Date
Publisher
arXiv
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.
Study at a glance
- Question: How do K–12 teachers adapt generative AI tools to meet the situated cultural demands of everyday classroom practice?
- Setting and data: 30 K–12 teachers (10 each in South Africa, Taiwan, and the United States) who frequently use generative AI in their teaching.
- Method: Semi-structured interviews analyzed using reflexive thematic analysis.
Why it matters
Generative AI embodies default cultures from dominant countries that do not always fit local classrooms. Existing research overlooks how closely AI outputs match situated classroom demands and how much effort teachers spend to close that gap.
Key findings
- Cultural distance operates across three levels: low distance requiring minor edits, mid distance needing sustained prompting and manual revision, and high distance presenting unbridgeable structural barriers.
- Teachers easily adapted generative AI outputs for communication and engaging activity design, requiring minimal effort because these tasks closely match the models' default cultural repertoires.
- Generative AI failed when local languages or Indigenous traditions were missing from training data or when local policy filters blocked queries, leaving teachers with no workable adaptation pathway.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Who is the user?
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Study design
