Do Teachers Dream of GenAI Widening Educational (In)equality? Envisioning the Future of K-12 GenAI Education from Global Teachers' Perspectives

TL;DR

Generative AI in K-12 education holds promise for reducing long-standing inequities but also risks widening gaps without proper support. The authors conducted 30 semi-structured interviews across the United States, South Africa, and Taiwan to examine how teachers frame GenAI as an equality-oriented practice. They identify three core contributions: (1) equality-oriented teaching practices that alleviate pre-existing inequalities and guard against new ones; (2) structural barriers—infra, training, norms—that limit scale; and (3) actionable visions for school, industry, and government to foster more inclusive GenAI education. The study offers a global sociotechnical perspective and actionable design/policy guidance to ensure GenAI education advances educational equality.

Abstract

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.