Truace
Subscribe

Generative AI and Social Inequality in Education: A Human-Centered Social Science Review of Global and Indonesian Evidence

This review examined how generative artificial intelligence (AI) can widen educational opportunity while also reproducing social inequality. An integrative review synthesized 24 sources: 17 Indonesian publications (2022–2026), identified through national journal portals and Google Scholar, and seven international policy documents and scholarly works (2016–2023) selected as conceptual anchors. Thematic analysis produced four themes: unequal access, unequal digital and AI literacy, unequal capacity to benefit from…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1995· cite & verify
Generative AI and Social Inequality in Education: A Human-Centered Social Science Review of Global and Indonesian Evidence
Reading Wikipedia in the Classroom for Secondary School Students 10 by James Rhoda. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

An integrative review pulled together 24 sources, mostly Indonesian studies from 2022 to 2026 plus a smaller set of international policy and scholarly documents, to ask whether generative AI expands educational opportunity or entrenches existing disadvantage. Thematic analysis identified four recurring themes covering access, literacy, capacity to benefit, and governance.

The findings matter because tools like AI tutors and translators may help mainly those who already have devices, connectivity, language resources, and skilled teachers, leaving others behind. How large the effect is remains unclear: the Indonesian evidence is thin on learning outcomes, and the proposed model of cumulative AI inequality is a conceptual idea rather than a tested result.

Main points

  1. The review synthesized 24 sources: 17 Indonesian publications from 2022 to 2026 and seven international policy documents and scholarly works from 2016 to 2023.
  2. Four themes emerged: unequal access, unequal digital and AI literacy, unequal capacity to benefit from AI-supported learning, and governance risks.
  3. Governance concerns named include privacy, bias, academic integrity, and human agency.
  4. A national household analysis of school participation, not AI use, found no significant difference between 3T and matched non-3T households.
  5. The proposed model extends van Dijk's digital inequality sequence with a governance stage and a feedback loop.

The problem

The benefits of generative AI in education depend on connectivity, devices, language resources, teacher competence, and evaluation skills, so unequal access and literacy can reproduce social inequality and raise governance risks.

The rundown

The authors searched national journal portals and Google Scholar for Indonesian work and chose the international documents as conceptual anchors. The reported advantages of generative AI, including tutoring, writing support, and translation, appear conditional on infrastructure, language resources, teacher competence, and students' ability to judge machine output.

One household analysis the review cites looked at school participation rather than AI use. It found no significant difference between 3T (disadvantaged, frontier, and outermost) households and matched non-3T households, suggesting regional labels may be an imperfect proxy for household conditions.

The review proposes a cumulative model of AI inequality that adds a governance stage and a feedback loop to van Dijk's sequence. Its recommendations are ordered by the strength of the evidence behind them.

Sources

  1. Peer-reviewedKIRANA : Social Science Journal2026-10-20

The debate