<![CDATA[This study employs a qualitative narrative review of literature published between 2010 and 2024. Sources were selected based on relevance to AI in education and engagement with sociological concepts such as equity, digital divide, and social interaction. The data were analyzed using thematic analysis and interpreted through sociological frameworks, including Bourdieu’s theory of capital, Bernstein’s pedagogic control, and Vygotsky’s social learning theory. The findings indicate that AI contributes to improved instructional quality through personalized learning, enhanced engagement, and efficient feedback mechanisms. AI also expands educational access, particularly for learners in remote areas. However, its implementation remains uneven, with disparities in infrastructure, digital access, and teacher readiness. Additionally, increased reliance on AI may reduce face-to-face interaction and shift pedagogical authority toward algorithmic systems. The study reveals that AI functions as a dual-pathway mechanism: it enhances educational quality and access while simultaneously reinforcing existing social inequalities. Its impact is shaped by institutional capacity, digital capital, and pedagogical practices, highlighting that AI is not a neutral tool but a sociotechnical construct embedded in broader social structures. AI has the potential to support equitable and high-quality education, but only when supported by inclusive policies, adequate infrastructure, and strong teacher capacity. A sociologically informed approach is essential to ensure that AI integration promotes educational equity rather than deepening existing disparities.]]>
