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| AI辅助博士生科研现状及其影响的学科差异——基于2024年全国博士毕业生调查的分析 |
| The Current Situation of AI-Assisted Research by Doctoral Students and the Disciplinary Differences Influenced Thereby: An Analysis based on the 2024 National Survey of Graduated Doctoral Students |
| 投稿时间：2025-05-26 |
| DOI：10.19834/j.cnki.yjsjy2011.2025.06.03 |
| 中文关键词: 博士生;人工智能;科研活动;学术训练;学科差异 |
| 英文关键词: doctoral students;artificial intelligence (AI);research activities;academic training;disciplinary differences |
| 基金项目:国家自然科学基金青年科学基金项目"基于IPOD模型的'医+X’博士生跨学科培养模式及其成效研究"(72304019);中国人民大学科学研究基金项目(中央高校基本科研业务费专项资金资助)"跨学科博士生培养质量的评价机制与保障体系研究"(25XNF041) |
| 作者 | 单位 | | 蔡芬 | 北京大学 全国医学教育发展中心/教育学院, 北京 100871 | | 谢鑫 | 中国人民大学 教育学院, 北京 100872 |
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| 中文摘要: |
| 人工智能(AI)在不同学科领域的科研活动中得到广泛应用,我国博士生使用AI辅助科研的学科差异亟待关注。对14371份全国学术学位博士生问卷数据进行分析发现:第一,理工农医尤其是计算机相关专业博士生的AI辅助科研使用率较高,而人文社科尤其是人文学科博士生的使用率较低。第二,人文社科博士生更多将AI用于前端科研工作,理工农医博士生更多用于后端科研工作。第三,性别、年龄、多学科导师联合指导以及跨学科学位论文研究需求因素可显著预测博士生的AI辅助科研行为。第四,AI辅助前端科研工作对博士生尤其是人文社科博士生的跨学科能力训练有益,但直接用于文本写作则对博士生的学术训练不利。据此,建议积极构建"人文社科+AI"的跨学科学术训练模式,加强理工农医领域AI辅助科研的规范化引导,结合学科文化特征推动AI技术对博士生教育的赋能。 |
| 英文摘要: |
| Artificial intelligence (AI) is widely used in research activities across various academic disciplines. However, the differences in how doctoral students in China use AI-assisted research across disciplines warrant greater attention. Based on an analysis of 14,371 valid responses from a national survey of academic doctoral students, this study presents four key findings:first, AI-assisted research is more prevalent among doctoral students in the Sciences, Engineering, Agriculture and Medicine (SEAM), particularly in computer-related fields, but it is less popular among those in the humanities and social sciences;second, doctoral students in the humanities and social sciences are more likely to use AI in the early stages of their research. In contrast, those in the SEAM tend to use it in the later stages; third, factors such as gender, age, joint supervision by supervisors from different disciplines, and the need for interdisciplinary dissertation research significantly predict doctoral students’ engagement with AI-assisted research; fourth, using AI in the early stages of research is beneficial to developing interdisciplinary competencies, particularly among doctoral students in the humanities and social sciences, whereas using AI directly for text writing may hinder academic training. Based on these findings, this study makes the following recommendations: construct interdisciplinary academic training modes that integrate "Humanities and Social Sciences + AI", enhance the normative guidance of AI-assisted research in the SEAM, and promote AI empowerment in doctoral education in ways that align with disciplinary cultures. |
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