Bridging or Deepening the Gap? A Conceptual Framework for Examining AI-Powered Adaptive Learning Platforms and Educational Equity in Chinese Urban and Rural Schools

Abstract

The strategic position of artificial intelligence (AI) in the modernisation process of China’s education is becoming increasingly important. At the same time, AI-powered adaptive learning platforms (ALPs) are being rapidly implemented to facilitate the inclusive sharing of high-quality educational resources and bridge significant geographical disparities. However, there is significant pressure on the government to create an “intelligent education ecosystem”, and there are legitimate concerns that structural differences between schools in urban and rural areas may exacerbate, rather than alleviate, educational inequality. This study goes beyond mere technical optimism by presenting an integrated conceptual framework that draws on the technology acceptance model (TAM) and the sociological “Matthew Effect” to examine how micro-adoption behaviours interact with macro-structural constraints. Furthermore, this paper describes the current situation of ALPs usage in rural areas as a “recursive cycle of digital stratification”, in which initial resource inequality in rural areas suppresses learners’ perceived ease of use (PEOU) and perceived usefulness (PU), leading to lower participation and insufficient data input for adaptive algorithms, ultimately resulting in an ever-widening learning gap when compared to urban areas. To assist policymakers in addressing the specific ecological issues faced by rural schools, this article advocates a shift away from algorithm optimisation towards an overall “socio-technical inclusiveness” approach.