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

Paper Title

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

Publication Info

• Topic area: Generative AI in K-12 education and its implications for educational equality.
• Keywords: Generative AI, K-12 education, educational inequality, AI literacy, global perspectives, teacher practices, sociotechnical systems, infrastructure, cultural localization, professional development.

Background and Problem

• Problem / challenge: The rapid integration of Generative AI (GenAI) into K-12 classrooms raises concerns about whether it will reduce or exacerbate educational inequalities. Existing research focuses on adoption factors and risks but lacks insight into how teachers actively navigate these tensions in practice.
• Significance: Understanding how GenAI impacts educational equality is crucial for designing inclusive systems and policies, given its potential to democratize learning or deepen divides based on infrastructure, cultural biases, and access.
• Motivation and related work: Prior studies have explored AI’s role in personalized learning and curriculum integration but have not sufficiently addressed how teachers mediate GenAI’s dual potential for equality and inequality. This paper builds on sociotechnical perspectives to examine teachers’ practices globally.

Solution

• Proposed approach: A qualitative study analyzing how K-12 teachers in the United States, South Africa, and Taiwan teach GenAI to promote educational equality, based on semi-structured interviews with 30 teachers.
• Novelty:
  1. Extends AI education frameworks by documenting global teacher practices for embedding GenAI into classrooms.
  2. Highlights systemic constraints—such as infrastructure gaps, insufficient training, and cultural resistance—that limit equality-oriented GenAI education.
  3. Proposes actionable guidelines for schools, companies, and governments to support inclusive GenAI education.
• Procedure and key techniques:
  • Conducted 30 semi-structured interviews with teachers actively teaching GenAI.
  • Focused on three regions (United States, South Africa, Taiwan) to capture diverse sociocultural contexts.
  • Analyzed data using reflexive thematic analysis to identify practices, challenges, and envisioned solutions.

Results

• Concrete findings:
  • Teachers use GenAI to address pre-existing inequalities (e.g., resource scarcity, digital literacy gaps) and prevent new ones (e.g., misuse, cultural bias, emotional overreliance).
  • Structural barriers include unreliable connectivity, uneven access to training, and restrictive social norms.
  • Teachers envision support from schools (innovation centers, resource redistribution), companies (localized and accessible GenAI tools), and governments (universal AI literacy initiatives).
• Advantage over baselines:
  • Demonstrates how teachers actively negotiate GenAI’s role in promoting equality, contrasting with prior studies that focus on technology adoption or risks.
  • Highlights the interplay between micro-level teacher practices and macro-level systemic constraints.
• Experiments / evaluation:
  • Interviews with 30 teachers across three regions, focusing on their practices, challenges, and visions for GenAI education.
  • Comparative analysis of regional contexts to identify global commonalities and local differences.
• Limitations and future work:
  • Limited to three regions; future studies should explore additional contexts.
  • Focuses on teacher perspectives; future research should include students, parents, policymakers, and developers.
  • Does not measure long-term student outcomes; empirical studies are needed to assess impacts on learning trajectories and AI literacy.

Summary

This study examines how K-12 teachers in the United States, South Africa, and Taiwan integrate GenAI into classrooms to promote educational equality. Teachers use GenAI to mitigate resource gaps and broaden access but face systemic barriers such as infrastructural constraints, insufficient training, and cultural resistance. They envision support from schools, companies, and governments to make GenAI education more inclusive. The findings highlight the dual potential of GenAI to alleviate or exacerbate inequalities and underscore the need for coordinated design and policy interventions to sustain equality-oriented practices globally.