Revisiting Techno-Optimism: Multidimensional Inequalities in AI Education across Urban, Suburban, and Rural Primary Schools in China

Yunlai Liu; Yuwei Zhang

doi:10.25159/1947-9417/18869

Authors

Yunlai Liu Anqing Normal University
Yuwei Zhang Anqing Normal University

DOI:

https://doi.org/10.25159/1947-9417/18869

Keywords:

AI education inequality, urban-rural divide, cultural capital, digital divide, China

Abstract

This study challenges the technologically optimistic narrative that artificial intelligence (AI) naturally promotes educational democratisation by delving into systemic inequalities in AI education across urban, suburban, and rural primary schools in China. Utilising a constructivist grounded theory approach, the research conducted six months of qualitative fieldwork, including in-depth interviews with 15 students, five parents, and five teachers, along with classroom observations at three primary schools located in Shenzhen (urban), Anqing (suburban), and Shangluo (rural). The findings unveil four interconnected layers of inequality: policy-driven infrastructure disparities favouring elite urban schools, regional stratification exacerbating urban-rural divides under standardised policies, intergenerational transmission of cultural and economic capital reinforcing educational privilege, and individual disparities in AI literacy widening the “new digital divide”. These insights resonate with the maximally maintained inequality theory, highlighting how AI education policies may inadvertently perpetuate existing inequities. The study advocates for equity-oriented reforms in AI education, emphasising the necessity to address structural barriers such as unequal resource allocation, inadequate teacher training, and disparities rooted in family backgrounds. By illuminating the socio-technical dynamics of AI integration in education, this research not only contributes to theoretical understandings of educational inequality but also offers practical implications for policymakers and educators striving to achieve more equitable AI education outcomes.

References

Benjamin, R. 2019. Race after Technology. Abolitionist Tools for the New Jim Code. Medford: Polity Press.

Bourdieu, P. 1986. “The Forms of Capital”. In Handbook of Theory and Research for the Sociology of Education, edited by J. G. Richardson, 241–258. New York: Greenwood Press.

Charmaz, K. C. 2006. Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. London: Sage Publications. https://doi.org/10.3402/qhw.v1i3.4932.

Chen, L., L. Wang, and L. Liao. 2021. “Research on the Resource Allocation of Compulsory Education from the Perspective of Efficiency Evaluation: Taking Three Province-Governed Counties in Jiangsu Province as Examples”. [In Chinese.] Journal of University of Electronic Science and Technology of China 23 (2): 86–98.

Dai, Y. 2023. “Negotiation of Epistemological Understandings and Teaching Practices between Primary Teachers and Scientists about Artificial Intelligence in Professional Development”. Research in Science Education 53: 577–591. https://doi.org/10.1007/s11165-022-10072-8.

Du, Q. 2018. “Whose Children Are More Likely to Enter ‘985 Project’ Universities? A Longitudinal Research on the Distribution of High Quality Education Opportunity”. [In Chinese.] Review of Higher Education 6 (5): 54–65.

Duan, H., H. Yan, Y. He, and X. Li. 2021. “Spatiotemporal Analysis of the Achievement of Equitable Quality Basic Education in Gansu Province, Northwest China”. Sustainability, 13 (11): 5862. https://doi.org/10.3390/su13115862.

DiPrete, T. A., and G. M. Eirich. 2006. “Cumulative Advantage as a Mechanism for Inequality: A Review of Theoretical and Empirical Developments”. Annual Review of Sociology 32: 271–297. https://doi.org/10.1146/annurev.soc.32.061604.123127.

Galliano, D., and P. Roux. 2008. “Organisational Motives and Spatial Effects in Internet Adoption and Intensity of Use: Evidence from French Industrial Firms”. The Annals of Regional Science 42: 425–448. https://doi.org/10.1007/s00168-007-0157-z.

Gu, X., R. Li, and S. Li. 2023. “What Is the State of Artificial Intelligence Education? Evidence Response to the Results of AI Education Competition for Primary and Secondary Schools”. [In Chinese.] Open Education Research 29 (6): 82–91. https://doi.org/10.13966/j.cnki.kfjyyj.2023.06.009.

Hällsten, M., and M. Thaning. 2018. “Multiple Dimensions of Social Background and Horizontal Educational Attainment in Sweden”. Research in Social Stratification and Mobility 56: 40–52. https://doi.org/10.1016/j.rssm.2018.06.005.

Holstein, K., and S. Doroudi. 2021. “Equity and Artificial Intelligence in Education: Will ‘AIEd’ Amplify or Alleviate Inequities in Education?” Preprint, ArXiv, April 27. https://doi.org/10.48550/arXiv.2104.12920.

Luo, J., and X. Wang. 2022. “Household Registration Status, Social Integration, and Equitable Distribution: Evidence from Field Experiments with Primary School Students”. [In Chinese.] World Economic Papers 8 (4): 39–56.

Mastekaasa, A., and G. E. Birkelund. 2023. “The Intergenerational Transmission of Social Advantage and Disadvantage: Comprehensive Evidence on the Association of Parents’ and Children’s Educational Attainments, Class, Earnings, and Status”. European Societies 25 (1): 1–21. https://doi.org/10.1080/14616696.2022.2059542.

Ministry of Education of the People’s Republic of China. 2018. “Education Informatization 2.0 Action Plan”. [In Chinese.] Official Website of the Ministry of Education of the People’s Republic of China, April 18. http://www.moe.gov.cn/srcsite/A16/s3342/201804/t20180425_334188.html.

Ministry of Education of the People’s Republic of China. 2024. “List of Artificial Intelligence Education Bases for Primary and Secondary Schools”. [In Chinese.] Official Website of the Ministry of Education of the People’s Republic of China, February 20. http://www.moe.gov.cn/srcsite/A06/s3321/202402/t20240223_1116386.html.

Nemorin, S., A. Vlachidis, H. M. Ayerakwa., and P. Andriotis. 2023. “AI Hyped? A Horizon Scan of Discourse on Artificial Intelligence in Education (AIED) and Development”. Learning, Media and Technology 48 (1): 38–51. https://doi.org/10.1080/17439884.2022.2095568.

OECD.AI. 2022. “The OECD Artificial Intelligence Policy Observatory”. Accessed October 13, 2024. https://oecd.ai/en/countries-and-initiatives.

Opesemowo, O. A., and V. Adekomaya. 2024. “Harnessing Artificial Intelligence for Advancing Sustainable Development Goals in South Africa’s Higher Education System: A Qualitative Study”. International Journal of Learning, Teaching and Educational Research 23 (4): 67–86. https://doi.org/10.26803/ijlter.23.3.4.

Schiff, D. 2022. “Education for AI, Not AI for Education: The Role of Education and Ethics in National AI Policy Strategies”. International Journal of Artificial Intelligence in Education 32 (3): 527–563. https://doi.org/10.1007/s40593-021-00270-2.

State Council of China. 2017. “Development Plan for New-Generation Artificial Intelligence”. Official Website of the Central Government of the People’s Republic of China, July 8. https://www.gov.cn/zhengce/zhengceku/2017-07/20/content_5211996.htm.

Tang, J. 2016. “Lost at the Starting Line: A Reconsideration of Educational Inequality in China (1978–2008)”. The Journal of Chinese Sociology 3: 1–18. https://doi.org/10.1186/s40711-016-0028-z.

Van Deursen, A. J. A. M., and E. J. Helsper. 2015. “The Third-Level Digital Divide: Who Benefits Most from Being Online?” In Communication and Information Technologies Annual, Studies in Media and Communications, Vol. 10, edited by L. Robinson., S. R. Cotten., J. Schulz., T. M. Hale, and A. Williams, 29–52. Leeds: Emerald Group Publishing. https://doi.org/10.1108/S2050-206020150000010002.

Wang, C., S. C. Boerman, A. C. Kroon, J. Möller, and C. de Vreese. 2024. “The Artificial Intelligence Divide: Who Is the Most Vulnerable?” New Media and Society 27 (7): 3867–3889. https://journals.sagepub.com/doi/10.1177/14614448241232345.

Wu, X., and D. J. Treiman. 2004. “The Household Registration System and Social Stratification in China: 1955–1996”. Demography 41 (2): 363–384. https://doi.org/10.1353/dem.2004.0010.

Xu, Q., and J. Gong. 2024. “AI Courses Integrated into Schedules, Campuses Create Intelligent Learning Spaces: These Schools Empower Student Growth with Comprehensive AI Capabilities”. [In Chinese.] Shanghai Observer, September 22. https://web.shobserver.com/wx/detail.do?id=799177.

Zou, W., and Z. H. Ma. 2019. “Family Background, Intergenerational Transmission and Educational Inequality”. [In Chinese.] China Industrial Economics 2: 80–89. https://doi.org/10.19581/j.cnki.ciejournal.2019.02.012.