Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices
This work addresses the issue of cultural misalignment in GenAI tools for K-12 education, offering implications for AI designers, policymakers, and educators to create more equitable tools, though it is incremental as it builds on existing concerns about bias in AI.
The paper tackled the problem of generative AI models being trained on culturally uneven datasets that misalign with local classroom demands, by introducing the concept of Cultural Distance and developing a framework based on interviews with 30 K-12 teachers from South Africa, Taiwan, and the United States.
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.