Date
Publisher
arXiv
Generative artificial intelligence (GenAI) is rapidly entering K-12 classrooms worldwide, initiating urgent debates about its potential to either reduce or exacerbate educational inequalities. Drawing on interviews with 30 K-12 teachers across the United States, South Africa, and Taiwan, this study examines how teachers navigate this GenAI tension around educational equalities. We found teachers actively framed GenAI education as an equality-oriented practice: they used it to alleviate pre-existing inequalities while simultaneously working to prevent new inequalities from emerging. Despite these efforts, teachers confronted persistent systemic barriers, i.e., unequal infrastructure, insufficient professional training, and restrictive social norms, that individual initiative alone could not overcome. Teachers thus articulated normative visions for more inclusive GenAI education. By centering teachers' practices, constraints, and future envisions, this study contributes a global account of how GenAI education is being integrated into K-12 contexts and highlights what is required to make its adoption genuinely equal.
Study at a glance
- Question: How do K-12 teachers view and navigate generative AI's impact on educational equality?
- Setting and data: 30 K-12 teachers in the United States, South Africa, and Taiwan
- Method: Interviews with teachers
Why it matters
Generative artificial intelligence is rapidly entering K-12 classrooms worldwide, initiating urgent debates about its potential to either reduce or exacerbate educational inequalities.
Key findings
- Across the United States, South Africa, and Taiwan, 30 K-12 teachers used generative AI to reduce existing inequalities while working to prevent new ones.
- Teachers faced persistent systemic barriers, including unequal infrastructure, insufficient professional training, and restrictive social norms, that individual efforts could not overcome.
- Teachers articulated visions for achieving more inclusive generative AI education.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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