The Cultural Construction of AI in Learning
A Comparative Analysis of Integration Models
DOI:
https://doi.org/10.3991/ijim.v20i06.60871Keywords:
Artificial Intelligence in Education (AIED); Vocational Education and Training (VET); Sociotechnical Systems; Cross-Cultural Analysis; Educational Policy; Digital EquityAbstract
This study contests deterministic narratives of educational technology adoption, arguing that Artificial Intelligence (AI) integration is a process of cultural construction. It examines how distinct national “cultures of AI use”—defined by core purposes, ethical framings, and equity outcomes—are shaped by societal structures, including pedagogical traditions, economic ideologies, and governance models. This perspective challenges the prevalent techno-utopian discourse by foregrounding the socio-political embeddedness of algorithmic systems in educational settings. Employing critical qualitative comparative analysis (QCA), this study uses a framework synthesizing Cognitive Load Theory (CLT), Dual-Process Theory (DPT), and Sociocultural Theory (SCT). Data triangulation combined a systematic literature review (2020–2025) with critical discourse analysis of policy documents from UNESCO, OECD, and the World Bank, constructing profiles for nine nations. The methodological rigor is enhanced by incorporating quantitative indicators on digital infrastructure to contextualize the qualitative findings within material realities of access and capacity. The analysis identifies three dominant models: (1) the state-coordinated deployment model (e.g., China, UAE), featuring top-down, systemic implementation; (2) the market-driven innovation model (e.g., USA), characterized by a fragmented, entrepreneurial tool ecosystem; and (3) the human-centric augmentation model (e.g., Finland, Singapore), where AI is a subordinate tool to enhance teacher professionalism. Each model presents a unique configuration of technological design, pedagogical application, and underlying social values. This paper offers an interdisciplinary lens, framing AI as a culturally constructed artifact to reveal the ideological underpinnings of adoption pathways. It argues for a shift from decontextualized “best practices” to contextual praxis, providing critical insights for stakeholders. The synthesis of cognitive, psychological, and sociocultural theories provides a novel holistic framework for evaluating the multifaceted impacts of AI in diverse educational ecosystems.
References
[1] Papadakis, S., Vaiopoulou, J., & Sifaki, E. (2022). Attitudes towards the use of educational robotics: Exploring the role of computational thinking and the STEAM approach in teacher professional development. Education and Information Technologies, 27(9), 12613–12637.
[2] Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
[3] Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.
[4] Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
[5] Zhang, L., Basham, J. D., & Yang, S. (2023). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 38, 100489.
[6] Wu, Y., & Zhao, J. (2023). State-led AI education in China: Policy and practice. Computers & Education, 194, 104700.
[7] European Commission. (2022). Ethical guidelines on the use of artificial intelligence and data in teaching and learning for educators. Publications Office of the European Union.
[8] UNESCO. (2023). AI in education: A global perspective on challenges and opportunities for sustainable development. UNESCO Publishing.
[9] OECD. (2023). AI and the future of skills in vocational training: Volume 1. OECD Publishing.
[10] World Bank. (2024). World development report 2024: Digital divides in education. World Bank Group.
[11] Shaikh, S. A. (2024). Bridging the digital skill divide: AI and mobile learning in emerging economies. International Journal of Interactive Mobile Technologies (iJIM), 18(8), 123-135.
[12] Nikou, S. A., & Economides, A. A. (2024). Perceived mobile learning barriers: A longitudinal study. International Journal of Mobile and Blended Learning, 16(2), 1-18.
[13] Al-Harthi, A., & Al-Maskari, A. (2022). Cultural considerations in the adoption of AI in Gulf Cooperation Council education systems. International Journal of Educational Development, 89, 102534.
[14] Straková, N., & Cimermanová, I. (2024). The role of institutional trust in the adoption of educational technology: A comparative study. Computers & Education, 215, 105001.
[15] Papadakis, S. (2020). Tools for evaluating educational apps for young children: A systematic review of the literature. Interactive Technology and Smart Education, 18(1), 18-49.
[16] Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2021). Teaching mathematics with mobile devices and the Realistic Mathematical Education (RME) approach in kindergarten. Advances in Mobile Learning Educational Research, 1(1), 5-18.
[17] Gkamas, A., & Christopoulou, K. (2024). Network architectures for supporting large-scale mobile learning deployments. International Journal of Interactive Mobile Technologies (iJIM), 18(4), 55-70.
[18] Hong, J., & Lee, S. (2023). Enhancing learner engagement in mobile AI tutors through adaptive interface design. *International Journal of Human-Computer Interaction, 39*(15), 3105-3120.
[19] Nikou, S. A. (2023). Factors influencing teachers' acceptance of AI-based tools in primary education. Technology, Pedagogy and Education, 32(3), 345-361.
[20] Shaikh, S. A., & Rajper, S. (2024). Exploring digital equity in AI-enabled education systems of the Global South. International Journal of Educational Technology in Higher Education, 21(1), 25.
[21] Straková, N. (2023). Cognitive and motivational predictors of success in digital learning environments. Journal of Computer Assisted Learning, 39(6), 1892-1905
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