Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation

TL;DR

This paper critically examines how Generative AI in education can reinforce global power imbalances and proposes a framework with design and pedagogical pathways to mitigate these inequities and promote cultural responsiveness.

Contribution

It introduces a dual-pathway mitigation framework addressing biases in Generative AI, emphasizing inclusive design and educator engagement to foster educational equity.

Findings

GenAI can reinforce Western dominance through curriculum and imagery biases.

Patterns of digital neocolonialism reveal inequities in AI-generated educational content.

Proposed mitigation includes inclusive design and human-centric prompt engineering.

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.