[논문 리뷰] Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation

Matthew Nyaaba, Alyson Wright외 1명|arXiv (Cornell University)|2024. 06. 05.

Global Education and Multiculturalism인용 수 21

한 줄 요약

이 논문은 Generative AI가 교육에서 글로벌 권력 위계를 강화하는 방식과 편향 완화를 위한 이중 경로 프레임워크를 제안합니다: Inclusive AI Design과 Pedagogical human-centric prompting.

ABSTRACT

This study examines how Generative Artificial Intelligence reproduces global power hierarchies in education and proposes a framework to address resulting inequities. Using a critical qualitative design, the study conducted zero-shot prompt testing with two leading systems, ChatGPT-4 Turbo and Gemini 1.5, and collected real-time outputs from Global North and South contexts. A critical interpretive analysis traced textual, visual, and structural patterns that revealed forms of digital neocolonialism and their implications for educational equity. Findings show six ways in which GenAI can reinforce Western dominance. Western curriculum assumptions appeared when Gemini listed the same four seasons for the United States and Ghana, reflecting Western climatology and overlooking regional knowledge systems. Other patterns included cultural stereotyping in imagery, Western-centered examples in instructional outputs, limited support for Indigenous and local languages, underrepresentation of non-Western identities in visuals, and access barriers linked to subscription-based models. These patterns demonstrate how GenAI can reproduce inequities even as it introduces new educational opportunities. In response, the study proposes a dual-pathway mitigation model. The Inclusive AI Design pathway includes three components: liberatory design methods that center non-Western epistemologies, anticipatory approaches to reduce representational harm, and decentralized GenAI hubs that support local participation and data sovereignty. The pedagogical pathway, human-centric prompt engineering, equips educators to contextualize prompts and critically engage with outputs. Together, these pathways position GenAI as a tool that can support more equitable and culturally responsive education.

연구 동기 및 목표

• Examine how Generative AI reproduces global power hierarchies in education.
• Identify concrete patterns of bias and inequity in GenAI outputs across Global North and South contexts.
• Develop a framework to mitigate bias and promote equitable, culturally responsive education.

제안 방법

• Conduct zero-shot prompt testing with ChatGPT-4 Turbo and Gemini 1.5.
• Collect real-time outputs from Global North and South contexts.
• Apply critical interpretive analysis to identify patterns of digital neocolonialism in text, visuals, and structures.

실험 결과

연구 질문

• RQ1 What forms of digital neocolonialism and Western-centric bias appear in GenAI educational outputs?
• RQ2 How do Western curriculum assumptions and representation issues manifest in GenAI-generated content?
• RQ3 What governance and design approaches can mitigate inequities in GenAI-assisted education?
• RQ4 Can a dual-pathway framework—Inclusive AI Design and Pedagogical prompting—reduce bias and improve local participation and data sovereignty?

주요 결과

• GenAI outputs show six patterns reinforcing Western dominance, including Western climatology assumptions and limited local knowledge representation.
• Imagery and examples tend toward Western-centric and non-Indigenous portrayals.
• Non-Western languages and Indigenous knowledge receive limited support in outputs.
• Access barriers are linked to subscription-based models.
• There is potential for GenAI to reproduce inequities despite new opportunities.