Generative AI and Social Inequality in Education: A Human-Centered Social Science Review of Global and Indonesian Evidence
DOI:
https://doi.org/10.61579/kirana.v3i3.991Keywords:
generative AI, social inequality, education, digital divide, AI literacy, human-centered AIAbstract
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 AI-supported learning, and governance risks involving privacy, bias, academic integrity, and human agency. The reported benefits of generative AI, such as tutoring, writing support, and translation, appear to depend on connectivity, devices, language resources, teacher competence, and students' ability to evaluate machine output. The Indonesian evidence is mostly cross-sectional, self-reported, or based on small single-institution samples, with little evidence on learning outcomes. One national household analysis of school participation (not AI use) found no significant difference between 3T (disadvantaged, frontier, and outermost) and matched non-3T households, suggesting that regional labels may be an imperfect proxy for household conditions. The review proposes a cumulative model of AI inequality that extends van Dijk's sequence of digital inequality with a governance stage and a feedback loop. The model is a conceptual proposition, not a tested result, and recommendations are ordered by the strength of supporting evidence.
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