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| 生成式人工智能赋能研究生教育:理论逻辑、法律风险和治理路径 |
| Generative AI Empowering Postgraduate Education: Theoretical Logic, Legal Risks, and Governance Approaches |
| 投稿时间：2025-01-07 |
| DOI：10.19834/j.cnki.yjsjy2011.2025.02.03 |
| 中文关键词: 生成式人工智能;研究生教育;个性化学习;数据隐私;知识产权;学术诚信 |
| 英文关键词: generative AI;postgraduate education;personalized learning;data privacy;intellectual property;academic integrity |
| 基金项目: |
| 作者 | 单位 | | 杨清望 | 中南大学 法学院, 长沙 410083 | | 唐乾 | 中南大学 法学院, 长沙 410083 |
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| 中文摘要: |
| 生成式人工智能在研究生教育中展现出赋能潜力,推动了课程学习、科研、论文写作及学术交流等多个方面的创新。从理论逻辑出发,生成式人工智能基于建构主义理论,通过个体知识构建与社会互动的双重驱动,优化知识生成与共享模式;依托学习分析理论,提升了数据驱动的学习行为监测与动态优化能力;结合个性化学习理论,支持精准化学习路径优化,提升教育效率与公平性。然而,这些技术应用也带来了诸多法律风险,如数据隐私保护与个性化数据采集的冲突,知识产权归属不明与生成内容权属的矛盾,责任主体不清与法律责任划分困境,以及技术介入下学术诚信的挑战。为规避这些风险,应通过授权控制和数据最小化保障隐私;在知识产权治理中明确生成内容的权属规则;建立多主体责任分配机制,划定法律边界,并通过加强学术诚信机制与伦理框架维护教育秩序,以平衡技术创新与法律规范,推动研究生教育的可持续发展。 |
| 英文摘要: |
| Generative AI has significant potential to empower postgraduate education to drive innovation in course learning, scientific research, academic writing, and scholarly exchange. From the perspective of theoretical logic, generative AI is based on constructive theory and optimizes the knowledge generation and sharing mode through the dual drivers of individual knowledge construction and social interaction. It utilizes the theory of learning analysis to improve the ability of data-driven learning behavior monitoring and dynamic optimization. Combined with the theory of personalized learning, it supports the precise optimization of learning paths and improves the efficiency and equity of education. However, these technology applications also pose some legal risks, such as conflicts between privacy and personalized data collection, the contradiction between unclear ownership of intellectual property rights and ownership of generated content, the dilemma of unclear accountability and vague allocation of legal responsibility, and challenges to academic integrity when technology is applied. To address these risks, this paper suggests that we should protect privacy through authorization control and data minimization, clearly define the ownership of generated content in intellectual property management, establish a multi-party responsibility allocation mechanism, and delineate legal boundaries. In addition, we should maintain educational order by strengthening the academic integrity system and ethical framework, so as to make technological innovation compatible with legal norms and promote the sustainable development of postgraduate education. |
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