in different documents, focusing on the similarities and differences in the attitudes and policy recommendations of various countries and institutions towards the application of GenAI. The team analyzed the measures proposed in the literature for addressing the GenAI technology in education systems and their effectiveness, as well as explored the vision and specific action frameworks for future educational transformation. For example, the phrase"Reflect on the long-term implications of GenAI for education and research; Build capacity for proper use of GenAI in education and research; Encourage learners and researchers to critique the responses provided by GenAI"was coded as"Shaping Reflective Abilities in GenAI Application,"reflecting the need to cultivate the reflective abilities of educators and learners in the education field, enabling them to think critically and use GenAI appropriately. Another segment,"Provide special programmes for older workers and citizens who may need to learn new skills and adapt to new environments; Enable inclusive access to learning programmes, especially for vulnerable groups such as learners with disabilities,"was coded as"Focus on Educational Equity,"indicating that the education system should ensure equal access to education for different groups, particularly older workers and students with disabilities, through special programs and inclusive learning pathways. Through these analyses, the research team identified the key actions and strategic directions necessary for driving educational system change through GenAI and ultimately constructed an action framework for educational system transformation in the GenAI era. The framework was described in detail from the macro, meso, and micro levels, covering various aspects such as educational models, teaching logic, teacher roles, and student learning methods.</p> <p>Finally, the analysis results were validated and refined through internal discussions and interviews with domain experts to ensure the theoretical consistency and practicality of the proposed action framework. Through this series of data collection and analysis processes, this study not only deeply explored the information in related policy reports but also systematically constructed a theoretical framework for educational system transformation in the GenAI era using the Meta-ethnography method, providing theoretical support and practical guidance for the education field in responding to the changes brought about by new technologies.</p> <hd id="AN0189592952-7">Results</hd> <p></p> <hd id="AN0189592952-8">Meta-ethnography coding results</hd> <p>Based on a deep understanding of the analysis steps for Meta-ethnography provided by Noblit ([<reflink idref="bib52" id="ref51">52</reflink>]), this study drew on the analytical steps from Meta-ethnography research in the education field (Pérez-Castejón & Vigo-Arrazola, [<reflink idref="bib57" id="ref52">57</reflink>]; Soundy & Heneghan, [<reflink idref="bib71" id="ref53">71</reflink>]) and combined both methods to conduct specific analytical practices. First, the authors systematically read and analyzed the selected policy texts. During the"in-depth reading"process, the researchers carefully examined the main points and findings of each policy, extracting key concepts and themes. To ensure the accuracy of the analysis, the researchers analyzed and annotated the core information in each text related to the research topic, identifying commonalities and differences across the texts. Based on this, we conducted a comparative analysis of these concepts or themes and further summarized and identified the action dimensions related to educational transformation. In this process, we organized multiple themes based on the information and meanings in the texts and developed a clear coding framework (see Table 2), with each code corresponding to a specific educational action theme. These themes were categorized into key areas such as educational system transformation, technology application, and policy guidance to help construct a more comprehensive action framework.</p> <p>Table 2 Meta-ethnography coding table</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Educational system's main entities</p></th><th align="left"><p>Initial category</p></th><th align="left"><p>Original statement (Partly)</p></th></tr></thead><tbody><tr><td align="left" rowspan="5"><p>Teachers and Students</p></td><td align="left"><p>Cultivation of Intelligence Literacy</p></td><td align="left"><p>Developing AI competencies; AI curricula; Instructing students on the necessity of academic integrity</p></td></tr><tr><td align="left"><p>Standardization of GenAI Usage Practices</p></td><td align="left"><p>Faculty should identify expectations regarding the use of GenAI tools in course; Detecting GenAI-based plagiarism in written assignments; Students should be taught why using GenAI in prohibited ways is not just unethical</p></td></tr><tr><td align="left"><p>Maintenance of Educational Ethics and Morals</p></td><td align="left"><p>We currently discourage the use of automatic detection algorithms for academic integrity violations using GenAI; We do not recommend the use of GenAI for student assessment; Any information that educators are obligated to keep private</p></td></tr><tr><td align="left"><p>Personal Professional Development</p></td><td align="left"><p>Enabling teachers to create specific GenAI-based tools to facilitate learning in the classroom and in their own professional development</p></td></tr><tr><td align="left"><p>Empowerment of Curriculum Development</p></td><td align="left"><p>Faculty use GenAI as a tool for developing teaching materials</p></td></tr><tr><td align="left" rowspan="8"><p>Educational Administrators (primarily referring to school-level managers)</p></td><td align="left"><p>GenAI Skills Cultivation</p></td><td align="left"><p>Providing guidance and training to researchers, teachers and learners about GenAI tools; Enhancing future-proof skills at all levels of education and lifelong learning systems based on prospective shifts in demand</p></td></tr><tr><td align="left"><p>Shaping Reflective Abilities in GenAI Application</p></td><td align="left"><p>Reflecting on the long-term implications of GenAI for education and research; Building capacity for proper use of GenAI in education and research; Encouraging learners and researchers to critique the responses provided by GenAI</p></td></tr><tr><td align="left"><p>Comprehensive Talent Cultivation Strategy in the AI Era</p></td><td align="left"><p>Supporting higher education and research institutions to enhance programs to develop local AI talent; Protecting teachers' unique roles in facilitating higher-order thinking, organizing human interaction and fostering human values</p></td></tr><tr><td align="left"><p>Establishment of a Comprehensive GenAI Education Policy Framework</p></td><td align="left"><p>Adopting whole-of-government, intersectoral and multistakeholder approaches to the planning of policies on AI in education; Endorsing international or regional general data protection regulations, or developing national ones; Adopting/Revising and funding whole-of-government strategies on AI</p></td></tr><tr><td align="left"><p>GenAI Risk Management</p></td><td align="left"><p>Protection of data privacy; Definition and enforcement of age limit for the use of GenAI</p></td></tr><tr><td align="left"><p>Enhancement of Regulatory Mechanisms for GenAI Usage</p></td><td align="left"><p>Elaborating regulatory frameworks on GenAI; Balancing between the regulation of GenAI and the promotion of AI innovation; Building validation mechanisms to test whether GenAI systems used in education and research are free of biases</p></td></tr><tr><td align="left"><p>Protection of Diverse Expressions</p></td><td align="left"><p>Supporting personalized and open learning options; Promoting plural opinions and plural expressions of ideas; Ensuring that quality, diversity and equity are maintained at all times</p></td></tr><tr><td align="left"><p>Focus on Educational Equity</p></td><td align="left"><p>Providing special programs for older workers and citizens who may need to learn new skills and adapt to new environments; Enabling inclusive access to learning programs, especially for vulnerable groups such as learners with disabilities</p></td></tr><tr><td align="left" rowspan="5"><p>Tool Developers</p></td><td align="left"><p>Compliance with Core Value in GenAI Product Development</p></td><td align="left"><p>Non-discriminatory content generation; Ensuring the design and adoption of GenAI are strategically planned; Incentivizing the designers of GenAI to target open-ended, exploratory and diverse learning options</p></td></tr><tr><td align="left"><p>Ensuring the Trustworthiness of GenAI Products</p></td><td align="left"><p>Trustworthy data and models; Acknowledging the limitations and preventing predictable risks</p></td></tr><tr><td align="left"><p>Establishment of Robust Remedial Mechanisms for GenAI Products</p></td><td align="left"><p>Mechanisms for complaints and remedies; Monitoring and reporting of unlawful use</p></td></tr><tr><td align="left"><p>Guarantee of the Transparency of GenAI Outputs</p></td><td align="left"><p>GenAI output should be clearly labeled as having been produced by a machine; Providers of GenAI should ensure secure, robust and sustainable service throughout the life cycle of a GenAI system</p></td></tr><tr><td align="left"><p>Development of Vertical Domain Design for GenAI</p></td><td align="left"><p>Steering the design of new domain-specific AI applications;</p></td></tr><tr><td align="left" rowspan="5"><p>Education Administrative Department; Educational Researchers</p></td><td align="left"><p>Inspiration of GenAI's Creative Use in Educational Research</p></td><td align="left"><p>Encouraging researchers to critique the responses provided by GenAI; Guiding the use of GenAI to trigger innovation in research; Institutional strategies to facilitate responsible and creative use of GenAI</p></td></tr><tr><td align="left"><p>Enhancement of Researchers' Digital Capabilities</p></td><td align="left"><p>The researchers should develop the ability to verify the information; The researchers must have a robust knowledge of methodologies and techniques for analyzing data</p></td></tr><tr><td align="left"><p>Advancement of Academic Integrity</p></td><td align="left"><p>The code of academic integrity should be updated with clear and explicit language on the use of GenAI; Faculty members are encouraged to engage in ongoing conversations about the importance of academic integrity</p></td></tr><tr><td align="left"><p>Clarification of the Social Impact of GenAI</p></td><td align="left"><p>Analysis of GenAI's use and impact in a domain; Analyzing the environmental costs of leveraging the AI technology at scale</p></td></tr><tr><td align="left"><p>Optimization of User Experience for GenAI Tools</p></td><td align="left"><p>Building GenAI prompt-engineering capacities</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0189592952-9">Synthesis and construction of the transformation action framework for the education system</hd> <p>After completing the initial coding, we needed to deepen the analysis further to avoid merely staying at the surface level interpretation of the policy texts and overlooking their deeper implications. According to Noblit and Hare ([<reflink idref="bib53" id="ref54">53</reflink>]), Meta-ethnography synthesis can take three forms: reciprocal, refutational, and line of argument. Reciprocal synthesis integrates concepts from one study into concepts from other studies to uncover similarities across different studies; refutational synthesis compares and contrasts seemingly contradictory concepts across multiple studies to explore their possible explanations; and line of argument synthesis brings together different aspects from multiple studies to form a new interpretative framework, revealing connections between the aspects.</p> <p>In this study, we adopted a combined approach of reciprocal and line of argument synthesis. First, we conducted a thorough review and comparison of the preliminary coding, juxtaposing related concepts and themes from different texts to identify their commonalities and differences. For example, there were several discussions in the policy texts regarding"technology application"and"educational innovation."By coding these discussions, we could summarize the similarities and differences in the integration of technology across different countries or regions. Then, based on these commonalities and differences, we carried out a line of argument synthesis, attempting to build a new, higher-level interpretive framework by comparing the educational actions under different policy contexts, revealing the interactions and connections between various levels (macro, meso, micro) of educational system transformation.</p> <p>In this process, we particularly focused on juxtaposing the concepts in the initial coding and searching for their potential connections. For instance, at the macro level, we found that the themes of"educational organization"and"educational resources"repeatedly appeared in the policy documents. By integrating the codes of these two themes, we identified their interdependence in promoting educational transformation. Based on this, we formed a comprehensive framework dividing the education system into three levels: macro, meso, and micro. Each level has its representative educational entities, such as educational organizations and institutions (including national and regional education departments) at the macro level, universities and their administrators at the meso level, and teachers and learners, the most important components of the education system, at the micro level.</p> <p>In-depth analysis of these different entities actually represents varying analytical perspectives. Thus, further elaborating, we meticulously analyze from the macro perspective of education-society nexus, the meso perspective of educational patterns and logic evolution, and the micro perspective of tangible educational scenario transformation, aiming to construct a multilayered transformation action framework for the education system in the GenAI era (as illustrated in Table 3 and Fig. 1).</p> <p>Table 3 An analysis of the diverse perspectives and constituent elements of the transformation action framework</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Perspective</p></th><th align="left"><p>The logical train of thought behind the transformation action</p></th><th align="left"><p>Subjects involved in the education system</p></th></tr></thead><tbody><tr><td align="left"><p>Macro perspective</p></td><td align="left"><p>Education-society nexus</p></td><td align="left"><p>Education Administrative Department; Educational Researchers</p></td></tr><tr><td align="left"><p>Meso perspective</p></td><td align="left"><p>Educational patterns and logic evolution</p></td><td align="left"><p>Educational administrators (primarily referring to school-level managers); Tool Developers</p></td></tr><tr><td align="left"><p>Micro perspective</p></td><td align="left"><p>Tangible educational scenario transformation</p></td><td align="left"><p>Teacher and Student</p></td></tr></tbody></table> </ephtml> </p> <p>Graph: Fig. 1 Transformation action framework</p> <hd id="AN0189592952-10">Discussions and insights: enlightening the path of future education through GenAI empowerment</hd> <p>In this chapter, we will discuss and analyze in detail the education system transformation action framework constructed based on the meta-ethnography method in the GenAI era (see Fig. 1). This framework not only considers the macro perspective of the education-society linkage, encompassing productivity innovation and changes in educational foundations, but also delves into the educational evolution action framework, reflecting on the transformation of educational forms and logic from the meso perspective. Additionally, we will deconstruct the new teaching and learning scenarios in the GenAI era, observing from a micro perspective the transformation of teacher roles and the updating of student learning patterns.</p> <p>In the <emph>Education-Society Nexus Framework</emph>, we identify how GenAI drives the productivity revolution, improves industry efficiency, and facilitates industrial transformation, thereby promoting more efficient resource allocation and innovation in the overall economy. This innovation in productivity directly impacts the labor market and human resource demand, especially the sharp increase in demand for high-skilled and GenAI-related fields. Relevant studies indicate that with the popularization of GenAI technology, jobs in automation and intelligence are changing, requiring the workforce to acquire new skills and adaptability [citation needed]. At the same time, the framework also considers how social attitudes towards GenAI and educational actions can adapt to this technology-driven transformation, particularly in the transformation of education policy and societal concepts.</p> <p>The <emph>Educational Evolution Action Framework</emph> further explores the changes in educational forms, from the thinking of educational entities to the evolution of educational carriers and the transformation of educational content, illustrating how GenAI affects all aspects of education. We will specifically analyze how GenAI reshapes educational practices and methods by promoting the personalization and intelligence of educational content, as well as optimizing teacher-student interactions.</p> <p>Finally, in the <emph>Educational Transformation Scenario Framework</emph>, we will explore the application of GenAI in actual teaching activities, how it facilitates the transformation of teacher roles and the refreshment of student learning patterns, particularly in the areas of personalized learning paths, intelligent tutoring, and adaptive learning systems. These changes not only promote innovations in teaching methods but also provide new ideas for the construction of future educational systems.</p> <p>Through this comprehensive analysis, this chapter aims to provide a holistic and profound perspective on understanding and addressing the complexity and multidimensionality of education system transformation in the GenAI era, offering valuable insights and recommendations for scholars, policymakers, and practitioners in the education field.</p> <hd id="AN0189592952-11">Education-society nexus framework: productivity innovation and educational equity (macroscopi...</hd> <p></p> <hd id="AN0189592952-12">Productivity innovation and human resource demand transformation enabled by GenAI</hd> <p>GenAI technology, as an artificial tool, extends human capabilities in areas such as computation, thinking, judgment, and learning. The term"inherent abilities"refers to the fundamental psychological and physiological capacities humans have in cognition, decision-making, and learning, while"extension"means that GenAI enhances the performance of these abilities, particularly in handling large-scale data, identifying complex patterns, and making predictions and decisions. However, this extension does not imply that GenAI is equivalent to human intelligence; rather, it complements and enhances human intelligence through technological means within its framework (Chen et al., [<reflink idref="bib18" id="ref55">18</reflink>]).</p> <p>Supported by vast amounts of data, GenAI shows significant advantages in recognizing, understanding, and applying patterns. These advantages do not equate to"machine forms of human intelligence,"but rather reflect GenAI's optimization ability in specific tasks. It mimics human decision-making processes through data-driven computational models for information processing and response (Jing et al., [<reflink idref="bib40" id="ref56">40</reflink>]). This display of capability is based on the differences between machine intelligence and biological intelligence, exploring GenAI's complementary role to human capabilities.</p> <p>GenAI's question-and-answer interaction, along with its no-download and no-configuration requirements, greatly lowers the barriers to technology usage, promoting the widespread and equitable adoption of GenAI technology. Furthermore, real-time data support after networking, open-source large models, and plugin ecosystems enhance users' creativity, further expanding GenAI's application and influence across various industries. However, this technological progress brings not only innovation but also potential unequal social consequences, particularly in terms of global disparities in resource access and technological application.</p> <p>From the macro perspective of comprehensive policy guidelines and related reports, many policy researchers believe that GenAI will become infrastructure in the near future, with certain foundational tasks being replaced, reshaping the structure of society's workforce and talent demands (EDUCAUSE, [<reflink idref="bib26" id="ref57">26</reflink>], [<reflink idref="bib25" id="ref58">25</reflink>]; UNESCO, [<reflink idref="bib80" id="ref59">80</reflink>]). However, technological leaps and productivity improvements do not automatically translate into overall societal welfare enhancement. On the contrary, this process often comes at the expense of certain groups, exacerbating changes in social structure and labor demand. While the GenAI application ecosystem thrives, with user-friendly interfaces and lower API costs making it more accessible, we must also consider its environmental costs and the global disparities in technology access—issues that need to be balanced during the development of the technology.</p> <p>Under the influence of GenAI, entry-level professionals and technical workers, especially certain white-collar workers in specific fields, may face the greatest challenges, which is different from the challenges traditionally faced by blue-collar workers in previous automation revolutions. According to the"Future of Jobs Report 2023,"the demand for big data and GenAI-related jobs will grow rapidly in the next five years, while general skills such as creative thinking, technological literacy, curiosity, and learning ability will become the most important skills for the future (Huang et al., [<reflink idref="bib33" id="ref60">33</reflink>]; Wang et al., [<reflink idref="bib83" id="ref61">83</reflink>]; World Economic Forum, [<reflink idref="bib93" id="ref62">93</reflink>]). In the context of the intelligent era, traditional skills can no longer meet societal needs, disciplinary barriers continue to break down, and the demand for cross-disciplinary talent is growing. Therefore, it can be foreseen that GenAI, as a new productive force, will drive a disruptive transformation in talent demand, and the education sector should adjust and transform accordingly in response to this change.</p> <hd id="AN0189592952-13">Societal attitudes and educational initiatives towards GenAI</hd> <p>Nearly all policy documents explicitly articulate their perspectives on GenAI, a burgeoning technology, acknowledging it as a double-edged sword. The education sector is urged to adopt an objective and rational approach, striving to leverage its advantages while mitigating its disadvantages (UK Department for Education, [<reflink idref="bib77" id="ref63">77</reflink>]; UNESCO, [<reflink idref="bib78" id="ref64">78</reflink>]), which is a stance largely concordant with academic views (Chen et al., [<reflink idref="bib18" id="ref65">18</reflink>]). As GenAI technology becomes increasingly prevalent in education, societal attitudes towards it have been increasingly complex, evolving from initial concerns and resistance to gradual acceptance and integration, reflecting the understanding and adaptation to the emerging technology as a dynamic process. Education action plays a pivotal role in this evolution, addressing not only the direct challenges posed by technology implementation, such as combating cheating, but also delving into how GenAI can foster pedagogical innovation and enhance learning efficiency (Adeshola & Adepoju, [<reflink idref="bib1" id="ref66">1</reflink>]). Moreover, society and educators should collaboratively reflect on and devise strategies to concurrently cultivate students' technical competencies and reinforce their ethical education and critical thinking, ensuring that technological applications are balanced with humanistic concerns, thereby preparing students to be responsible and well-rounded citizens in a technologically integrated future (Esmaeilzadeh, [<reflink idref="bib27" id="ref67">27</reflink>]). Additionally, by fostering interdisciplinary collaboration and communication, various societal sectors can better comprehend GenAI's potential and challenges, collectively advancing technological ethics and laying a solid foundation for educational and societal progress.</p> <p>Hence, societal attitudes and educational initiatives towards GenAI should adhere to principles of openness, prudence and innovation, aiming to fully harness the educational potential of GenAI technology while ensuring effective risk management (Adeshola & Adepoju, [<reflink idref="bib1" id="ref68">1</reflink>]). Societal attitudes should be the encouragement and facilitation of interdisciplinary dialogue and collaboration, aligning technological development with educational demands to support the enhancement of educational quality and equity. Educational institutions should collaborate with technology developers to explore optimal practices and applications of GenAI in education, ensuring that educational policies evolve in tandem with technological advancements to promote educational equity and inclusivity.</p> <p>At the action level, educational departments should conduct GenAI-related training and education for teachers, students and administrators to augment their understanding and application capabilities while heightening their awareness of potential ethical and social issues (Chiu, [<reflink idref="bib20" id="ref69">20</reflink>]). Furthermore, policymakers should address the digital divide engendered by GenAI, devising strategies to ensure equitable access to these advanced tools for all students, thereby mitigating technological disparities (Su & Yang, [<reflink idref="bib73" id="ref70">73</reflink>]).</p> <p>In summary, in the face of the GenAI era, both society and the education sector should actively adapt to and guide technological development. We must not only promote the application and popularization of technology but also pay attention to its potential risks, taking effective measures to prevent them, ensuring that advancements in educational technology can foster educational equity, improve quality, and drive innovation.</p> <hd id="AN0189592952-14">Challenges and solutions for educational equity in the GenAI era</hd> <p>In the GenAI era, the challenge of educational equity extends beyond merely ensuring equitable access to technology; it also concerns guaranteeing that all students have equal learning opportunities and benefits from this technology (EDUCAUSE, [<reflink idref="bib26" id="ref71">26</reflink>]; UNESCO, [<reflink idref="bib80" id="ref72">80</reflink>], [<reflink idref="bib79" id="ref73">79</reflink>]). Although GenAI can power personalized learning, it may also exacerbate the impact of socio-economic status disparities on education. For instance, children from higher-income families may have easier access to high-quality GenAI learning resources and tutoring, while those from lower-income families might face barriers in accessing these resources.</p> <p>To address these challenges, educational policymakers and relevant government organizations need to take actions on a broader level to ensure educational equity. This includes formulating policies that promote the widespread distribution of high-quality educational resources, such as through collaborations between government and non-governmental organizations to provide necessary technological and educational support to schools in remote and low-income areas.</p> <p>Furthermore, structural adjustments within the education system are essential, such as improving curriculum design to integrate the cultivation of GenAI-related knowledge and skills, ensuring that all students are equipped to leverage these advanced tools for learning and problem-solving. Additionally, public education campaigns and the provision of online resources can enhance public awareness of GenAI's potential impacts and encourage participation from all sectors of society in efforts towards educational equity (Chen et al., [<reflink idref="bib17" id="ref74">17</reflink>]).</p> <p>Finally, to monitor and evaluate the effectiveness of measures for educational equity, it is recommended to establish a multi-stakeholder platform comprising educators, technology experts, policymakers and community representatives (UK Department for Education, [<reflink idref="bib77" id="ref75">77</reflink>]). This platform can facilitate knowledge sharing, monitor the progress of GenAI technology applications in education, and ensure that all actions effectively serve the goal of enhancing educational fairness. Through such comprehensive and collaborative efforts, we can better harness the potential of GenAI technology to provide high-quality educational opportunities for every student, and to ensure that everyone can benefit equitably from educational advancements in future society, actualizing the welfare potential of GenAI as a new productive force.</p> <hd id="AN0189592952-15">Ethical and moral considerations of GenAI in education</hd> <p>With the widespread application of GenAI technology in education, ensuring its ethical use has become an urgent issue to address in global educational policies and practices. While GenAI can enhance learning efficiency, personalize education, and foster teaching innovation, its potential ethical risks have sparked widespread discussion (Han, [<reflink idref="bib31" id="ref76">31</reflink>]).</p> <p>When using GenAI in education, students' personal data and learning behaviors may be collected, analyzed, and stored, posing a threat to their privacy rights. This is especially critical for minor students, as protecting their personal information from misuse, leakage, or commercial exploitation is essential. Education systems should establish strict data protection measures, ensuring that the use of GenAI technology complies with national and regional privacy protection laws, with high transparency in data collection and usage, allowing students and parents to be informed and consent to the use of their data.</p> <p>The core of GenAI technology relies on algorithms, and algorithmic bias and unfairness have always been challenges faced by AI technology. GenAI applications in education may lead to unjust evaluations or opportunities for certain groups of students due to incomplete or biased data sources. For example, recommendation algorithms based on students' grades or background data may unintentionally exacerbate social inequality or disadvantage low-income students. Educators and technology developers should focus on algorithmic fairness, striving to eliminate biases and ensure that all students have equal learning opportunities on GenAI platforms.</p> <p>Moreover, the widespread use of GenAI as an educational tool may also change the roles and responsibilities of teachers and students in the teaching process. Teachers are shifting from traditional"knowledge transmitters"to"guides"and"collaborative partners,"while students' ability to learn independently and think critically will become key in the learning process. However, this transformation may bring certain challenges, especially when teachers are not adequately trained, or students fail to utilize GenAI tools correctly. Therefore, education policymakers need to support ongoing teacher training to ensure they can play an active role in this new environment while also cultivating students' technological literacy and ethical awareness.</p> <p>In the application of GenAI in education, in addition to cultivating technological abilities, it is crucial to integrate ethics education. Education should encourage students to deeply reflect on the ethical issues behind GenAI technologies, such as the transparency of GenAI decision-making, machine bias, and the impact of GenAI on the job market. At the same time, students should be guided to focus on how to responsibly use technology, avoiding its potential negative effects (Zhang et al., [<reflink idref="bib98" id="ref77">98</reflink>]). Therefore, education systems should incorporate ethics teaching into curriculum design, enabling students to develop critical thinking and social responsibility in an era of rapid technological development, thus promoting the positive development of technology.</p> <p>In conclusion, the ethical use of GenAI in education is not only a technical issue but also a societal one. To ensure its positive role in education, education systems need to integrate ethical considerations into technology applications. Through interdisciplinary collaboration, technological regulation, and ethics education, we can ensure that GenAI technology development remains human-centered, promoting fairness, quality, and innovation in education.</p> <hd id="AN0189592952-16">Educational evolution action framework: reflecting on educational modes and logics (mesoscopi...</hd> <p></p> <hd id="AN0189592952-17">Transformation of educational forms in the GenAI era</hd> <p>The evolution of education lies in integrating the cutting-edge production technology with educational theories to explore"how to achieve better teaching". Many policy documents emphasize the impact of GenAI, a representative of the advanced production technology, on what changes and what remains in education. A deep analysis of these documents reveals that GenAI is unlikely to fundamentally transform the form and mode of education in the short term. Education, serving the ancient function of human cultural and knowledge transmission, possesses inherent characteristics of continuity, stability and heredity, which dictate that it does not evolve rapidly with technological advancements in production. Past scholars have tended to exaggerate the transformative value of emerging technologies for educational change, whether it be internet technology, information and communication technology, or mobile learning technology. Therefore, Selwyn ([<reflink idref="bib64" id="ref78">64</reflink>]) once pointed out:"computer technology use is constructed in limited, linear, and rigid terms far removed from the creative, productive, and empowering uses which are often celebrated by educational technologists."This perspective remains valuable today. This is because, on top of traditional educational modes, advanced production technology primarily introduces innovations in educational resource types, teaching organization methods and interactive modalities. Their impact is cumulative, incremental and gradual rather than outright substitutive. The conventional classroom teaching paradigm of"teacher instruction—student absorption—knowledge transmission"remains the mainstream educational model, fundamentally unchanged from centuries past. How to leverage the cutting-edge scientific technology combined with modern educational theories to realize education on a larger scale, of higher quality, of higher efficiency, and with better outcomes, remains a persistent inquiry in contemporary educational research (Valtonen et al., [<reflink idref="bib81" id="ref79">81</reflink>]).</p> <p>In the GenAI era, educational forms have undergone more profound transformations, integrating artificial intelligence deeply with teaching and bringing disruptive changes to traditional education. Education in this era extends beyond classroom knowledge transmission to broader learning environments and contexts, enabling more personalized, adaptive, flexible, and interactive learning experiences that significantly address the shortcomings of traditional industrialized and scale teaching modes (Mo et al., [<reflink idref="bib50" id="ref80">50</reflink>]). Based on discussions in various policy documents, we can summarize the educational transformation as shown in Fig. 2. The current stage of GenAI's empowerment pathway offers the greatest value at the meso-level by fully utilizing existing data sources to improve school educational forms. More specifically, the application of GenAI technologies, such as intelligent tutoring systems, virtual reality learning environments, and personalized learning paths, significantly enriches teaching methods and learning modalities, fostering students' active learning and innovative thinking (Zhu et al., [<reflink idref="bib99" id="ref81">99</reflink>]).</p> <p>Graph: Fig. 2 The practical logic of GAI's transformation of meso-level educational forms</p> <p>Moreover, the educational landscape in the era of GenAI emphasizes the integration and innovation of interdisciplinary knowledge, encouraging students to develop comprehensive problem-solving abilities, as highlighted by the CU Committee ([<reflink idref="bib22" id="ref82">22</reflink>]). This also implies that AI-assisted teaching not only enhances the efficiency and quality of education but also promotes greater equity and accessibility, helping to reduce learning gaps among students from diverse backgrounds. Simultaneously, the education system itself continuously learns and adapts, using GenAI to optimize and improve educational policies, curriculum design, and teacher training, ensuring that educational practices evolve in tandem with societal and economic development demands. In this process, the role of teachers gradually shifts to that of facilitators, supporters and learning partners, while students become the central agents of learning, actively exploring, collaborating and creating, together shaping a more open and innovative learning environment (Wang et al., [<reflink idref="bib86" id="ref83">86</reflink>]).</p> <p>However, we must acknowledge that this discourse on the positive role of GenAI in education is not without controversy. As Selwyn ([<reflink idref="bib66" id="ref84">66</reflink>]) points out, the history of educational technology is replete with overly optimistic expectations and unfulfilled promises. Critical scholars such as Selwyn ([<reflink idref="bib65" id="ref85">65</reflink>]) have long called for a more cautious and critical approach to educational technology, emphasizing the need to"look beyond learning"and focus on the power structures, social inequalities, and realization of educational values behind technology. Williamson and Eynon ([<reflink idref="bib91" id="ref86">91</reflink>]) further argue that the application of AI in education has historical gaps and missing links, requiring a more comprehensive and critical analytical framework.</p> <p>We recognize that the application of GenAI technology in education faces numerous challenges and risks, including data privacy and security issues, algorithmic bias, impacts on teacher professional development, verification of learning content authenticity, and the potential exacerbation of the digital divide. As Selwyn's ([<reflink idref="bib67" id="ref87">67</reflink>]) concept of"digital degrowth"suggests, we need to rethink sustainable development pathways for technology in education, rather than blindly pursuing unlimited technological expansion. Therefore, this study, while analyzing GenAI education policies across countries, focuses not only on their positive transformative potential but also fully considers these critical challenges, striving to present a more balanced and critical perspective. Our position is that GenAI technology provides important opportunities for educational transformation, but such transformation must be thoughtful, responsible, and sustainable, requiring a balance between technological innovation and educational essence, social equity and ethical considerations.</p> <hd id="AN0189592952-18">Reflections on educational entities in the GenAI era</hd> <p>In discussions on the transformative impact of GenAI technology in the field of education, its effect on the teaching entities is a notable topic. Based on an analysis and synthesis of multiple policy documents, we can delineate the potential forms of educational entities under GenAI intervention as illustrated in Fig. 3. In the conventional teacher-student instructional model, the teacher acts as an authoritative knowledge transmitter. However, this model faces challenges such as uneven distribution of teaching resources, difficulties in personalized instruction, and the burdensome mechanical labor that hinders teachers' professional growth. The introduction of GenAI technology is perceived as an opportunity, symbolizing the potential shift from traditional teaching models to a"teacher-student-computer"one. GenAI's capabilities to supplement teaching resources, facilitate large-scale personalized instruction, and alleviate teachers' workloads increase the feasibility of enhancing educational quality, thereby gaining recognition in both academia and the market. Simultaneously, human teachers can transition from traditional knowledge disseminators to companions in learning and guides in values' fostering.</p> <p>Graph: Fig. 3 Possible Forms of Educational Entities in the GAI Era</p> <p>However, concerns about"GenAI completely replacing human teachers"also exist. The current non-neutrality of GenAI's values, the lack of accuracy in information, issues related to copyright, and its inability to replicate human emotions and cultural values make it difficult for GenAI to stand alone. Moreover, the possibility of GenAI entirely taking over human teaching roles may trigger anxiety regarding teacher employment. The discussions on both sides highlight the need for a careful delineation of GenAI's supportive application in education, ensuring it becomes a helper rather than an obstacle for educators.</p> <p>A closer look at the evolution of the role of educators reveals that the future role of teachers will gradually transform, placing more emphasis on their functions as learning facilitators, innovation guides, and emotional supporters. Teachers will no longer merely act as knowledge transmitters, but will become designers and guides of the learning process. They will use GenAI technology to personalize learning content and pathways (for example, using large language models like ChatGPT to generate text-based and image-based learning materials, or using large visual models like Sora to generate video teaching content), helping each student discover and reach their full potential.</p> <p>This shift is supported by research. For instance, educational research indicates that personalized learning can effectively improve student motivation and academic performance (Shemshack & Spector, [<reflink idref="bib70" id="ref88">70</reflink>]). Furthermore, providing students with customized resources and recommendations using AI has been shown to enhance their active learning and critical thinking abilities (Holmes, [<reflink idref="bib32" id="ref89">32</reflink>]). AI-assisted personalized learning not only improves educational quality but also helps close learning gaps between students from different backgrounds, especially in resource-poor areas (Ryzheva et al., [<reflink idref="bib61" id="ref90">61</reflink>]). These findings suggest that the application of GenAI technology can not only enhance learning outcomes but also contribute to making education more equitable and accessible.</p> <p>At the same time, schools, as one of the key educational entities, will experience innovation in their management and operations driven by GenAI technology. Data-driven decision-making will become more widespread, helping school administrators more effectively allocate resources, monitor teaching quality and student progress, and respond to educational needs and social changes. In this process, collaboration and interaction among educational stakeholders will also be enhanced. Teachers, students, parents, and school administrators will need to jointly build an active and open learning community, facing the opportunities and challenges brought by GenAI together, ensuring that the progress of educational technology truly benefits every learner and promotes the realization of equitable and quality education.</p> <hd id="AN0189592952-19">Evolution of educational media in the GenAI era</hd> <p>Educational media, encompassing both software and hardware foundations and tools, have always been at the forefront of advanced technology integration. Thanks to the continuous enhancement of information infrastructure across educational institutions worldwide, the impact of GenAI technology on educational media is increasingly evident: GenAI's involvement at the instructional level renders advanced educational concepts like large-scale personalized teaching more feasible; its viability in empowering teaching and educational research is also widely acknowledged. However, the application of this technology is accompanied by challenges and controversies: issues related to the accuracy of GenAI and potential value biases, the technological dependence that prolonged use of GenAI by students may lead to, and the pressures that GenAI-assisted cheating poses to assessment tasks have elicited skepticism or opposition regarding the direct use of GenAI by students (particularly children and adolescents) in policy documents, underscoring the conditional use and effective control of GenAI as a learning tool (Jin et al., [<reflink idref="bib38" id="ref91">38</reflink>]). Amid these opportunities and challenges, educators are called to adapt to technological advancements and adopt more advanced, fair, comprehensive and balanced tech tools while critically evaluating the functional systems and ethical boundaries of GenAI-based educational media to ensure its beneficial and effective service to the whole education system.</p> <p>Numerous documents propose action guidelines for educational product developers, providing a top-level logic for the design of GenAI-associated educational products. Analyzing these perspectives, the following approach can be summarized: the externalization and hardware integration of GenAI are essential paths for technological evolution, but mere simple embedding of software functions fails to capitalize on the unique advantages of hardware and the value of multimodal data integration. GenAI-related technology is converging, with large language models addressing high-level cognitive issues and multimodality offering channels for data interaction and integration. Agent and embodied intelligent robots tackle task planning and execution in virtual and physical realms respectively (Lin & Yu, [<reflink idref="bib45" id="ref92">45</reflink>]), delineating a comprehensive vision for AGI (Artificial General Intelligence). In educational contexts, large models address personalized teaching through data analysis and content generation; native multimodal capabilities integrate various learning data for comprehensive student assessments and generate multimodal resources to meet individual needs, such as those of special-needs children. Agent can assist teachers in parts of their tasks like creating teaching materials and grading assignments, and can perform experimental planning, implementation and data forecasting in research. Embodied intelligent robots (for example, embodied virtual teachers generated by AI), with their wide applicability across various teaching scenarios and numerous sensing units, can delve deep into specific educational contexts (Lin & Yu, [<reflink idref="bib46" id="ref93">46</reflink>]). Moreover, with their societal entity benefits, these robots can interact with students through expressions and gestures, establishing emotional connections and providing motivational and companionate educational roles.</p> <hd id="AN0189592952-20">Reformation of educational content in the GenAI era</hd> <p>In the GenAI era, the importance of advanced general competencies and interdisciplinary skills is reaffirmed, and the demand for AI-related literacy is emphasized, which is not only frequently mentioned in numerous policy documents (UNESCO, [<reflink idref="bib80" id="ref94">80</reflink>], [<reflink idref="bib79" id="ref95">79</reflink>]; EDUCAUSE, [<reflink idref="bib26" id="ref96">26</reflink>], [<reflink idref="bib25" id="ref97">25</reflink>]; CU Committee, [<reflink idref="bib22" id="ref98">22</reflink>]) but is acknowledged within the academic community (Jing et al., [<reflink idref="bib40" id="ref99">40</reflink>]). As the latest GenAI technology significantly enhances societal productivity, it concurrently precipitates shifts in the relations of production and the reorganization of the societal division of labor, with AI displacing certain traditional roles and emergent professions swiftly arising, while some established disciplines are gradually phased out. This era poses a substantial and challenging research question for the education system: How can we cultivate valuable talents capable of adapting to these changes? This encompasses not only adjustments in the scope, goals, and content of education but also alterations in academic and professional program structures. Furthermore, the success of large models like ChatGPT in passing various professional exams, such as those for accountants and lawyers, signifies their capabilities in information gathering and integration nearing or surpassing human proficiency, prompting a reconsideration of the societal adaptability of mere professional knowledge instruction in the GenAI era.</p> <p>Thus, the education system needs to revise its curricular content, emphasizing the cultivation of information literacy and advanced general competencies, and progressively augmenting the emphasis on interdisciplinary integrative education to expedite the provision of cutting-edge talent (Huang et al., [<reflink idref="bib33" id="ref100">33</reflink>]). Simultaneously, the education system must promptly define the scope and standards surrounding GenAI technology learning, aiding learners in embracing and adapting to the onset of the GenAI era and comprehending the technology and its extensive impact on the world. This will ensure that future technological professionals are equipped to navigate and influence the direction and pace of technological advancements, possessing the capacity to utilize GenAI technology to better transform the world and benefit humanity.</p> <hd id="AN0189592952-21">Educational transformation scenarios framework: educational applications of GenAI (microscopi...</hd> <p></p> <hd id="AN0189592952-22">Transformation of teacher roles in the GenAI era</hd> <p>In the GenAI era, the mode of teaching is undergoing profound transformation, gradually shifting from traditional methods reliant on teachers' experience to data-driven approaches. The role of teachers has also undergone significant changes during this transition (Ausat et al., [<reflink idref="bib4" id="ref101">4</reflink>]). Specifically, teachers' work will focus more on creative tasks, and this change is particularly noticeable in lesson preparation. Preparing lessons involves not only a significant workload but also a high degree of creativity. The workload primarily involves writing course syllabi and lesson plans for different grade levels and classes, gathering teaching resources, and creating teaching materials. The creative demands mainly involve making personalized adjustments based on the learning situation and progress of different classes, as well as designing effective teacher-student interactions. The application of GenAI technology, especially the use of big data to collect teaching resources and create teaching materials, makes these routine tasks more efficient, saving teachers a substantial amount of time. This allows teachers to focus on more creative, emotional, and social tasks, such as communication with students, providing student care, and designing interactive activities.</p> <p>In terms of the specific manifestation of teacher transformation, the change in the role of teachers in the GenAI era reflects a fundamental shift from being traditional knowledge transmitters to becoming learning facilitators, innovation promoters, and emotional supporters (Jeon & Lee, [<reflink idref="bib36" id="ref102">36</reflink>]). The core of this change lies in the introduction of GenAI technology, which can handle a large volume of information processing and basic educational tasks, thereby freeing teachers to focus more on promoting personalized learning and holistic development for students.</p> <p>At the macro level, the transformation of the teacher's role is mainly reflected in the repositioning of educational policies and school systems. GenAI technology has driven educational policies toward a more personalized and data-driven direction. Teachers are not only required to adapt to new technologies but also need to engage at a higher level in educational reforms, promoting collaboration between schools and communities. Teachers will increasingly be seen as innovators and leaders of social change within the educational system. This level of transformation emphasizes the strategic role of teachers in policy-making and educational philosophy updates, particularly during the large-scale transformation of the education system, where teachers' roles become more important.</p> <p>At the micro level, the transformation of teachers' roles is more specific and direct, focusing on daily teaching practices. With GenAI technology, teachers gain deep insights and data that allow them to precisely identify and respond to each student's learning needs. This not only makes learning more personalized but also enhances the effectiveness of teaching. At this level, teachers play the roles of learning guides, motivators, and designers of personalized education. At the same time, teachers need to provide emotional support in the classroom, helping students build self-confidence, develop interpersonal skills, and adapt to the ever-changing social demands. This level of transformation highlights the critical role of teachers in fostering students' innovative thinking, problem-solving abilities, and emotional and social skills.</p> <p>Overall, the transformation of teachers' roles differs between the macro and micro levels. At the macro level, teachers, as drivers of educational systems and policies, bear greater strategic responsibility; while at the micro level, teachers focus more on the practical aspects of personalized learning and emotional support for students. These transformations complement each other and jointly drive comprehensive changes in education.</p> <hd id="AN0189592952-23">Update of student learning patterns in the GenAI era</hd> <p>Under traditional artificial intelligence technology, students often face challenges in oral practice, such as simplistic dialogues, limited scenarios, difficulty in conducting multi-turn interactions, and a lack of personalization. However, the application of GenAI is transforming this situation. By expanding the parameters of large models and adopting pre-training architectures, GenAI enhances capabilities in multi-turn dialogue, logical reasoning, and contextual understanding (Baidoo-Anu & Ansah, [<reflink idref="bib5" id="ref103">5</reflink>]; Chen et al., [<reflink idref="bib18" id="ref104">18</reflink>]). Additionally, GenAI generates new data rather than merely recognizing and classifying existing ones, enabling it to handle a broader range of scenarios and tasks, thus meeting user demands for specialized learning contexts. When combined with digital humans and humanoid robots, multimodal large models can recognize students' vocal and facial expressions, providing human-like interactive companionship through lifelike appearances, thereby introducing new possibilities for educational interactions.</p> <p>From the perspective of maturity across different specialized scenarios, tasks such as oral and writing exercises exhibit a higher level of application readiness and adoption compared to more logic-driven subjects like mathematics and chemistry. This is partly because users tend to have a higher tolerance for errors in these tasks. This observation aligns with the UNESCO's Guidelines for "Generative AI in Education and Research", which indicate that language learning applications are relatively mature, and users demonstrate greater acceptance of unstructured learning content (UNESCO, [<reflink idref="bib79" id="ref105">79</reflink>]). This suggests that when promoting GenAI technology, it is essential to consider subject-specific characteristics and varying learning models. In particular, content related to language learning and other competency-based education areas is more suited for flexible AI-assisted systems, as these fields embrace diverse learning models and have relatively lenient assessment standards, making them better aligned with GenAI's non-standardized nature (Cai et al., [<reflink idref="bib12" id="ref106">12</reflink>]; Chen et al., [<reflink idref="bib16" id="ref107">16</reflink>]; Yun et al., [<reflink idref="bib96" id="ref108">96</reflink>]).</p> <p>GenAI technology not only supports academic education but also presents significant opportunities for competency-based education. The strengths of GenAI in knowledge experience, efficient processing, and generalization capabilities mirror the advantages of human creativity and adaptability as key components of general literacy (Huang et al., [<reflink idref="bib33" id="ref109">33</reflink>]; Yan, [<reflink idref="bib95" id="ref110">95</reflink>]). Competency-based educational content has a natural affinity with GenAI, as it emphasizes knowledge generation, spans a broader and deeper knowledge scope with scattered knowledge points, and features more personalized and non-standardized approaches to knowledge construction and assessment—attributes that align well with GenAI's characteristics. In contrast, academic subjects tend to have clearer knowledge structures and standardized evaluation methods, making them more suitable for AI techniques such as structured knowledge graphs and classification-based models.</p> <p>However, UNESCO's guidelines emphasize that the application of GenAI in education must remain human-centered, preserving human creativity and autonomy (UNESCO, [<reflink idref="bib80" id="ref111">80</reflink>]). This implies that while GenAI holds immense potential in education, its implementation should focus on augmenting rather than replacing human capabilities. Therefore, integrating GenAI into education requires careful design and implementation to ensure ethical compliance and genuinely foster students' holistic development.</p> <hd id="AN0189592952-24">New modern education in the GenAI era</hd> <p>Upon closer examination of the post-reform education scenarios, it is evident that policy documents converge on the vision of future education empowered by GenAI technology. Numerous policy documents articulate that the modern education system, a product of the industrial revolution era, utilizes scale education approaches devoid of supportive policies for personalized learning. The emergence of GenAI offers a viable avenue to break this mold (UNESCO, [<reflink idref="bib80" id="ref112">80</reflink>], [<reflink idref="bib78" id="ref113">78</reflink>]). However, to realize personalized adaptive learning supported by artificial intelligence, it is essential to implement supportive measures not only technologically and methodologically but also at the policy level, encompassing education system, resources, evaluation methods and teacher support (Gill et al., [<reflink idref="bib30" id="ref114">30</reflink>]; Mirata & Bergamin, [<reflink idref="bib49" id="ref115">49</reflink>]).</p> <p>Furthermore, there is a natural alignment between GenAI technology and modern education in terms of teaching content, faculty allocation and interaction modalities, highlighting the necessity for technological integration. The prevailing educational mode, shaped during the industrial revolution, is characterized by its scale and standardization, aiming to cultivate talents for various societal roles based on the logic of social division of labor. This corresponds to specialized disciplinary structures and scaled teaching through hierarchical and class-based grouping (Tomczyk et al., [<reflink idref="bib75" id="ref116">75</reflink>]). Under GenAI applications, the coexistence of massive general data with specific educational domain data can satisfy the requirements of both general and specialized education. Unlike teacher resources, GenAI resources are not limited by time and space, promising widespread personalized instruction. Notably, in terms of interaction, oral face-to-face instruction is the conventional and familiar teaching method, while GenAI's unique capability for multi-turn natural language interactions offers a dialogic approach closer to the Socratic method of elicitive teaching than one-way knowledge transmission (Ding et al., [<reflink idref="bib24" id="ref117">24</reflink>]; Jin et al., [<reflink idref="bib37" id="ref118">37</reflink>]).</p> <p>Thus, the future of education will likely witness a symbiosis between GenAI and humans, a trend anticipated by communication theorist Harold Innis ([<reflink idref="bib34" id="ref119">34</reflink>]), who argued that technology could engender new knowledge and civilizations through mediation. With the support of vast data, GenAI's capabilities in pattern recognition and application surpass those of the human brain, gradually equating or even exceeding human contributions in labor and value creation (Peng et al., [<reflink idref="bib56" id="ref120">56</reflink>]), evolving human–machine relations from"symbiosis"to"mutualism"(Fui-Hoon Nah et al., [<reflink idref="bib29" id="ref121">29</reflink>]). At the"symbiosis"level, this reflects in co-education and resource sharing between humans and AI. Both humans and AI are learners and contributors; AI externalizes and extends human cognitive processes, sharing intrinsic logical similarities, and both require extensive societal and data resource support for development. At the"mutualism"level, GenAI can be viewed as an"external brain", complementing the"internal brain"with distinct functional roles and collaborative interaction. The internal brain, due to its creativity and flexibility, should guide the collaboration direction and technological boundaries, customizing the external brain based on personalized needs; the external brain, capable of efficiently and standardly completing numerous tasks, also possesses generalized processing capabilities in various scenarios, significantly enhancing human brain efficacy. Throughout the prolonged evolution of civilization, humans and GenAI will perpetually learn and construct together, diverging from the mere utilization of GenAI, marking an iterative and interwoven process of co-learning (as depicted in Fig. 4).</p> <p>Graph: Fig. 4 The Evolutionary Relationship Between GenAI and Human Education</p> <hd id="AN0189592952-25">Reflection and outlook: interpretation and critique of technological optimism in policy</hd> <p>Against the backdrop of contemporary educational reform increasingly driven by technology, GenAI, as a representative of the latest wave of educational technology innovation, has been invested with broad expectations in policy discourse. However, these expectations are often framed within"technological determinism"or a"techno-utopian"narrative, overlooking the cultural, social, and institutional diversity embedded within education systems. This chapter seeks to conduct a systematic critique of technology across macro, meso, and micro levels, revealing the narrative logic that positions GenAI as an 'agent of progress' in policy construction (see Fig. 5), as well as the implicit biases and real-world tensions this logic generates at different levels. Through a deconstructive reading of policy texts, we aim to dismantle the myth of unidirectional technological transformation in education and return to deeper reflections on the ontology of education, its practical complexity, and its humanistic values.</p> <p>Graph: Fig. 5 The core issues and interconnections in the critique of technological optimism</p> <p>Specifically, at the macro level, policy texts construct a narrative of a 'technological miracle' by linking GenAI with educational equity and national competitiveness, while intentionally or unintentionally obscuring core issues such as systemic inequality and resource redistribution. At the meso level, GenAI is assigned the role of a mediator in governance reform and curriculum innovation, supplanting reflection on the meaning of education and the humanistic spirit, thus giving rise to a 'myth of technological mediation.' Finally, at the micro level, GenAI is shaped as a rational and precise 'cognitive partner,' yet it struggles to respond to the emotional, ethical, and subjective needs inherent in learning processes, ultimately slipping into a dehumanized logic of educational interaction. These three levels—macro, meso, and micro—are nested within one another, forming a systemic structure of the current techno-optimist narrative. This chapter aims to raise critical questions from within this system, in hopes of promoting a more human-centered and structurally conscious path for the development of educational technology.</p> <hd id="AN0189592952-26">Macro-perspective technological discourse: the"miracle imagination"of educational productivit...</hd> <p>At the <emph>Education-Society Nexus Framework</emph> level, multiple policy documents emphasize GenAI's key role in enhancing national educational productivity and promoting educational equity. Whether through slogans like"intelligent learning assistance"in policy texts or vision statements about"bridging the urban–rural digital divide,"these all demonstrate a typical technological optimism that believes deploying advanced technology can automatically achieve systemic transformation of educational systems. This narrative treats technology as a"universal key,"obscuring the deep structural reforms and resource redistribution mechanisms required behind the technology. In fact, although this critical perspective has been raised in previous scholarly research (e.g., Williamson et al., [<reflink idref="bib92" id="ref122">92</reflink>]), it deserves renewed analysis and discussion in the current context of rapid GenAI technological development and swift invasion into educational systems.</p> <p>Upon further examination of this viewpoint, this techno-utopian logic ignores a crucial issue: achieving educational equity depends not only on tool-level innovation but is more deeply connected to systemic variables such as institutional design, regional development, and cultural resources (Watson & Romic, [<reflink idref="bib89" id="ref123">89</reflink>]). For GenAI to play an actual role in narrowing educational gaps, it requires support from specific institutional guarantees, data governance, and regulatory mechanisms (Jin et al., [<reflink idref="bib39" id="ref124">39</reflink>]). Technology can only provide"possibilities"and cannot substitute for"institutional pathways."In existing policy documents, this complexity is often simplified as"technology-driven equity,"with the potential risk of technologizing educational problems and thereby concealing structural inequities.</p> <p>Therefore, we must confront the institutional dependence of GenAI technology applications at this level and raise more politically and economically conscious critical questions, such as: How are technological resources distributed across different regions? Are algorithmic designs embedded with mainstream discourse biases? Only by reflecting on the institutional presuppositions and value orientations behind these policy documents can we truly understand how macro-level technological optimism has become one of the dominant narratives in contemporary educational reform.</p> <hd id="AN0189592952-27">Implicit assumptions at the meso level: the "myth of technical mediation" in educational evol...</hd> <p>At the meso level, the <emph>Educational Evolution Action Framework</emph> emphasizes the transformative impact of GenAI on teaching organization models, curriculum structures, and modes of educational governance. Policy texts commonly place technological mediation at the center, assuming that the mere introduction of intelligent platforms and AI-powered mechanisms will automatically lead to more "precise," "intelligent," and "automated" forms of governance. This logic reduces the process of educational evolution to a linear, technology-driven progression, overlooking the embedded value tensions and humanistic complexities within education systems.</p> <p>This so-called "myth of technical mediation" effectively weakens the humanistic tradition's claim over the meaning of education. In the age of GenAI, the normative assertions of educational values are increasingly in tension with the exclusivity imposed by technological systems (Alfredo et al., [<reflink idref="bib2" id="ref125">2</reflink>]). For instance, some policies relegate the role of teachers to mere data inputters and executors of platform commands, marginalizing their professional judgment and emotional engagement in education. Technology, as a "mediator," is often granted an almost sacred neutrality and objectivity—yet this is precisely what must be questioned. How is technology deployed? Who controls it? Whom does it serve? These critical questions are frequently absent in policy documents, even as current cutting-edge research actively addresses the paradigm shifts and ontological nature of technology (O'Dea, [<reflink idref="bib54" id="ref126">54</reflink>]). Thus, any policy assessment of technological neutrality must be grounded in the actual application and use of technology, rather than relying solely on theoretical claims of objectivity.</p> <p>Accordingly, when constructing the <emph>Educational Evolution Action Framework</emph>, we must not view technology merely as a neutral tool, but as a value-laden social construct. Future research should explore how to preserve teacher agency in the context of technological integration, reimagine the professional role of teachers in the AI era, and build an "educational technology community" centered on teacher development and learner well-being. Such a community would directly counteract the de-skilling trend fostered by technological optimism. Indeed, recent frontier studies are already beginning to engage with these concerns (Chan & Tsi, [<reflink idref="bib15" id="ref127">15</reflink>]; Zhang & Zhang, [<reflink idref="bib97" id="ref128">97</reflink>]).</p> <hd id="AN0189592952-28">Micro-level practical deviations: from tool to companion, the absence of humanity in technolo...</hd> <p>Within the <emph>Educational Transformation Scenarios Framework</emph>, GenAI is portrayed as a"cognitive-enhancing learning companion"in teaching practices, showing unprecedented potential in areas such as personalized feedback and learning path planning. However, many policy documents still adopt a logic of"instrumental rationality"at this level of technological application, viewing GenAI as a "neutral, emotionless, and precise" assistant. This perspective neglects the emotional interactions, empathetic capacities, and ethical values inherent in educational practice, and lacks a humanistic examination of educational behavior and processes (Chen et al., [<reflink idref="bib17" id="ref129">17</reflink>]).</p> <p>This logic results in a"humanity gap"in human–machine interaction scenarios. Education is not a simple process of information transmission—it encompasses trust, understanding, misunderstanding, repair, and guidance, all of which involve complex psychological and emotional mechanisms. For GenAI to truly become a"learning companion,"it must be capable of emotional recognition, social adaptation, and ethical judgment (Boulus-Rødje et al., [<reflink idref="bib9" id="ref130">9</reflink>]). Developing these capabilities is not just a technical challenge but also a philosophical question about the nature of education. Current policy documents tend to focus excessively on measurable goals like"increasing efficiency"and"optimizing pathways,"which risks reducing education to a mechanistic process of algorithmic control.</p> <p>Although GenAI is depicted as a cognitive enhancer for learners—equipped with simulated reasoning, language comprehension, and generative capabilities, and offering unmatched precision in learning path matching and feedback—the core issue remains: learners are not machines awaiting optimization, and learning is not a linear model of input–output-adjustment. Educational activity is inherently uncertain, affective, and situated in context (Roshanaei, [<reflink idref="bib60" id="ref131">60</reflink>]). Yet in today's policy discourse, the emphasis on the"embeddedness"of algorithmic intelligence in teaching processes seldom addresses the possible"decontextualizing effects"it may cause. In short, while GenAI systems "customize" learning paths for students, they may also unintentionally exclude the legitimacy of diverse cognitive styles, emotional rhythms, and cultural backgrounds, constructing instead an"ideal learner profile under technological aesthetics."</p> <p>This"profile"is often defined by technological standards: high efficiency, logical consistency, and outcome orientation—standing in sharp contrast to real student experiences. Many learners, when interacting with AI systems, do not feel"understood"but rather"categorized"and"standardized."When feedback is algorithm-driven, it often fails to capture the confusion, disappointment, or self-doubt students may experience during learning struggles. Human teachers possess "educational intuition" and "empathetic responses" that are replaced in this process by technical logic, marginalizing students' psychological states and socio-emotional needs. This tendency has received little attention in policy texts (understandably, as most policies pay limited attention to micro-level learning subjects). Many documents equate"human–machine interaction"with"human–machine collaboration,"while overlooking the irreplaceable roles of emotional relationships and trust-building in education.</p> <p>A deeper issue lies in the fact that GenAI systems are not neutral entities; they are human-designed and inevitably carry the value judgments and cultural positions of their creators (Bearman et al., [<reflink idref="bib7" id="ref132">7</reflink>]). When systems guide learner behavior through data-driven processes, they also subtly shape the image of a"predictable learner"—the more predictable the behavior, the more efficient the system's intervention appears. Yet, the precondition for such efficiency is behavioral simplification and context stripping. This not only risks misinterpreting complex learning behaviors but may also erode learners' capacity for reflective agency and control over their own learning. Over time, education could become trapped in a state of algorithmic dependency, losing its social value as a mechanism for cognitive emancipation and personal development (Shailendra et al., [<reflink idref="bib69" id="ref133">69</reflink>]).</p> <p>Therefore, building an AI system in education that truly embodies humanistic care requires a return to an ontological understanding of education—to reexamine what"learning"actually means. As Bayne ([<reflink idref="bib6" id="ref134">6</reflink>]) has pointed out, the future of education should be a shared exploration of meaning and value, not a matter of pre-programmed outcomes and control. In this sense, AI should not remain merely an "optimization tool," but become a participant that understands the other. This requires the establishment of a collaborative governance mechanism among policymakers, educational technology developers, and frontline educators—not only to discuss the feasibility of technology but to engage in joint deliberation on its ethical boundaries, emotional support roles, and responsibilities regarding learner autonomy. Only in this way can we preserve the human element amid rapid technological advancement, and build an AI educational ecosystem that inspires motivation, fosters belonging, and supports individual growth.</p> <hd id="AN0189592952-29">Conclusions, implication and limitation</hd> <p></p> <hd id="AN0189592952-30">Conclusions</hd> <p>In this study, we delve into the profound impacts and potential transformations that GenAI technology imposes on the current education system. By comprehensively analyzing 11 policy reports, guidelines and educational practices across various dimensions, this paper constructs a multidimensional transformation action framework for the education system in the GenAI era, highlighting strategies and directions for the education sector to respond to new technological challenges.</p> <p>Initially, we identify the pivotal role of GenAI technology in transforming educational modalities, which reshapes the types of teaching resources and modes of instructional interaction, and advances the personalization and flexibility of educational modes. The transformation in the roles of teachers and the evolution of student learning modes further underscore the profound significance of GenAI technology's application in education. Furthermore, from meso and micro perspectives, we examine the considerations of educational entities, the evolution of educational media and the transformation of educational content, unveiling the common challenges and unique opportunities the education system faces in the GenAI era.</p> <p>Conclusively, the transformation action framework for the education system in the GenAI era offers a comprehensive understanding and guidance, emphasizing the necessity and urgency of education reform enabled by technology. Looking ahead, the education sector needs to continuously explore pathways for the collaborative development of GenAI technology. It should leverage its advantages in improving efficiency and optimizing educational processes, while also paying attention to the potential risks it may bring, ensuring fairness and quality in education. Through such endeavors, we can anticipate the construction of a more open, inclusive and innovative educational ecosystem in the wave of GenAI.</p> <hd id="AN0189592952-31">Implication</hd> <p>This study's exploration of the interaction between GenAI and the field of education not only highlights its potential for transformation but also seeks to uncover the complexity and contradictions underlying this process. Theoretically, the framework proposed in this paper extends the understanding of GenAI's role in education, moving beyond a view of it as a mere technical application toward a multidimensional perspective of human–AI collaboration. At the same time, we recognize that technology is not a neutral entity—its functions, boundaries, and impacts are deeply shaped by social structures, cultural contexts, and power relations. As Selwyn ([<reflink idref="bib65" id="ref135">65</reflink>]) argues, educational technology research should transcend the surface-level logic of "technology enhancing learning" and instead focus on the situated realities, institutional constraints, and social conflicts that underpin the use of technology in educational settings. Accordingly, we propose viewing GenAI as a dynamic and contested educational actor rather than a predetermined or inherently progressive tool.</p> <p>Moreover, the theoretical challenges posed by GenAI to education must be accompanied by a systematic reflection on its potential risks and broader social implications (Williamson, [<reflink idref="bib90" id="ref136">90</reflink>]). As Selwyn ([<reflink idref="bib68" id="ref137">68</reflink>]) emphasizes, the promotion of AI technologies is often accompanied by the marginalization of critical voices and a tendency to reduce complex political and ethical concerns to mere engineering problems. Therefore, we must be cautious of the techno-optimist "narratives of inevitability" that dominate mainstream discourse and critically reconsider the far-reaching implications of GenAI for teaching, assessment, labor relations, and learners' subjectivity.</p> <p>At the practical level, this study also refrains from endorsing GenAI as an uncritically accepted "silver bullet" for education. While educators are encouraged to explore the potential of GenAI for personalized instruction and intelligent feedback, they must also remain mindful of how overreliance on technology may erode professional judgment, intensify data surveillance, and contribute to algorithmic forms of discipline in the learning process. Policymakers, in formulating AI education strategies, should look beyond deployment efficiency to consider issues such as data ethics, teacher labor protections, educational equity, and student privacy. Especially in highly unequal educational systems, the unreflective adoption of technology risks exacerbating existing disparities rather than bridging them. Technology developers, meanwhile, should resist falling into the trap of "solutionism," and instead ground the design of GenAI tools in deep interdisciplinary dialogues across pedagogy, sociology, and ethics—attuned to the cultural and contextual diversity of education.</p> <p>In conclusion, this study advocates for a more critically informed approach to GenAI in education. We argue that GenAI should not be seen as the inevitable endpoint of educational development, but rather as a medium for ongoing negotiation, reflection, and redesign. In this process, both supporters and critics must work together to co-construct a more inclusive, pluralistic, and politically conscious future for educational technology. We hope that this balanced perspective—marked by both optimism and caution—can provide a richer and more dialectical foundation for future research.</p> <hd id="AN0189592952-32">Limitation and prospect</hd> <p>Although this study provides a comprehensive action framework for the transformation of the education system in the GenAI era, there are still some limitations that may affect the broad applicability and depth of the research. Firstly, the 11 policy reports and educational practice documents included in the study, although covering different dimensions and perspectives, still suffer from a limited sample size. As GenAI technology is still in a developing stage within the education sector, the current available policy documents and reports are limited in number, and most of the literature focuses on a few advanced countries or regions, making it impossible to fully represent the global transformation needs of educational systems. Additionally, the content of these documents may be influenced by publication time, research methodology, and the author's stance, thus having some degree of selectivity and limitation. Future research could expand the corpus to include a more diverse and representative range of global policy voices, especially from the Global South and emerging economies, to enrich the perspective. Moreover, a comparative cross-country analysis might help to surface systemic differences in GenAI integration.</p> <p>Secondly, the study employed qualitative analysis to examine and interpret these documents. Although qualitative analysis allows for a deep exploration of key themes and trends in the literature, it is more subjective and may be influenced by the personal views and understanding of the researcher. While efforts were made to maintain objectivity, due to the diversity of analytical frameworks and interpretations, there may still be different interpretations of the same issue by different scholars. To mitigate this limitation, future work can consider adopting mixed-methods approaches (e.g., combining content analysis with computational text analysis) to triangulate findings and enhance robustness. Additionally, involving multiple coders with intercoder reliability checks could help reduce subjective bias.</p> <p>Furthermore, caution should be exercised when using the themes discovered in policy documents to speculate about how GenAI will change education in the future. While these policies provide guidance for the current transformation of educational systems, they cannot be fully relied upon to accurately predict the long-term impacts of GenAI on future education. Therefore, the conclusions of this study may have some degree of uncertainty. It is also important to acknowledge that policy texts are rhetorical and political artifacts. Their aspirational language may reflect strategic visions rather than grounded reality. A reflexive stance is needed to consider whose voices are represented and whose are omitted. Therefore, although this study proposes a multi-dimensional action framework for the transformation of the educational system, the framework currently remains largely at the level of theoretical construction and policy recommendations, lacking validation and application in real-world educational environments. In particular, the inherent technological optimism of policymakers and advocates should be carefully evaluated and critically reflected upon, in order to identify reasonable paths for integrating technology with education through critique. In addition, future studies could adopt an iterative design-based research approach to test and refine the proposed framework within authentic educational settings (Wang et al., [<reflink idref="bib88" id="ref138">88</reflink>]; Xing et al., [<reflink idref="bib94" id="ref139">94</reflink>]). This would allow for greater contextual responsiveness and enhance the framework's practical utility.</p> <p>The limitation of perspective is indeed a significant constraint of this study; however, it has extensively consulted and incorporated a large body of cutting-edge literature to help mitigate the potential biases inherent in qualitative analysis. Many scholars have expressed concerns about the drawbacks of how technology might disrupt current educational models. For example, the work of Chen et al. ([<reflink idref="bib18" id="ref140">18</reflink>]) synthesizes empirical evidence and forecasts from over a hundred studies regarding the potential risks posed by GenAI technologies. Their follow-up study (Chen et al., [<reflink idref="bib19" id="ref141">19</reflink>]) attempts to construct a human-centered discourse framework to counterbalance the influence of technocentrism. Their aim is to address the challenges posed by GenAI and other emerging technologies to existing educational systems and paradigms, and to promote a vision of convergence between educational and technological values. This vision is broadly aligned with the goals of the current study, though their analytical lens differs. Such perspectives offer valuable insights for understanding the educational transformations and epistemological shifts driven by GenAI.</p> <hd id="AN0189592952-33">Acknowledgements</hd> <p>This research has benefited from the comments and suggestions of the Editor and three anonymous reviewers. Meanwhile, the graphic design of this study also benefited from the support of my girlfriend, Xiaojiao Chen, to whom I would like to express my sincere gratitude.</p> <hd id="AN0189592952-34">Funding</hd> <p>This work was supported by Major Program of National Fund of Philosophy and Social Science of China (19ZDA364).</p> <hd id="AN0189592952-35">Data availability</hd> <p>The data presented in this study are available on request from the first author on reasonable request.</p> <hd id="AN0189592952-36">Declarations</hd> <p></p> <hd id="AN0189592952-37">Competing interests</hd> <p>The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.</p> <hd id="AN0189592952-38">Ethical approval</hd> <p>There are no ethical issues involved in this research.</p> <hd id="AN0189592952-39">Appendix</hd> <p>See Tables 4, 5.</p> <p>Table 4 Policy guidance documents included in the analysis</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Rank</p></th><th align="left"><p>Policy Document</p></th><th align="left"><p>Web Link</p></th></tr></thead><tbody><tr><td align="left"><p>D1</p></td><td align="left"><p><italic>Guidance for generative AI in education and research</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://unesdoc.unesco.org/ark:/48223/pf0000386693" /></p></td></tr><tr><td align="left"><p>D2</p></td><td align="left"><p><italic>Education in the age of artificial intelligence</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://unesdoc.unesco.org/ark:/48223/pf0000387029%5feng" /></p></td></tr><tr><td align="left"><p>D3</p></td><td align="left"><p><italic>Generative AI and the future of education</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://unesdoc.unesco.org/ark:/48223/pf0000385877" /></p></td></tr><tr><td align="left"><p>D4</p></td><td align="left"><p><italic>AI and the Future of Skills, Volume 2</italic></p><p><italic>Methods for Evaluating AI Capabilities</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://www.oecd-ilibrary.org/education/ai-and-the-future-of-skills-volume-2%5fa9fe53cb-en" /></p></td></tr><tr><td align="left"><p>D5</p></td><td align="left"><p><italic>Is Education Losing the Race with Technology?</italic></p><p><italic>AI's Progress in Maths and Reading</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://www.oecd-ilibrary.org/education/is-education-losing-the-race-with-technology%5f73105f99-en" /></p></td></tr><tr><td align="left"><p>D6</p></td><td align="left"><p><italic>Artificial Intelligence and the Future of Teaching and Learning</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://tech.ed.gov/ai-future-of-teaching-and-learning/" /></p></td></tr><tr><td align="left"><p>D7</p></td><td align="left"><p><italic>Generative AI in education Educator and expert views</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://assets.publishing.service.gov.uk/media/65b8cd41b5cb6e000d8bb74e/DfE%5fGenAI%5fin%5feducation%5f-%5fEducator%5fand%5fexpert%5fviews%5freport.pdf" /></p></td></tr><tr><td align="left"><p>D8</p></td><td align="left"><p><italic>2023 EDUCAUSE Horizon Report: Teaching and Learning Edition</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://library.educause.edu/resources/2023/5/2023-educause-horizon-report-teaching-and-learning-edition" /></p></td></tr><tr><td align="left"><p>D9</p></td><td align="left"><p><italic>2023 EDUCAUSE Horizon Action Plan: Generative AI</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://library.educause.edu/resources/2023/9/2023-educause-horizon-action-plan-generative-ai" /></p></td></tr><tr><td align="left"><p>D10</p></td><td align="left"><p><italic>Generative Artificial Intelligence for Education and Pedagogy</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://teaching.cornell.edu/generative-artificial-intelligence/cu-committee-report-generative-artificial-intelligence-education" /></p></td></tr><tr><td align="left"><p>D11</p></td><td align="left"><p><italic>English language education in the era of generative AI: our perspective</italic></p></td><td align="left"><p><ext-link ext-link-type="url" href="https://www.cambridgeenglish.org/Images/685411-english-language-education-in-the-era-of-generative-ai-our-perspective.pdf" /></p></td></tr></tbody></table> </ephtml> </p> <p>Table 5 Background, purpose, and key themes of policy guidance documents</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Rank</p></th><th align="left"><p>Background</p></th><th align="left"><p>Purpose</p></th><th align="left"><p>Key Themes</p></th></tr></thead><tbody><tr><td align="left"><p>D1</p></td><td align="left"><p>In the context of the rapid rise of generative artificial intelligence systems, UNESCO calls on governments to regulate their use in education to ensure that these applications adhere to human-centered principles</p></td><td align="left"><p>Aimed at supporting countries in developing and implementing policies to ensure the responsible and effective use of generative artificial intelligence in education and research</p></td><td align="left"><p>Ethics and social impact, educational equity, capacity building, policy and governance, research and innovation</p></td></tr><tr><td align="left"><p>D2</p></td><td align="left"><p>With the rapid development of artificial intelligence technology, the education sector faces unprecedented opportunities and challenges</p></td><td align="left"><p>Aimed at providing guidance to policymakers to help them harness the opportunities offered by artificial intelligence while addressing associated risks and ensuring fairness and quality in education systems</p></td><td align="left"><p>Personalized learning, administrative efficiency, ethics and privacy, transformation of teacher roles, policy recommendations</p></td></tr><tr><td align="left"><p>D3</p></td><td align="left"><p>With the fast development of generative artificial intelligence (GenAI) technology, the education sector faces unprecedented opportunities and challenges</p></td><td align="left"><p>Aimed at exploring the impact of GenAI on education and providing policy recommendations to help countries effectively respond to this technological transformation</p></td><td align="left"><p>Technology application, ethical considerations, educational equity, teacher roles, student learning, policy-making, global cooperation</p></td></tr><tr><td align="left"><p>D4</p></td><td align="left"><p>As the application of artificial intelligence expands across various sectors of society, understanding its impact becomes increasingly important</p></td><td align="left"><p>Aimed at exploring three different approaches to evaluating artificial intelligence capabilities</p></td><td align="left"><p>Educational testing and assessment, professional certification testing, direct AI assessment</p></td></tr><tr><td align="left"><p>D5</p></td><td align="left"><p>With the rapid advancement of artificial intelligence technology, the education sector faces unprecedented challenges and opportunities</p></td><td align="left"><p>This document aims to provide guidance to national governments and educational institutions to help them develop effective education policies in the age of artificial intelligence</p></td><td align="left"><p>Artificial intelligence, educational policy, skill development, ethics, global collaboration</p></td></tr><tr><td align="left"><p>D6</p></td><td align="left"><p>With the swift development of artificial intelligence technology, the education sector urgently needs to share knowledge, actively engage educators, and improve technology plans and policies to effectively utilize AI</p></td><td align="left"><p>Aimed at helping educators understand the potential of AI technology in education, assess and mitigate key risks, and advance educational goals</p></td><td align="left"><p>Artificial intelligence technology, educator involvement, technology plans and policies, educational goals, risk assessment</p></td></tr><tr><td align="left"><p>D7</p></td><td align="left"><p>The rapid development of generative artificial intelligence (GenAI) technology has sparked widespread attention regarding its application in education</p></td><td align="left"><p>Aimed at evaluating the education sector's response and adoption of GenAI technology</p></td><td align="left"><p>The application and opportunities of GenAI technology, the impact and benefits of GenAI in education, barriers and risks to GenAI adoption, the need for government support in the education sector, and the perspectives of educators and experts</p></td></tr><tr><td align="left"><p>D8</p></td><td align="left"><p>Based on the perspectives and expertise of leaders in the global higher education sector</p></td><td align="left"><p>Exploring the application and opportunities of GenAI in education</p></td><td align="left"><p>Artificial Intelligence, Blended Learning, Digital Learning, Student Experience, etc</p></td></tr><tr><td align="left"><p>D9</p></td><td align="left"><p>Generative artificial intelligence (GenAI) has rapidly become the fastest adopted technology in history, with members of the higher education community, including students and administrators, striving to determine the impact of GenAI tools on life, learning, and work</p></td><td align="left"><p>Aimed at providing insights for higher education professionals to help them understand the factors that may impact teaching and learning now and in the future</p></td><td align="left"><p>The ideal future state of generative artificial intelligence, specific actions for individuals, departments, and cross-institutional collaboration, as well as milestones and action steps for the short, medium, and long term</p></td></tr><tr><td align="left"><p>D10</p></td><td align="left"><p>In the spring of 2023, Cornell University established a committee to assess the feasibility, benefits, and limitations of generative artificial intelligence (GAI) in education and to develop usage guidelines and recommendations</p></td><td align="left"><p>Aimed at depicting the ideal future of GenAI in higher education and proposing practical actions that individuals, departments, and cross-departmental teams can take</p></td><td align="left"><p>Academic integrity, accessibility, use of GAI tools, authorship, access rights, etc</p></td></tr><tr><td align="left"><p>D11</p></td><td align="left"><p>Generative artificial intelligence (GenAI) is rapidly developing, having a profound impact on education, particularly in English language teaching, learning, and assessment</p></td><td align="left"><p>Evaluated the feasibility, benefits, and limitations of using generative artificial intelligence in educational environments and its impact on learning outcomes</p></td><td align="left"><p>Generative artificial intelligence, educational impact, teaching methods, learning assessment, technology applications, etc</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0189592952-40">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0189592952-41"> <title> References </title> <blist> <bibl id="bib1" idref="ref17" type="bt">1</bibl> <bibtext> Adeshola I, Adepoju AP. 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Over the past three years, he has published 25 SSCI Q1 papers as first author or corresponding author, with 8 papers selected as ESI 1% highly cited papers and 5 papers selected as ESI 0.1% hot papers.</p> <p>Yufan Chen Yufan Chen is a postgraduate in Department of Education Information Technology, Faculty of Education, East China Normal University. His research interests include Educational Governance and Development of Educational Standards.</p> <p>Zhebing Hu Zhebing Hu is an undergraduate in College of Foreign Languages, Zhejiang University of Technology. Her research interests include Artificial Intelligence-Assisted Language Learning and Cross-Cultural Education.</p> <p>Yuanyuan Li Yuanyuan Li is a postgraduate in Department of Education Information Technology, Faculty of Education, East China Normal University. His research interests include design and development of Information Technology-Based Teaching Systems, Artificial Intelligence Teaching System Design.</p> <p>Xiaoqing Gu Xiaoqing Gu is the Department Head and a full professor in the Department of Education Information Technology, Faculty of Education, East China Normal University, and the director of Shanghai Engineering Research Center for Digital Education Equipment. Her main research interests include Learning Design, Computer-Supported Collaborative Learning (CSCL), and Learning Analytics.</p> </aug> <nolink nlid="nl1" bibid="bib12" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib18" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib84" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib85" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib80" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib79" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib26" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib22" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib42" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib43" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib44" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib51" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib59" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib47" firstref="ref18"></nolink> <nolink nlid="nl15" 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  Label: Title
  Group: Ti
  Data: The Journey of Challenges and Victories: Exploring the Transformation Action Framework in the GenAI Era from Multifaceted Policies
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chengliang+Wang%22">Chengliang Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2208-3508">0000-0003-2208-3508</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yufan+Chen%22">Yufan Chen</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0002-5568-6399">0009-0002-5568-6399</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zhebing+Hu%22">Zhebing Hu</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0005-7911-3036">0009-0005-7911-3036</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yuanyuan+Li%22">Yuanyuan Li</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0000-8956-3907">0009-0000-8956-3907</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiaoqing+Gu%22">Xiaoqing Gu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-8256-5408">0000-0001-8256-5408</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Technology+Research+and+Development%22"><i>Educational Technology Research and Development</i></searchLink>. 2025 73(5):2951-2993.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 43
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Influence+of+Technology%22">Influence of Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Policy%22">Educational Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+Analysis%22">Policy Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Guidelines%22">Guidelines</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Role%22">Teacher Role</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Methods%22">Educational Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Development%22">Student Development</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11423-025-10535-5
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1042-1629<br />1556-6501
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Generative Artificial Intelligence (GenAI) stands as a cornerstone of the technological revolution, significantly impacting the global educational landscape. This prompts worldwide governments and educational institutions to craft strategic frameworks. This study aims to analyze GenAI's influence on the education system, particularly focusing on transformations in educational paradigms, modalities, pedagogical logics, and educational contexts. It seeks to establish a transformation action framework for the education system in the GenAI era. Utilizing Meta-ethnography, the research synthesizes, analyzes and interprets 11 policy and guideline documents from UNESCO, OECD, ministries of education and universities, which reveal trends towards personalized and interactive educational forms, shifts in the role of the teacher, and updates in student learning modes. The study explores GenAI's integration into education at macro, meso, and micro levels. At the macro level, the framework identifies how GenAI drives a productivity revolution and reshapes human resource demands, alongside societal attitudes and educational actions adapting to this transformation. At the meso level, it reflects on educational pattern and logic shifts, delving into the evolution of educational modalities, entities, media and content. At the micro level, it deconstructs new teaching and learning scenarios in the GenAI era, closely examining the evolution of the role of the teacher and student learning modes, scrutinizing the core value of education as a fundamental human right and constructing a vision for future education in the GenAI era. The findings underscore the need for comprehensive transformation in the education system to adapt to GenAI-driven changes, updating educational content and methods to enhance teaching efficiency and quality as well as fostering holistic student development. These insights offer theoretical and practical guidance for the educational sector to respond to GenAI-driven technological changes, aiming to equip the education system to overcome challenges, seize opportunities and prepare talents needed for the future society.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1497434
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1497434
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      – Type: doi
        Value: 10.1007/s11423-025-10535-5
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 43
        StartPage: 2951
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Influence of Technology
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      – SubjectFull: Educational Policy
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      – SubjectFull: Policy Analysis
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      – SubjectFull: Guidelines
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      – SubjectFull: Educational Trends
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      – SubjectFull: Educational Methods
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      – SubjectFull: Efficiency
        Type: general
      – SubjectFull: Student Development
        Type: general
    Titles:
      – TitleFull: The Journey of Challenges and Victories: Exploring the Transformation Action Framework in the GenAI Era from Multifaceted Policies
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            NameFull: Xiaoqing Gu
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              Y: 2025
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