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 usage efficacy does not mediate at the evaluation level, indicating a dissociation between perceived utility and critical engagement. 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.

翻译：随着人工智能生成内容（AIGC）重塑知识获取方式，高等教育面临日益加剧的不平等问题，亟需系统性描绘与干预。本研究通过结合网络科学与来自中国人工智能教育领先机构之一——南京大学301名学生的调查证据，绘制了本科教育中的人工智能鸿沟。我们利用课程选修模式构建了一个学科网络，识别出四个不同的学生社区：科学主导型、科学边缘型、社会科学与科学结合型以及人文与社会科学型。调查结果显示，学生在AIGC素养和动机效能方面存在显著差异，科学主导型学生表现优于人文与社会科学型学生。普通最小二乘法（OLS）回归分析表明，动机效能——特别是技能效能——部分中介了这种差距，而使用效能在评估层面未起中介作用，这表明感知效用与批判性参与之间存在分离。我们的研究结果表明，课程结构和跨学科整合是技术流畅性的关键决定因素。这项工作为通过制度设计诊断和解决人工智能鸿沟提供了一个可扩展的框架。