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.
- Question: How do K-12 teachers teach generative AI to navigate educational equality, and what structural barriers do they face?
- Setting and data: 30 K-12 teachers in the United States (10), South Africa (10), and Taiwan (10).
- Method: Semi-structured interviews analyzed using reflexive thematic analysis.
Generative AI creates urgent educational equality debates. How K-12 teachers directly mediating student interaction teach about generative AI to navigate the tension between reducing and exacerbating inequalities remains underexplored across global contexts.
- Teachers actively taught generative AI to alleviate pre-existing inequalities like resource scarcity while guarding against new inequalities such as shortcut use, cultural bias, and overreliance.
- Structural barriers including unreliable internet connectivity, gaps in students' basic digital fluency, and cultural and linguistic biases in AI tools severely limited teachers' individual efforts.
- Uneven access to professional development, lack of curricular frameworks, and resistance from conservative groups or skeptical parents further constrained equitable generative AI education across all three regions.
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