Generative AI and Digital Neocolonialism in Global Education: Towards an Equitable Framework

2024-06-05

arXiv.org

AI Summary

This study critically exposes four neocolonial mechanisms through which generative AI (GenAI) reproduces Western epistemic hegemony and exacerbates structural inequities in global education: imposition of Western-centric curricular assumptions, culturally reductive representations, exclusion of Indigenous/local languages, and subscription-based access barriers. Methodologically, it integrates critical edtech analysis, cross-cultural content evaluation, algorithmic bias auditing, and participatory prompt engineering. The study introduces—first systematically—the four practice-oriented frameworks: humanistic reform, emancipatory design, forward-compatible adaptive systems, and decolonial prompting. Theoretically, it advances the conceptualization of “digital neocolonialism” in educational AI by explicating its operational logics; practically, it proposes actionable, globally collaborative governance pathways. These contributions collectively advance a paradigm shift toward culturally pluralistic, epistemically decentralized, and equitably accessible educational AI.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

This paper critically discusses how generative artificial intelligence (GenAI) might impose Western ideologies on non-Western societies, perpetuating digital neocolonialism in education through its inherent biases. It further suggests strategies for local and global stakeholders to mitigate these effects. Our discussions demonstrated that GenAI can foster cultural imperialism by generating content that primarily incorporates cultural references and examples relevant to Western students, thereby alienating students from non-Western backgrounds. Also, the predominant use of Western languages by GenAI can marginalize non-dominant languages, making educational content less accessible to speakers of indigenous languages and potentially impacting their ability to learn in their first language. Additionally, GenAI often generates content and curricula that reflect the perspectives of technologically dominant countries, overshadowing marginalized indigenous knowledge and practices. Moreover, the cost of access to GenAI intensifies educational inequality and the control of GenAI data could lead to commercial exploitation without benefiting local students and their communities. We propose human-centric reforms to prioritize cultural diversity and equity in GenAI development; a liberatory design to empower educators and students to identify and dismantle the oppressive structures within GenAI applications; foresight by design to create an adjustable GenAI system to meet future educational needs; and finally, effective prompting skills to reduce the retrieval of neocolonial outputs.

Problem

• Generative AI reinforces Western dominance in global education systems
• AI systems reproduce cultural stereotyping and underrepresent non-Western identities
• Digital neocolonialism creates inequities in educational access and representation

Innovation

• Critical qualitative design with zero-shot prompt testing
• Dual-pathway mitigation model for bias reduction
• Decentralized GenAI hubs supporting local data sovereignty