摘要
数字经济与技术迅速发展背景下,生成式人工智能正重塑高等教育人才培养模式,尤其在研究生教育中,为培养新质生产力人才提供了新的可能性。研究以“双一流”大学研究生为对象,运用技术接受模型理论,系统考察了研究生群体使用生成式人工智能的现状、分层特征及影响因素。结果表明,研究生群体在生成式人工智能接受过程中呈现“深度融合型”“理性权衡型”和“选择性适应型”典型分层特征。各群体性别、专业背景和教育层次差异揭示了技术接受过程的复杂性,表明高校培养人才时需要因材施教,制定差异化的人才培养策略,以更好地适应技术变革要求。
Against the backdrop of the digital economy and rapid technological advancements,generative artificial intelligence(Al)is reshaping talent development models in higher education,particularly in graduate education,offering new possibilities for cultivating high-caliber talent.This study examines graduate students at"Double First-Class"universities,employing the Technology Acceptance Model(TAM)to systematically investigate the current usage,stratified characteristics,and influencing factors of generative AI adoption among this group.The findings reveal three distinct adoption patterns among graduate students:"deep integration","rational balancing",and"selective adaptation".Furthermore,intergroup differences based on gender,disciplinary background,and educational level highlight the complexities of the technology acceptance process.These variations suggest that universities should adopt tailored educational strategies to accommodate diverse student needs and better align with the demands of technological transformation.This study provides theoretical insights and practical implications for optimizing talent development pathways and enhancing the effectiveness of AI application in education.
作者
林婕
陈庆
邱意弘
Lin Jie;Chen Qing;Qiu YiHong
出处
《高等工程教育研究》
北大核心
2025年第4期160-165,共6页
Research in Higher Education of Engineering
基金
上海交通大学2024年学生促教基金项目“研究生使用生成式人工智能的态度与影响因素探究”(CTLD24X0001)。
关键词
生成式人工智能
研究生教育
技术接受模型
分层特征
群际差异
generative artificial intelligence
graduate education
technology acceptance model
hierarchical characteristics
group differences
