The use of ai-based adaptive homework and teacher feedback: a comparative analysis of their impact on students in rural and urban areas

Abstract

This study examines the comparative impact of AI-based adaptive homework and teacher feedback on students in rural and urban areas. The introduction justifies the relevance of the topic through the rapid development of digital education, existing educational inequalities between regions, and the scientific-pedagogical significance of adaptive technologies. The main objective of the research is to empirically determine how AI-based adaptive tasks influence students’ academic achievement, motivation, and responsiveness to teacher feedback across different geographical contexts. A quasi-experimental approach was employed in the methodology, with pre- and post-tests, platform-generated statistical data, and semi-structured interviews serving as the primary research instruments. The results indicate that urban students demonstrated a 25% increase in post-test scores, while rural students showed an 18% increase. Additionally, the urban group exhibited higher engagement with feedback, faster task completion, and a more dynamic progression toward advanced task levels. The conclusion emphasizes that adaptive technologies are effective in both settings; however, technical infrastructure, digital literacy, and teacher competence significantly influence outcomes. The findings have practical implications for integrating AI tools into school education, reducing regional educational disparities, and preparing teachers for digital pedagogy.