Mapping the AI Divide in Undergraduate Education: Community Detection in Disciplinary Networks and Survey Evidence

As artificial intelligence-generated content (AIGC) reshapes knowledge acquisition, higher education faces growing inequities that demand systematic mapping and intervention. We map the AI divide in undergraduate education by combining network science with survey evidence from 301 students at Nanjing University, one of China’s leading institutions in AI education. Drawing on course enrolment patterns to construct a disciplinary network, we identify four distinct student communities: science dominant, science peripheral, social sciences & science, and humanities and social sciences. Survey results reveal significant disparities in AIGC literacy and motivational efficacy, with science dominant students outperforming humanities and social sciences peers. Ordinary least squares (OLS) regression shows that motivational efficacy—particularly skill efficacy—partially mediates this gap, whereas the mediating role of usage efficacy varies across model specifications, suggesting that the relationship between perceived utility and critical engagement is sensitive to how community differences and school-level clustering are modeled. Our findings demonstrate that curriculum structure and cross-disciplinary integration are key determinants of technological fluency. This work provides a scalable framework for diagnosing and addressing the AI divide through institutional design.