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Reclaiming Indigenous Knowledge Systems in the Age of

Technological Modernity: A Critical Analysis of Cultural

Westernization and Epistemic Erosion in India

(Prof) Dr. Mandeep Kaur Kochar¹*, Sahibpreet Singh Kochar², Prashant Dixit³

¹Vice Principal, Bombay Teachers' Training College, Mumbai, India

²Education Consultant for Emerging Technologies, The Kalgidhar Society, India

³Department of Science, The Kalgidhar Society, India

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150600203

Received: 08 July 2026; Accepted: 13 July 2026; Published: 22 July 2026

ABSTRACT

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.

Keywords: ndigenous knowledge systems; digital colonialism; epistemic erosion; generative AI; NEP 2020

INTRODUCTION

The rapid expansion of global digital infrastructures has fundamentally reconfigured human cognition, social

organization, and the paradigms through which knowledge is produced and legitimized. Although technological

advancement is frequently framed through a lens of neutrality or universal progress, a substantial body of

decolonial and Science and Technology Studies (STS) scholarship demonstrates that these systems carry deeply

embedded cultural assumptions that are predominantly Western, industrial, and reductionist [5, 17]. In

postcolonial and rapidly developing societies such as India, the uncritical adoption of such technologies has

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produced a subtle yet profound displacement of Indigenous Knowledge Systems (IKS)—frameworks that

historically provided holistic, context-sensitive lenses for interpreting reality [1, 20].

The scale of India's digital transition lends urgency to this inquiry. At the start of 2024, India had approximately

751.5 million internet users, equivalent to an internet-penetration rate of 52.4% of the total population, meaning

that roughly 683.7 million people remained offline [11]. This uneven and accelerating diffusion of digital

infrastructure does not occur on neutral terrain; it interacts with one of the most linguistically and epistemically

diverse societies on earth. Recent advances, particularly in generative AI and global data-extraction networks,

have intensified this dynamic, giving rise to what contemporary scholars describe as “digital colonialism” [5,

21].

This paper contends that technological modernity does not merely coexist alongside indigenous traditions; rather,

it actively restructures, absorbs, or marginalizes them. Three research questions guide the analysis: (a) Through

what structural mechanisms do contemporary digital systems erode Indigenous Knowledge Systems in India?

(b) How do specific Indian policy and technological developments—NEP 2020, generative AI, and agricultural

data platforms—manifest these mechanisms empirically? (c) What design and governance interventions could

enable a transition from epistemic erosion toward epistemic revitalization? By integrating decolonial theory with

empirical indicators and India-specific case studies, the study offers a structured framework for understanding

and resisting epistemic erosion while preserving the analytic value of critical technology scholarship.

METHODOLOGY

This study adopts a qualitative, critical-interpretive design situated within decolonial theory and STS. Rather

than testing a hypothesis through primary data collection, it synthesizes secondary evidence to build and illustrate

a conceptual framework, a strategy appropriate for theory-oriented work on socio-technical change [20]. The

analysis proceeds in three stages. First, a documentary analysis of policy texts and scholarly literature establishes

the theoretical scaffolding, drawing on foundational works in decolonial epistemology, philosophy of

technology, and algorithmic-bias research. Second, the study triangulates this scaffolding against three

purposively selected India-specific case studies—education policy and EdTech, generative AI and language, and

indigenous agricultural data—chosen because each illuminates a distinct mechanism of epistemic erosion while

sharing a common national context. Third, publicly available quantitative indicators from governmental,

intergovernmental, and peer-reviewed sources (e.g., the Census of India, national survey data, and

computational-linguistics corpora) are integrated to ground the interpretive claims in measurable trends.

Sources were selected according to relevance, recency, and credibility, prioritizing peer-reviewed publications,

official statistical releases, and reputable industry analyses. The approach is interpretive rather than predictive;

its aim is conceptual clarification and the generation of testable propositions rather than statistical generalization.

Limitations arising from this design are addressed explicitly in the penultimate section.

Conceptualizing Indigenous Knowledge Systems

Indigenous Knowledge Systems refer to the cumulative, context-specific, and dynamic bodies of knowledge

embedded within cultural traditions, localized practices, and ecological interactions [1, 20]. In the Indian context,

IKS is not a monolith but a pluralistic amalgamation of traditions. Its significance is partly demographic: the

2011 Census enumerated more than 104.2 million people belonging to Scheduled Tribes—approximately 8.6%

of the national population—distributed across some 705 notified ethnic groups, the overwhelming majority of

whom reside in rural and forest regions and remain custodians of distinct ecological and cultural knowledge [3,

14]. Indian IKS encompasses several interrelated domains:

• Traditional Ecological Knowledge (TEK): agricultural practices, biodiversity conservation, and

localized climate-resilience strategies, including those of Adivasi communities.

• Health and wellness systems: Ayurveda, Siddha, and localized ethnobotanical healing practices.

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• Cognitive and scientific frameworks: Vedic mathematics, ancient metallurgical techniques, and

astronomical observation.

• Cultural memory: oral traditions, folklore, and community-based governance models.

Unlike Cartesian dualism—which separates mind from matter and humanity from nature—Indian IKS is

characterized by a holistic epistemology, intergenerational transmission, and the integration of ethics,

spirituality, and practical utility [20]. To formalize this divergence, Table 1 outlines the fundamental

epistemological differences between IKS and technological modernity.

Table 1. Comparative Epistemological Paradigm: Indigenous Knowledge Systems and Technological

Modernity

Dimension

Indigenous Knowledge Systems (IKS)

Technological Modernity

Epistemology

Holistic, relational, and context-dependent

Reductionist, abstract, and standardized

Transmission

Oral traditions, experiential learning, and

intergenerational storytelling

Codification, algorithmic indexing, and

digital databases

View of nature

Synergistic and sacred; humans embedded

within the ecosystem

Resource-centric; nature as quantifiable,

extractable data

Locus of power

Decentralized, community-held, and

ethically bound

Centralized (Big Tech), privatized, and

market-driven

Value metric

Wisdom, sustainability, and communal

harmony

Efficiency, scalability, and algorithmic

optimization

Note. Adapted from the conceptual distinctions developed in [1], [20], and [5].

Mechanisms of Epistemic Erosion: Technology as a Vector of Westernization

The displacement of IKS operates through several structural mechanisms embedded within modern

technological architectures. These mechanisms are largely invisible because they are framed as efficiency,

inclusion, or progress.

Epistemic Standardization and Quantifiability

Digital technologies depend inherently on codification, quantification, and abstraction. These processes privilege

knowledge that can be digitized, measured, and standardized into machine-readable formats. Consequently, tacit,

experiential, and oral knowledge forms—which define much of India's indigenous heritage—are systematically

excluded or distorted in translation [16]. When traditional knowledge is forced into digital repositories such as

the Traditional Knowledge Digital Library, it is often stripped of its localized, sacred, or communal context,

converting living wisdom into static data [21].

Algorithmic Bias and Generative Imperialism

Algorithms embedded within global platforms reflect Western linguistic, cultural, and epistemological

assumptions [15]. In the era of generative AI, this threat has become more insidious, because generative systems

do not merely disseminate culture but actively rewrite it [12]. Large language models (LLMs) are trained on

corpora that are overwhelmingly English-centric: English constitutes roughly 46% of the most recent Common

Crawl web archive [7], and by several estimates more than 90% of the training data used for prevailing LLMs is

in English [23]. Hundreds of lower-resource languages each account for less than 0.01% of the Common Crawl

corpus, reflecting a steep long tail of digital invisibility [10]. As a result, models frequently generate exoticized,

stereotypical, or erased representations of Global South epistemologies [18].

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Data Dispossession and Digital Colonialism

The extraction of data from indigenous populations without equitable benefit-sharing mirrors historical colonial

extraction [5]. Corporations headquartered in the Global North frequently mine Traditional Ecological

Knowledge to train AI systems or to identify patentable resources, often circumventing the principles of Free,

Prior, and Informed Consent. This dynamic reduces indigenous communities to data subjects rather than

knowledge sovereigns, reproducing colonial asymmetries in digital form [21].

Psychological and Cognitive Impacts

Technological systems do not merely change what is known; they alter how knowing occurs. Research indicates

that sustained digital immersion restructures human attention, memory, and cognitive processing [2]. Two effects

are particularly consequential for IKS. First, cognitive offloading—the reliance on search engines and AI for

information retrieval—diminishes the internalized memorization central to oral IKS traditions, such as the

mnemonic and recitational practices associated with śruti and smṛti in Indian intellectual history [2, 16]. Second,

algorithmic conditioning, whereby continuous exposure to algorithmically curated feeds shapes perception and

cultural preferences, drives younger generations toward homogenized, globalized content [15].

Together these effects contribute to what may be termed epistemic dependency: a condition in which individuals

increasingly rely on external, privately owned technological systems to mediate their understanding of the world,

progressively abandoning internalized cultural knowledge. Epistemic dependency is not merely an individual

cognitive shift but a structural reallocation of interpretive authority from communities to platforms [5].

Critical Perspectives on Technological Determinism

A rigorous critique of technological modernity requires engagement with the philosophical tradition that

interrogates how large-scale technical systems reduce human autonomy and cultural diversity. While radical

anti-industrial polemics sometimes advocate the wholesale dismantling of technology, a scholarly perspective

extracts their analytic value without endorsing technological regression or harmful ideologies.

Ellul [6] and Heidegger [9] argued that technology is not merely a set of tools but an overarching framework—

technique, or Gestell—that compels all human experience to be calculated and optimized. Three propositions

follow from this tradition. Technological systems tend to prioritize efficiency over cultural meaning; industrial-

technological structures naturally centralize power in the hands of those who control the infrastructure,

exemplified today by large technology firms; and human behavior becomes increasingly system-dependent,

eroding local resilience. These classical critiques align with contemporary analyses of surveillance capitalism

and data colonialism [5], suggesting that the loss of indigenous agency is a structural feature of unregulated

technological expansion rather than an incidental flaw.

India-Specific Case Studies

To contextualize the theoretical account of erosion, the following case studies examine the friction between IKS

and technological modernity in contemporary India. Each foregrounds a distinct mechanism: infrastructural

determinism in education, linguistic erasure in AI, and data dispossession in agriculture.

Case Study 1: The NEP 2020 Paradox and the EdTech Boom

The National Education Policy (NEP) 2020 represents a paradigmatic shift, explicitly mandating the integration

of Indian Knowledge Systems into mainstream curricula in order to decolonize education [13]. Yet this mandate

coincides with a large-scale, state-supported integration of global EdTech platforms and the establishment of the

National Educational Technology Forum. The Indian EdTech market generated revenues of approximately

US$6.26 billion in 2024, having grown at a compound annual rate of about 20.3% between 2019 and 2024, with

the pre-K-12 and K-12 segment alone accounting for roughly 44% of value [8].

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The paradox is structural. While the policy champions IKS, the dominant medium of delivery—standardized,

screen-based EdTech—is itself reductionist. Traditional learning paradigms such as the gurukula ethos, which

emphasizes holistic, localized, and ethical mentorship, are displaced by hyper-individualized modules, risking

the commodification of IKS into superficial “content” rather than a lived epistemological reality. The volatility

of the sector underscores the risk of subordinating pedagogy to market logic: the once-dominant platform

BYJU'S collapsed from a peak valuation of approximately US$22 billion into insolvency proceedings, even as

the hybrid provider Physics Wallah achieved a public listing valued at roughly US$5.2 billion [19]. An education

system that anchors IKS to such infrastructure inherits its instability.

Case Study 2: Generative AI and Linguistic Marginalization

India is among the most linguistically diverse societies in the world. The 2011 Census recorded 19,569 raw

mother-tongue returns, which official classification rationalized into 1,369 mother tongues and ultimately

grouped into 121 languages—of which only 22 are listed in the Eighth Schedule of the Constitution, alongside

99 non-scheduled languages [3, 22]. Approximately 97% of the population reports a scheduled language as its

mother tongue, leaving the remaining 3% linguistically peripheral to official recognition [3]. Crucially, the

classification regime itself renders diversity invisible: widely spoken tongues such as Bhili (around 10.4 million

speakers) and Gondi (around 2.98 million speakers) are subsumed under broader categories and denied the status

of “language” [22].

Generative AI compounds this marginalization. Because foundational models are trained overwhelmingly on

English and a handful of dominant global languages—with English alone comprising roughly 46% of the latest

Common Crawl corpus and over 90% of typical LLM training data [7, 23]—the sociolinguistic nuance of

regional Indian dialects is poorly captured. State initiatives such as Bhashini seek to digitize and translate Indic

languages, but the underlying generative logic tends to default to hegemonic tropes or to hallucinate when

representing minority cultures, enacting a form of epistemic erasure that renders localized narratives invisible to

the machine [12, 18].

Case Study 3: Adivasi Data Extraction and Agricultural Technology

In regions such as Andhra Pradesh and Odisha, digital platforms introduced under the rubric of “agribusiness

inclusion” have increasingly monitored Adivasi farmers. Given that roughly 93% of India's 104.2 million

Scheduled Tribe members reside in rural areas and depend on agriculture and allied activities [3, 14], the stakes

of such monitoring are considerable. While marketed as developmental technology, these top-down systems

frequently bypass traditional village councils and customary governance structures.

The asymmetry is sharpened by an underlying digital divide. As of early 2025, internet access reached 91.6% of

urban households but only 83.3% of rural households, and only 3.8% of rural households had high-speed fiber

connections, compared with 15.3% in urban areas; the single most commonly reported barrier to adoption was

low digital readiness rather than absent infrastructure [4]. Communities that are least equipped to negotiate data

terms are thus most exposed to extraction. As indigenous biodiverse farming data is mapped and absorbed into

corporate databases, local data sovereignty is undermined and collective rights are disrespected, converting

indigenous agricultural wisdom into an exploitable commodity [21].

Reintegrating IKS: A Framework for Technological Sovereignty

The relationship between technology and culture is asymmetrical; absent deliberate intervention, technological

systems persistently privilege dominant epistemologies. Preserving IKS therefore requires active, systemic

resistance rather than passive coexistence. Table 2 outlines a structured framework for transitioning from

epistemic erosion to revitalization across four domains.

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Table 2. The Epistemic Erosion and Revitalization Framework

Domain

Current State (Epistemic Erosion)

Proposed Strategy (Epistemic

Revitalization)

Data governance

Extractive data colonialism; individual-

consent models (e.g., DPDP Act 2023)

inadequate for collective indigenous rights

Data sovereignty: collective-consent

protocols (Free, Prior, and Informed

Consent) and indigenous-owned data trusts

Education

Standardized, Western-centric EdTech

platforms; learning decoupled from local

ecology

Hybrid pedagogies: organic integration of

IKS into curricula; experiential, land-based

learning alongside digital literacy

AI and

algorithms

Algorithmic bias; erasure of non-dominant

languages and oral traditions

Decolonial AI: localized, community-

trained models that respect cultural nuance

and indigenous linguistic datasets

Cultural heritage

Commodification of IKS; knowledge

stripped of sacred and ethical context

Contextual digital archiving: community-

governed repositories that retain

provenance, protocol, and meaning

Note. Framework synthesized by the authors from [5], [20], and [21]. DPDP Act = Digital Personal Data

Protection Act.

DISCUSSION

The case studies illuminate a consistent asymmetry: technological systems, although transformative, are not

culturally neutral. In India, the tension between NEP 2020's aspirational decolonization rhetoric and the reality

of a multibillion-dollar, Western-influenced EdTech market exemplifies how progressive intentions can be

undermined by infrastructural determinism [8, 13]. Similarly, the algorithmic marginalization of Indic languages

and the extraction of data from Adivasi communities reveal the continuity of colonial dynamics in digital form

[5, 18].

These findings cohere into a single argument: erosion is mediated less by overt hostility to IKS than by the

default settings of digital infrastructure—its preference for the quantifiable, the English-language, and the

centrally controlled. This is precisely why good-faith policy can coexist with epistemic loss. Yet the framework

proposed in Table 2 indicates that erosion is not inevitable. Where technology is designed for amplification

rather than assimilation—through collective data rights, hybrid pedagogy, and decolonial AI—it can extend

rather than displace indigenous epistemologies. Realizing this potential requires political will, community

leadership, and interdisciplinary collaboration.

Limitations and Future Research

Three limitations qualify these conclusions. First, the study is conceptual and interpretive; its propositions are

illustrative rather than statistically generalizable, and the secondary indicators it integrates derive from

heterogeneous sources with differing methodologies. Second, the treatment of IKS necessarily aggregates highly

diverse traditions, and some nuance is lost in synthesis. Third, the case studies are purposively rather than

randomly selected. Future research should empirically test the revitalization strategies through longitudinal

studies of hybrid education models, participatory evaluations of community-trained language models, and field

assessments of indigenous data trusts. Comparative work across other postcolonial contexts would further clarify

which mechanisms of erosion are India-specific and which are general features of digital modernity.

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CONCLUSION

Technological modernity, while offering transformative potential, has accelerated the marginalization of

Indigenous Knowledge Systems in India through epistemic standardization, algorithmic bias, cognitive

restructuring, and digital colonialism. The cases of NEP 2020 and EdTech, generative-AI linguistic erasure, and

Adivasi data extraction reveal how these processes manifest locally, displacing holistic, relational

epistemologies in favor of reductionist, market-driven paradigms. A balanced reclamation demands

technological sovereignty: deliberate design and governance choices that integrate IKS without subsuming it.

By operationalizing hybrid models, enforcing collective data rights, and fostering decolonial AI, India can

pioneer a pluralistic digital future that preserves cultural sovereignty while enriching global knowledge

ecosystems with diverse perspectives. The imperative is clear—without critical intervention, the promise of

technological progress risks becoming a new vector of epistemic erosion.

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