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August 19, 2026Transforming Government People Process and Policy

Beyond access: a mixed-methods analysis into generative AI integration and educational equity

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Authors

PCPraveen ChoudharyHCL Technologies (United States)PUParijat UpadhyayIndian Institute of Management Shillong

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Overview

Mixed-methods study reveals socioeconomic disparities in AI readiness among students and educators, highlighting the need for structured integration policies to ensure educational equity.

Key Points

  • To examine how the integration of generative artificial intelligence influences educational equity across diverse socioeconomic strata using a digital inequality framework.
  • Conducted mixed-methods research (2023–2024) involving 27 focus groups with 133 educators alongside qualitative interviews with school administrators in the National Capital Region in Delhi.
  • Administered performance testing among N=587 students attending schools across varied socioeconomic tiers.
  • Formulated the GenAI Educational Equity Matrix encompassing three interrelated domains: technological capital, engagement ecology, and policy implementation.
  • Identified significant disparities in artificial intelligence readiness, infrastructure access, and adoption capacity aligned with socioeconomic status.

Cite This Study

Choudhary et al. (2026) studied this question.

synapsesocial.com/papers/6a858aaf03308d306e2d8027https://doi.org/10.1108/tg-05-2025-0142
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