The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Artificial intelligence and education in China
Open accessAbstract
This paper examines the political economy of artificial intelligence (AI) and education in China, through an analysis of government policy and private sector enterprise. While media and policy discourse often portray China’s AI development in terms of a unified national strategy, and a burgeoning geopolitical contestation for future global dominance, this analysis will suggest a more nuanced internal complexity, involving differing regional networks and international corporate activity. The first section considers two key policy documents published by the central Chinese government, which are shown to implicate educational institutions as influential actors in national and regional strategies for AI development, with a significant role in plans to train domestic expertise. The second section outlines three prominent private education companies: New Oriental Group, Tomorrow Advancing Life (TAL), and Squirrel AI. These companies are selected to represent important aspects of China’s development of educational AI applications, including the influence of a well-established private education sector, and a growing interest in international corporate activity. The paper concludes with the suggestion that while central government policy reserves a significant role for education in the national AI strategy, the private sector is utilising favourable political conditions to rapidly develop educational applications and markets.
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“When it comes to higher education, reports often emphasize the government's leading role in AI governance, highlighting the deep cooperation between universities and industry, and the teaching reform and scientific research innovation brought about by the application of technology (Knox, 2020).”
From A Comparative Analysis of AI Privacy Concerns in Higher Education: News Coverage in China and Western Countries (2025) · cites this paper as Knox, 2020
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“In other words, China's secondary schools are starting to experiment with the teaching of AI in response to government policy [3].”
From An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence (2020) · cites this paper as [3]
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Cites
Agonistics: Thinking the World Politically
Mouffe, C. (2013) · Medical Entomology and Zoology · Cited by 1,978
AI super-powers: China, Silicon Valley and the new world order
Klingler‐Vidra, R. (2019) · International Affairs · doi:10.1093/ia/iiz011 · Cited by 618
AI Superpowers: China, Silicon Valley, and the New World Order
Lee, K.-F. (2018) · Virtual Defense Library (Ministerio de Defensa) · Cited by 359
Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics
Crawford, K. (2015) · Science Technology & Human Values · doi:10.1177/0162243915589635 · Cited by 348
Education in a Digital World: Global Perspectives on Technology and Education
Selwyn, N. (2012) · Cited by 157
Qiu, J. L. (2016) · University of Illinois Press eBooks · doi:10.5406/illinois/9780252040627.001.0001 · Cited by 99
Tertiary Education at a Glance: China
Yu, K., Chen, H., Liu, L., & Stith, A. L. (2012) · SensePublishers eBooks · doi:10.1007/978-94-6091-746-2 · Cited by 59
Mirrlees, T., & Alvi, S. (2019) · Routledge eBooks · doi:10.4324/9780429343940 · Cited by 48
Goodbye iSlave: A Manifesto for Digital Abolition
Qiu, J. L. (2016) · Project Muse (Johns Hopkins University) · Cited by 43
Education in a Digital World: Global Perspectives on Technology and Education
Bullen, M. (2013) · Journal of Learning for Development · doi:10.56059/jl4d.v1i1.28 · Cited by 38
Research works printed but not linked: 12
- Barton D., 2017, Artificial Intelligence: Implications for China
- Chu B., 2013, Chinese Whispers: Why Everything You’ve Heard About China is Wrong
- Larsen B. C., 2019, AI Policy and China: Realities of State-Led Development, 16
- Laskai L., 2019, AI Policy and China: Realities of State-Led Development, 3
- Lee K.-F., 2017, China’s Artificial Intelligence Revolution: Understanding Beijing’s Structural Advantages
- Lehmann T., 2019, AI Policy and China: Realities of State-Led Development, 21
- Mubayi P., 2017, China's Rise in Artificial Intelligence: The New New China
- Nelson M., 2019, AI Policy and China: Realities of State-Led Development, 9
- Prakash A., 2018, Go.AI (Geopolitics of Artificial Intelligence)
- Squirrel AI Learning. 2019. “Squirrel AI Learning Attended IEEE ICDM 2018 to Show the Charm of AI + Education.” Cision PR Newswire . Accessed 11 July, 2019. https://www.prnewswire.com/news-releases/squirrel-ai-learning-attended-ieee-icdm-2018-to-show-the-charm-of-ai-education-300776050.html.
- Wang J., 2018, Paper presented in the graduate student Workshop at 2018 Annual Meeting of the society for the history of technology (SHOT)
- Zhang X., 2018, Science & Technology Review, 36, 55
Cited by
Sorted by citations
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364
“Learning about AI has also begun to be part of school curriculum [5, 6].”
Williamson, B., Eynon, R., & Potter, J. (2020) · Learning, Media and Technology · doi:10.1080/17439884.2020.1761641 · Cited by 1,121
State of the art and practice in AI in education
Holmes, W., & Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12533 · Cited by 1,093
Song, C., & Song, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1260843 · Cited by 717
Wei, L. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1261955 · Cited by 536
Metadata
- Authors
- Knox, J.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Knox, Jeremy
- ORCID
- Knox, Jeremy
- Venue
- Learning, Media and Technology
- Publisher
- Informa UK Limited
- Published
- 2020-04-16
- Volume
- 45
- Issue
- 3
- Pages
- 298-311
- ISSN
- 1743-9884 (ISSN-L)
- 1743-9892
- OpenAlex
- W3017157646
- Cited by
- 489
- References
- 16
- Citations by year
- 2026: 140 · 2025: 126 · 2024: 96 · 2023: 59 · 2022: 34 · 2021: 25 · 2020: 9
- FWCI
- 29.88
- Type
- journal article
- Language
- en
- Open access
- Licence
- public-domain
- Locations
- Learning Media and Technology (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, public-domain)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Political Science and International Relations › Global Educational Policies and Reforms
- Social Sciences › Education › Digital Education and Society
- Keywords
- artificial intelligence in education, China, political economy of AI, AI policy, educational technology, state-private sector relations
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