Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices

2025-09-12

AI Summary

Generative AI (GenAI) exhibits a “cultural distance” problem in K–12 education: systemic misalignment between its culturally biased training data and the socioculturally situated needs of local classrooms. Drawing on in-depth interviews and cross-case analysis with 30 teachers across South Africa, Taiwan, and the United States, this study conceptualizes “cultural distance” and develops a three-level analytical framework that identifies six distinct teacher-led cultural adaptation strategies across typical pedagogical contexts. It is the first study to systematically document the practical manifestations of GenAI’s cultural mismatch in educational settings and to theorize corresponding adaptive mechanisms. The findings establish a foundational framework for cross-cultural adaptation in AI-education integration, informing culturally responsive design for developers, equity-oriented policy formulation for educational authorities, and contextually grounded AI pedagogy for educators.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a "default culture" that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI's default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 10 each from South Africa, Taiwan, and the United States, who had integrated AI into their teaching practice. These teachers' experiences informed the development of our three-level cultural distance framework. This work contributes the concept and framework of cultural distance, six illustrative instances spanning in low, mid, high distance levels with teachers' experiences and strategies for addressing them. Empirically, we offer implications to help AI designers, policymakers, and educators create more equitable and culturally responsive GenAI tools for education.

Problem

• Addressing cultural mismatch between GenAI models and local classrooms
• Examining how teachers adapt GenAI to diverse cultural contexts
• Developing framework for culturally responsive AI educational tools

Innovation

• Defined Cultural Distance concept for AI
• Conducted cross-cultural teacher interviews
• Developed three-level cultural distance framework