发表机构
Digital Ethics Center, Yale University（耶鲁大学数字伦理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
探讨生成式人工智能侵蚀学术判断形成和学术信任建立的实践致研究退化，指出仅让人参与AI输出流程不足，需重归将研究作为实践的承诺，提出第二种学术研究。
AI 中文摘要
我们认为生成式人工智能会侵蚀学术判断形成和学术信任建立的实践，导致研究退化。当研究者将核心探究任务交给大语言模型等系统时，可能不再践行这些实践，虽AI生成的单个研究成果可能改善，但研究者自身无法发展。仅让人类参与AI输出不足以维护研究，需重归将研究作为实践的承诺，我们捍卫基于四种非自动化来源和依据的第二种学术研究。
英文摘要
We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed. As constitutive conditions for the production and validation of knowledge, these practices cannot be reduced to the outputs of research, which is what AI tools so effectively simulate. When scholars delegate central tasks of inquiry to Large Language Models (LLMs), they may stop engaging in these practices and lose the formation they provide: the output may improve while the researcher fails to develop. Against this risk, merely keeping humans "in the loop" as prompters or quality checkers of AI outputs is insufficient to preserve research as a site of intellectual formation. What is needed instead is a renewed commitment to research as a lived practice in which judgement is formed gradually, often through friction, and participation in a scholarly community. We defend it because it rests on four sources and warrants of research that cannot be automated: tacit knowledge, personal commitment, socialisation, and deep reading. These enact what we call second scholarship, i.e., the reappropriation of scholarly craft, chosen out of a critical experience of what AI tools can and cannot do. Researchers who practise it may delegate tasks to these tools, provided that their own judgement in the delegated competence is already formed, that they can answer for the output, and that they keep exercising the competence themselves. What cannot and should not be delegated becomes what research communities must value and answer for. This is what is left for us.
