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

arXiv: https://arxiv.org/abs/2406.02966 · HTML: https://mlsanity.com/paper/2406.02966/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://mlsanity.com/paper/2311.05943/) (2023)
- [How Culture Shapes What People Want From AI](https://mlsanity.com/paper/2403.05104/) (2024)
- [Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning](https://mlsanity.com/paper/2402.01674/) (2024)
- [Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?](https://mlsanity.com/paper/2401.15081/) (2024)
- [Culturally Responsive Artificial Intelligence -- Problems, Challenges and Solutions](https://mlsanity.com/paper/2312.08467/) (2023)

## Built on by (most cited first, 2 of 2)

- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [Glocalizing Generative AI in Education for the Global South: The Design Case of 21st Century Teacher Educator AI for Ghana](https://mlsanity.com/paper/2504.07149/) (2025)

## Abstract (arXiv)

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.