Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği

Yasemin Yeşilbaş Özenç

doi:10.17679/inuefd.1582553

TR EN

Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği

Öz

Bu çalışma, Çin ve Japonya’nın eğitimde yapay zeka politikalarını karşılaştırmalı bir bakış açısıyla ele alarak, iki ülkenin stratejik yaklaşımlarını değerlendirmektedir. Eğitim politikalarının ideolojik temelleri ve uygulanabilirliği, her iki ülkenin eğitim sistemlerinde yapay zekanın rolünü belirlemektedir. Çin ve Japonya’nın eğitimde yapay zeka politikalarının karşılaştırılması, bu iki ülkenin küresel eğitim stratejilerini şekillendiren önemli örnekleri ortaya koymaktadır. Bu araştırma sistematik derleme deseninde literatür taraması türünde nitel bir araştırmadır. Çalışmada, her iki ülkenin yapay zeka uygulamaları incelenerek stratejik yaklaşımlardaki benzerlikler ve farklılıklar analiz edilmiştir. Çin, hızlı ve merkeziyetçi bir dijital dönüşüm süreci izleyerek, yapay zeka tabanlı öğretim araçları ve öğrenci izleme sistemleri gibi uygulamalarla yükseköğretimde önemli ilerlemeler kaydetmiştir. Japonya ise insan merkezli bir yaklaşım benimseyerek, yapay zekayı toplumsal fayda sağlamak ve eğitimde kişiselleştirilmiş öğrenme deneyimleri sunmak amacıyla kullanmaktadır. Çin, hızla gelişen yapay zeka ekosistemini eğitim politikalarına entegre ederken, Japonya etik ve sosyal uyum ilkesine dayanarak daha sürdürülebilir bir yol izlemektedir. Her iki ülke de eğitimde dijitalleşmeyi artırarak küresel rekabet gücünü artırmayı hedeflese de Çin’in rekabetçi yaklaşımı ve Japonya’nın insan odaklı stratejileri arasındaki farklar, kültürel ve stratejik temelleri yansıtmaktadır. Çin ve Japonya’nın deneyimleri, dijital eğitim dönüşümü için rehber niteliğindedir ve dünya genelinde yapay zekanın eğitim sistemlerine entegre edilmesi sürecinde yol gösterici olabilecek önemli bulgular sunmaktadır.

Anahtar Kelimeler

Yapay zeka , yapay zeka politikaları , eğitim politikaları , Çin , Japonya

Comparative Analysis of Artificial Intelligence Policies in Education: The Case of China and Japan

Abstract

This study takes a comparative perspective on China and Japan’s AI in education policies and evaluates their strategic approaches. The ideological underpinnings and feasibility of their educational policies determine the role of AI in the educational systems of both countries. The comparison of China and Japan’s AI in education policies reveals important examples that shape the global education strategies of these two countries. This research is a qualitative study in the form of a literature review in a systematic review design. The study analyzes the similarities and differences in strategic approaches by examining the artificial intelligence practices of both countries. China has made significant progress in higher education by following a rapid and centralized digital transformation process, with applications such as AI-based teaching tools and student monitoring systems. Japan, on the other hand, adopts a human-centered approach and uses AI to provide social benefits and personalized learning experiences in education. While China is integrating the rapidly developing AI ecosystem into its education policies, Japan is pursuing a more sustainable path based on the principle of ethics and social harmony. While both countries aim to increase global competitiveness by increasing digitalization in education, the differences between China’s competitive approach and Japan’s human-centered strategies reflect cultural and strategic underpinnings. The experiences of China and Japan provide guidance for digital education transformation and offer important findings that can guide the process of integrating AI into education systems around the world.

Keywords

Artificial intelligence , artificial intelligence policies , education policies , China , Japan

References

Ahmad, S., Rahmat, M., Mubarik, M., Alam, M., & Hyder, S. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. https://doi.org/10.3390/su132212902.
Applied Technology Review (2023). The future of robotics education in Japan. https://www.appliedtechnologyreview.com/news/the-future-of-robotics-education-in-japan-nwid-1096.html Retrieved 07.11.2024.
Auliawan, A., & Ong, S. (2020). The usage of AI Robot in English language teaching for city revitalization case study: Toda Daini elementary school, Toda City, Saitama, Japan. IOP Conference Series: Earth and Environmental Science, 436. https://doi.org/10.1088/1755-1315/436/1/012022.
Borrego, M., Foster, M., & Froyd, J. (2014). Systematic literature reviews in engineering education and other developing interdisciplinary fields. Journal of Engineering Education, 103(1), 45-76. https://doi.org/10.1002/jee.20038.
Burton, E. (2024). New school guidelines in Japan emphasize AI education, the AI track. https://theaitrack.com/school-guidelines-in-japan-ai-education/ Retrieved 06.11.2024.
Cabinet Office, Government of Japan. (2018). Society 5.0. https://www8.cao.go.jp/cstp/english/society5_0/index.html Retrieved 10.11.2024
Cabinet Office, Government of Japan. (2019). AI strategy 2019. https://www8.cao.go.jp/cstp/english/aistrategy2019.html Retrieved 10.11.2024
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510.
Clarke, J. (2011). What is a systematic review? Evidence Based Nursing, 14(3), 64- 64. https://doi.org/10.1136/ebn.2011.0049.
Council for Science, Technology and Innovation. (2011). 4th basic policy on science and technology. Cabinet Office, Government of Japan. https://www8.cao.go.jp/cstp/english/basic/4th-BasicPolicy.pdf Retrieved 06.11.2024.

Council for Science, Technology and Innovation (2022). Secretariat of science, technology and innovation policy, cabinet office, government of Japan, AI Strategy 2022. https://www8.cao.go.jp/cstp/ai/senryaku/10kai/sanko1.pdf Retrieved 06.11.2024
Ecommerce to China. (2024). The role of AI in transforming Chinese education and learning. https://ecommercetochina.com/the-role-of-ai-in-transforming-chinese-education-and-learning/ Retrieved 03.11.2024.
Edutechtalks (2024). Digital transformation of Japanese education and “GIGA” school initiative. https://edutechtalks.com/digital-transformation-of-japanese-education-and-giga-school-initiative/ Retrieved 06.11.2024.
Filgueiras, F. (2023). Artificial intelligence and education governance. Education, Citizenship and Social Justice, 19(3). https://doi.org/10.1177/17461979231160674.
Fu, X., Krishna, K., & Sabitha, R. (2021). Artificial intelligence applications with e-learning system for China’s higher education platform. Journal of Interconnection Networks, 22, 2143016:19. https://doi.org/10.1142/s0219265921430167.
Gallagher, J. (1984). Policy analyses and program implementation (P.L. 94-142). Topics in Early Childhood Special Education, 4, 43-53. https://doi.org/10.1177/027112148400400107.
Gong, X., Wu, Y., Ye, Z. & Liu, X. (2018). Artificial intelligence course design: iSTREAM-based visual cognitive smart vehicles. 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 2018, 1731-1735. https://doi.org/10.1109/IVS.2018.8500457
Guo, Y. (2021). Artificial Intelligence and Education: A comparative analysis of relevant national policies between China and Japan. 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE), 106-109. https://doi.org/10.1109/ICAIE53562.2021.00030.
Han, X., Hu, F., Xiong, G., Liu, X., Gong, X., Niu, X., Shi, W., & Wang, X. (2018). Design of AI + curriculum for primary and secondary schools in Qingdao. 2018 Chinese Automation Congress (CAC), 4135-4140. https://doi.org/10.1109/CAC.2018.8623310.
Han, J., & Wang, Z. (2010). Capability building in educational technology for teachers in China. British Journal of Educational Technology, 41, 607-611. https://doi.org/10.1111/j.1467-8535.2010.01089.x.
He, J. (2017). The Next Generation AI Development Development Plan — What’s inside? https://medium.com/@jiahe/the-next-generation-ai-development-plan-whats-inside-72824a9bcc3 Retrieved 03.11.2024.
He, J. (2021). An Exploratory study on the application of artificial intelligence technology in the teaching of Japanese language in university. 2021 2nd International Conference on Information Science and Education (ICISE-IE), 1454-1457. https://doi.org/10.1109/ICISE-IE53922.2021.00324.
Hiraki, E., Ishihara, M., & Umetani, K. (2022). Differences in visibility of students’ proficiency by grading methods in energy electronics-related lectures based on DX format. 2022 IEEE 9th International Conference on e-Learning in Industrial Electronics (ICELIE), 1-4. https://doi.org/10.1109/ICELIE55228.2022.9969423.
Jetro (2024). Japan external trade organization. A nation’s drive towards a data-first digital society future. https://www.jetro.go.jp/en/invest/insights/japan-insight/nation-drive-datafirst-digital-society-future.html Retrieved 06.11.2024.
Khanal, S., Zhang, H., & Taeihagh, A. (2024). Development of new generation of artificial intelligence in China: When Beijing’s global ambitions meet local realities. Journal of Contemporary China, 1–24. https://doi.org/10.1080/10670564.2024.2333492
Kikuchi, K., & Browne, C. (2009). English educational policy for high schools in Japan. RELC Journal, 40, 172-191. https://doi.org/10.1177/0033688209105865.
Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 3(45), 298-311. https://doi.org/10.1080/17439884.2020.1754236.
Knox, J. (2021). How the ‘taming’ of private education in China is impacting AI. On Education. Journal for Research and Debate, 4(12). https://doi.org/10.17899/on_ed.2021.12.6. Leading DX School, 2024. Leading DX school: Generative AI pilot school. https://leadingdxschool.mext.go.jp/r05/ai_school/ Retrieved 06.11.2024
Lee, S., Kim, S., Joo, Y., & Kim, H. (2023). Exploring policy trends and implications for supporting Chinese teachers’ AI and digital competencies. Educational Research Institute. https://doi.org/10.34245/jed.43.2.429.
Likova-Arsenova, V. (2020). Artificial intelligence in education. Педагогически форум, 3, 49-56. https://doi.org/10.15547/PF.2020.022.
Liu, X. M. (2022). Nurturing the next-generation AI workforce: A snapshot of AI education in China’s public education system. Asia Pacific Foundation of Canada. https://www.asiapacific.ca/publication/nurturing-next-generation-ai-workforce-snapshot-ai-education Retrieved 01.11.2024.
Meng, Y. (2021). Research on AI education for primary and secondary schools in China, 2, 44-51. https://doi.org/10.23977/APPEP.2021.020109.
MEXT (2024b). White paper on science, technology, and innovation 2024. https://www.mext.go.jp/en/publication/whitepaper/title03/detail03/mext_00007.html Erişim Tarihi: 06.11.2024.
MEXT (2023). White paper on science, technology, and innovation 2023. Japan Ministry of Education, Culture, Sports, Science and Technology. https://www.mext.go.jp Retrieved 06.11.2024.
Ministry of Education, Culture, Sports, Science and Technology (MEXT) (2024a). https://www.mext.go.jp/ Retrieved 06.11.2024
Ministry of Education of the People’s Republic of China (MOEPRC), 2018. http://en.moe.gov.cn/ Retrieved 01.11.2024.
Ministry of Foreign Affairs (2024). Shanghai declaration on global AI governance. https://www.mfa.gov.cn/eng/xw/zyxw/202407/t20240704_11448351.html Retrieved 01.11.2024.
Ministry of Science and Technology of China. (2017). Next generation artificial intelligence development plan. Retrieved from http://www.most.gov.cn
Nevezhin, V. (2021). Application of artificial intelligence systems in education in China and Japan. Profession-Oriented School, 9, 41-49. https://doi.org/10.12737/1998-0744-2021-9-1-41-49.
OECD. (2021). AI and the future of skills and knowledge. OECD iLibrary. https://www.oecd-ilibrary.org Retrieved 10.11.2024.
OECD AI (2024). AI in Japan. https://oecd.ai/en/dashboards/countries/Japan Retrieved 06.11.2024.
Park, Y. (2023). The Introduction and implications of teacher AI education policy in China. The Association of Korea Counseling Psychology Education Welfare. https://doi.org/10.20496/cpew.2023.10.2.111.
Pollock, A., & Berge, E. (2018). How to do a systematic review. International Journal of Stroke, 13(2), 138-156. https://doi.org/10.1177/1747493017743796.
Prime Minister’s Office of Japan (2023). AI strategy council. https://japan.kantei.go.jp/101_kishida/actions/202312/21ai.html Retrieved 06.11.2024.
Purnomo, Y., & Herwin, (Eds.). (2021). Educational innovation in society 5.0 era: Challenges and opportunities: Proceedings of the 4th International Conference on Current Issues in Education (ICCIE 2020), Yogyakarta, Indonesia, 3 - 4 October 2020 (1st ed.). Routledge. https://doi.org/10.1201/9781003206019
RIKEN (2024). RIKEN Center for Advanced Intelligence Project (AIP). https://www.riken.jp/en/research/labs/aip/ Retrieved 06.11.2024.
Russell, S. J. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
Sato, Y. (2021). AI development and higher education reform in Japan. Asian Journal of Technological Education, 9(1), 30-48. https://doi.org/10.1177/1355807810341121
Siddaway, A., Wood, A., & Hedges, L. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual review of psychology, 70, 747-770. https://doi.org/10.1146/annurev-psych-010418-102803.
Song, J., Zhang, L., Yu, J., Peng, Y., Ma, A., & Lu, Y. (2022). Paving the way for novices: How to teach AI for K-12 education in China. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12852-12857. https://doi.org/10.1609/aaai.v36i11.21565
Takahashi, M., & Shibata, K. (2023). Implementing digital literacy in Japanese education: Enhancing student engagement through AI integration. International Review of Education Policy, 40(2), 120-137. https://doi.org/10.1080/13669877.2023.1890128
Tamai, T., Murai, K., Ohnishi, Y., & Kawada, K. (2021). Proposal of parameter tuning skills evaluation by using sensor car behavior data in technology education. J. Robotics Netw. Artif. Life, 8, 104-107. https://doi.org/10.2991/jrnal.k.210713.007.
Tanveer, M., Hassan, S., & Bhaumik, A. (2020). Academic policy regarding sustainability and artificial intelligence (AI). Sustainability, 12(22), 9435. https://doi.org/10.3390/su12229435.
Tazume, H. & Morita, T. (2021). Use of AI robots in ECEC: Young children’s literacy and cognition towards interactive AI robots. https://www.childresearch.net/projects/ecec/2021_06.html Retrieved 07.11.2024.
Tsukazaki, K., Shintoku, T., & Fukuzoe, T. (2019). Teaching ICT skills to children and the empowerment of female college students in STEM in Japan. IOP Conference Series: Materials Science and Engineering, 551. https://doi.org/10.1088/1757-899X/551/1/012036.
The Japan Times, 2021. Japan’s GIGA school program equips students for digital society. https://www.japantimes.co.jp/2021/03/22/special-supplements/japans-giga-school-program-equips-students-digital-society/ Retrieved 06.11.2024.
The State Council The People’s Republic of China (PRC State Council), 2024. https://english.www.gov.cn/ Retrieved 01.11.2024.
UNESCO (2023). Smart Education Platform of China: Laureate of UNESCO Prize for ICT in Education. https://www.unesco.org/en/articles/smart-education-platform-china-laureate-unesco-prize-ict-education Retrieved 07.11.2024.
Vykhodets, R. (2022). China’s AI Strategy. Eurasıan Integratıon: Economics, law, politics, 16(2), 140-147. https://doi.org/10.22394/2073-2929-2022-02-140-147.
Wahlström, N., Alvunger, D., & Wermke, W. (2018). Living in an era of comparisons: Comparative research on policy, curriculum and teaching. Journal of Curriculum Studies, 50(5), 587- 594. https://doi.org/10.1080/00220272.2018.1502814.
Wu, F., He, Q., & Wu, C. (2021). AI+X micro-program fosters interdisciplinary skills in China. Communications of the ACM, 64, 52-54. https://doi.org/10.1145/3481612.
Xia, L., & Zheng, G. (2020). To meet the trend of AI: The ecology of developing AI talents for pre-service teachers in China. The international journal of learning, 186-190. https://doi.org/10.18178/ijlt.6.3.186-190.
Xu-qing, X. (2019). Strengthening top-down design, fortifying categorized management, and promoting healthy development—A few reflections on discussions of the “regulations for the implementation of the Law of the People’s Republic of China on the promotion of private education (Revised) (Draft for Review)”. Chinese Education & Society, 52, 11-21. https://doi.org/10.1080/10611932.2019.1606615.
Yamakawa, R. (2022). Japan’s society 5.0 and AI education initiatives in schools: A pathway to personalized learning. Journal of AI in Education Research, 15(3), 45-62. https://doi.org/10.1016/j.aier.2022.03.015
Yan (闫守轩), S., & Yang (杨运), Y. (2021). Education Informatization 2.0 in China: Motivation, framework, and vision. ECNU Review of Education, 4(2), 410-428. https://doi.org/10.1177/2096531120944929
Yang, X. (2019). Accelerated move for AI education in China. ECNU Review of Education, 2, 347 - 352. https://doi.org/10.1177/2096531119878590.
Yuan, L. (2023). Where does AI-driven education, in the Chinese context and beyond, go next? International Journal of Artificial Intelligence in Education, 34(2). http://dx.doi.org/10.1007/s40593-023-00341-6
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 39. https://doi.org/10.1186/s41239-019-0171-0.

Details

Primary Language

Turkish

Subjects

Educational Administration, Supervision, Planning and Economics (Other) , Education Policy

Journal Section

Review

Authors

Yasemin Yeşilbaş Özenç ^*
0000-0002-5590-4520
Türkiye

Publication Date

September 2, 2025

Submission Date

November 10, 2024

Acceptance Date

March 5, 2025

Published in Issue

Year 2025 Volume: 26 Number: 2

DOI

https://doi.org/10.17679/inuefd.1582553

IZ

https://izlik.org/JA33EG69DY

APA

Yeşilbaş Özenç, Y. (2025). Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği. İnönü University Journal of the Faculty of Education, 26(2), 674-713. https://doi.org/10.17679/inuefd.1582553

AMA

1.Yeşilbaş Özenç Y. Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği. INUJFE. 2025;26(2):674-713. doi:10.17679/inuefd.1582553

Chicago

Yeşilbaş Özenç, Yasemin. 2025. “Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin Ve Japonya Örneği”. İnönü University Journal of the Faculty of Education 26 (2): 674-713. https://doi.org/10.17679/inuefd.1582553.

EndNote

Yeşilbaş Özenç Y (September 1, 2025) Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği. İnönü University Journal of the Faculty of Education 26 2 674–713.

IEEE

[1]Y. Yeşilbaş Özenç, “Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği”, INUJFE, vol. 26, no. 2, pp. 674–713, Sept. 2025, doi: 10.17679/inuefd.1582553.

ISNAD

Yeşilbaş Özenç, Yasemin. “Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin Ve Japonya Örneği”. İnönü University Journal of the Faculty of Education 26/2 (September 1, 2025): 674-713. https://doi.org/10.17679/inuefd.1582553.

JAMA

1.Yeşilbaş Özenç Y. Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği. INUJFE. 2025;26:674–713.

MLA

Yeşilbaş Özenç, Yasemin. “Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin Ve Japonya Örneği”. İnönü University Journal of the Faculty of Education, vol. 26, no. 2, Sept. 2025, pp. 674-13, doi:10.17679/inuefd.1582553.

Vancouver

1.Yasemin Yeşilbaş Özenç. Eğitimde Yapay Zeka Politikalarının Karşılaştırmalı Analizi: Çin ve Japonya Örneği. INUJFE. 2025 Sep. 1;26(2):674-713. doi:10.17679/inuefd.1582553

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