Reclaiming Indigenous Knowledge Systems in the Age of Technological Modernity: A Critical Analysis of Cultural Westernization and Epistemic Erosion in India
Article Sidebar
Main Article Content
This paper critically examines the relationship between technological modernity and the gradual erosion of Indigenous Knowledge Systems (IKS), with specific reference to the Indian context. It argues that contemporary digital infrastructures—while offering unprecedented global connectivity and operational efficiency—simultaneously accelerate epistemic homogenization, digital colonialism, and cultural Westernization. Drawing on decolonial theory, Science and Technology Studies (STS), and recent scholarship on data sovereignty, the study situates the marginalization of IKS within a broader socio-technical framework. Employing a critical-interpretive methodology that triangulates documentary policy analysis with three India-specific case studies—the National Education Policy (NEP) 2020 and the EdTech sector, generative artificial intelligence (AI) and linguistic marginalization, and Adivasi agricultural-data extraction—the paper demonstrates how digital systems restructure cognition, commodify cultural memory, and displace indigenous epistemologies. The analysis identifies four structural mechanisms of erosion—epistemic standardization, algorithmic bias, data dispossession, and cognitive restructuring—which together produce a condition of epistemic dependency in which interpretive authority shifts from communities to privately owned platforms. Empirical indicators are integrated throughout: India recorded 19,569 raw mother-tongue returns in the 2011 Census yet officially recognizes only 121 languages, while English constitutes roughly 46% of the most recent Common Crawl corpus and over 90% of the training data underpinning large language models. The paper advances two conceptual models—a comparative epistemological paradigm and an epistemic erosion–revitalization framework—and argues for a paradigm of technological sovereignty that critically engages digital advancement while safeguarding pluralistic knowledge ecosystems. The findings carry implications for education policy, AI governance, and indigenous data rights, suggesting that hybrid pedagogies, collective-consent data protocols, and decolonial AI design can convert digital infrastructure from a vector of erosion into an instrument of epistemic revitalization.
Downloads
References
Agrawal, A. (1995). Dismantling the divide between indigenous and scientific knowledge. Development and Change, 26(3), 413–439. https://doi.org/10.1111/j.1467-7660.1995.tb00544.x
Carr, N. (2010). The shallows: What the Internet is doing to our brains. W. W. Norton.
Census of India. (2013). Primary census abstract and language data, Census of India 2011. Office of the Registrar General & Census Commissioner, Ministry of Home Affairs, Government of India. https://censusindia.gov.in
Centre for Economic Data and Analysis. (2025). One nation, many disconnects: Mapping India's home internet gaps. Ashoka University. https://ceda.ashoka.edu.in
Couldry, N., & Mejias, U. A. (2020). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
Ellul, J. (1964). The technological society (J. Wilkinson, Trans.). Vintage Books. (Original work published 1954)
Etxaniz, J., Sainz, O., Perez, N., Aldabe, I., Rigau, G., Agirre, E., Ormazabal, A., Artetxe, M., & Soroa, A. (2024). Latxa: An open language model and evaluation suite for Basque. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (pp. 14952–14972). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.799
GlobalData. (2024). India EdTech market summary, competitive analysis and forecast to 2029. GlobalData Plc. https://www.globaldata.com
Heidegger, M. (1977). The question concerning technology and other essays (W. Lovitt, Trans.). Harper & Row.
Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 6282–6293). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.560
Kemp, S. (2024). Digital 2024: India. DataReportal. https://datareportal.com/reports/digital-2024-india
Lakshmi, S. (2025). Rebranding empire in the age of generative AI. Frontiers in Communication, 10, Article 1604361. https://doi.org/10.3389/fcomm.2025.1604361
Ministry of Education, Government of India. (2020). National Education Policy 2020. https://www.education.gov.in/sites/upload_files/mhrd/files/NEP_Final_English_0.pdf
Ministry of Tribal Affairs, Government of India. (2013). Statistical profile of Scheduled Tribes in India 2013. https://tribal.nic.in
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Ong, W. J. (1982). Orality and literacy: The technologizing of the word. Routledge.
Postman, N. (1992). Technopoly: The surrender of culture to technology. Vintage Books.
Qadri, M., Gillespie, T., & Peters, J. (2024). Digital marginalisation, AI bias, and cultural erasure in South Asia. Journal of Underrepresented & Minority Progress. https://ojed.org/jump
RAYSolute. (2026). Indian EdTech market 2026: BYJU'S collapse, Physics Wallah's rise, and the 2035 forecast [Industry report]. RAYSolute Consultants. https://www.raysolute.com/indian-edtech-analysis-2026.html
Santos, B. de S. (2014). Epistemologies of the South: Justice against epistemicide. Routledge.
Suhaiu, S. (2025, September 3). Digital colonialism: How Big Tech marginalizes indigenous rights. Kautilya Society, NUSRL Blog. https://kautilyasocietynusrl.in/2025/09/03/digital-colonialism-how-big-techs-marginalize-indigenous-rights/
The India Forum. (2021, December 19). What the census obscures about India's linguistic diversity. https://www.theindiaforum.in/article/what-census-obscures
Tilde.ai. (2025, November 27). The LLM data dilemma: Why data quality matters for multilingual models. https://tilde.ai/blog/the-llm-data-dilemma/

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.