«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

清华大学教育研究[ISSN:1001-4519/CN:11-1610/G4]

卷:

期数:

2025年01期

页码:

68-78

栏目:

人工智能与教育

出版日期:

2025-02-20

文章信息/Info

Title:

Enabling-Oriented Risk Regulation for the Early Application of Generative AI: A Case Study of Higher Education

作者:

李焕宏¹ 薛澜²

1.南京大学 政府管理学院;2.清华大学 公共管理学院

Author(s):

LI Huan-hong¹ XUE Lan²

1.School of Government, Nanjing University;2.School of Public Policy and Management, Tsinghua University

关键词:

风险管理; 组织适应; 技术风险; 制度创新

Keywords:

risk management; organizational adaptability; technological risk; institutional innovation

分类号:

G434

文献标志码:

A

摘要:

尽管学术界已经广泛讨论了生成式人工智能技术的风险,但对于“技术的正负面影响取决于使用行为”这一观点的认识仍然不足,这限制了切实可行的风险规制方案的提出。研究表明,引导行为的关键在于全面理解人工智能的技术和业态的整体图景,以及洞察不同风险受体的具体处境。本研究以生成式人工智能在高等教育机构中的应用场景为例,发现技术应用风险构成了一个复杂的多层次体系。这些风险不仅源自技术本身,还包括人际竞争、新旧制度摩擦等由社会互动引发的复杂冲击。基于此,本研究提议构建一个使能型风险规制体系,重点在于提升高等教育机构及其师生的技术能力,并完善教育转型所需的制度配套,以增强组织的韧性。这样的体系旨在引导高等教育机构找到既高效又安全的技术应用路径。本研究强调,在新兴技术应用的早期阶段,培养技术驾驭能力对于风险规制的成效至关重要。

Abstract:

While the risks associated with generative AI technologies have been extensively discussed in academic circles, there is still a lack of sufficient understanding that “the positive or negative impacts of technology are contingent on usage behavior,”which constrains the development of feasible risk governance strategies. This study argues that effectively guiding behavior requires a holistic understanding of the landscape of AI application risks—including the technology itself, its uses, and its broader impacts—and insight into the specific contexts faced by different risk receptors. Using the case of generative AI applications in higher education, this research identifies a complex, multi-layered risk framework in educational AI applications. These risks arise not only from the technology but also from complex social dynamics, such as interpersonal competition and friction between new and existing institutional structures. Accordingly, the study proposes an enabling-oriented risk regulation framework that emphasizes strengthening the technical capacity of higher education institutions and establishing institutional support systems essential for educational transformation. This approach aims to enhance organizational resilience and guide higher education institutions toward safe and effective pathways for AI adoption. The study underscores that, in the early stages of emerging technology applications, building capacity for technology stewardship is crucial to the success of risk regulation efforts.

常用功能

更新日期/Last Update: 2025-02-20