人工智能时代大数据专业跨学科创新应用型人才培养

Cultivation of Interdisciplinary Innovative Application-Oriented Talents in Big Data Major in the Era of Artificial Intelligence

摘要: 生成式人工智能与大模型技术的快速迭代，推动大数据产业向“技术 + 场景”深度融合方向发展，对人才的跨学科素养、创新能力与工程实践能力提出了更高要求。针对传统大数据专业培养中学科壁垒明显、实践场景不足、AI技术融合不深等问题，文章以成果导向教育理念为核心，构建“课程–实践–师资–评价”四位一体的跨学科创新应用型人才培养模式，分模块阐述分层交叉课程、三维联动实践平台、多元育人团队、闭环质量管控四项落地路径，同时展示本专业专属育人成效，可为地方应用型高校大数据特色专业建设提供实践参考。

Abstract: The rapid iteration of generative artificial intelligence and large-model technologies is driving the big data industry toward deep integration of “technology + scenarios,” imposing higher demands on talents’ interdisciplinary literacy, innovative capabilities, and engineering practice skills. To address the prominent issues in traditional big data programs—such as rigid disciplinary boundaries, insufficient practical scenarios, and shallow integration with AI technologies—this paper adopts Outcome-Based Education (OBE) as the core philosophy and constructs a four-in-one interdisciplinary innovative application-oriented talent cultivation model encompassing “curriculum-practice-faculty-assessment.” It elaborates on four specific implementation pathways: layered and cross-disciplinary curriculum design, a three-dimensional collaborative practice platform, a diversified educational team, and a closed-loop quality assurance system. Meanwhile, it demonstrates the distinctive educational outcomes achieved by our program, offering practical references for the development of featured big data majors in local application-oriented universities.