Generative AI has made sophisticated digital tools easier to reach, but access alone is not producing equal competence, according to research from Nanjing University. The study, “Mapping the AI Divide in Undergraduate Education: Community Detection in Disciplinary Networks and Survey Evidence,” authored by Xiaomin Ji, Wei Si, Liwen Zhang, Jiangli Zhu and Ke-Ke Shang and published in Big Data and Cognitive Computing, combines curriculum network analysis with survey evidence from 301 undergraduates to examine how disciplinary environments relate to artificial intelligence-generated content, or AIGC, literacy. Students in science-dominant curricular networks reported stronger ability to use and evaluate AIGC than students in humanities and social sciences, while differences in motivation and perceived skills accounted for part of the gap. The pattern does not establish causation, but it raises a difficult question for universities: can equal access to AI coexist with unequal opportunities to become competent users of it? The study argues inequality can shift from possession of technology to the quality of interaction with it, since two students may have access to the same AI platform yet differ greatly in their ability to formulate useful requests, test outputs, identify weaknesses and decide when generated material can be trusted.