[论文解读] Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation

Matthew Nyaaba, Alyson Wright+1 more|arXiv (Cornell University)|Jun 5, 2024

Global Education and Multiculturalism被引用 21

一句话总结

本文分析生成式AI如何强化教育领域的全球权力层次，并提出一个偏见缓解的双路径框架：Inclusive AI Design 和 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.

研究动机与目标

• 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.

提出的方法

• 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.

实验结果

研究问题

• 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?

主要发现

• 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.