Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction

Computer Science > Artificial Intelligence arXiv:2610.36116 (cs) [Submitted on 26 Oct 2026] Title: Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction Authors: Hoda Heidari, Aydin Mohseni.

计算机科学 > 人工智能 arXiv:2610.36116 (cs) [提交于 2026 年 10 月 26 日] 标题：当人工智能更胜一筹时，为何以及如何进行科学研究：一个基于人类理解与导向的框架 作者：Hoda Heidari, Aydin Mohseni。

Abstract: As AI begins to match or exceed human researchers at core scientific tasks, two questions become pressing: \emph{why} should humans keep doing science, and \emph{how} should they use AI when they do? This paper argues that the second can be answered only by answering the first. It holds that one indispensable aim of science is \emph{durable, collective human understanding of the world, pursued in service of human ends and values}.

摘要：随着人工智能在核心科学任务上开始匹配甚至超越人类研究人员，两个问题变得紧迫：人类为什么要继续从事科学研究？在进行研究时又该如何使用人工智能？本文认为，只有回答了第一个问题，才能回答第二个问题。文章主张，科学的一个不可或缺的目标是“对世界进行持久的、集体性的人类理解，并服务于人类的目的和价值观”。

From this principle it derives a two-part test for when a research task can be delegated to AI. The first part, \emph{human understanding}, requires that the scientific community remain able to explain, defend and contest the resulting claims, and retain the skills to do so. The second, \emph{human direction}, requires that judgments about which questions matter and what findings mean stay with accountable humans.

基于这一原则，文章推导出了一个两部分测试，用于判断何时可以将研究任务委托给人工智能。第一部分是“人类理解”，要求科学界必须保持解释、辩护和质疑研究结论的能力，并保留相应的技能。第二部分是“人类导向”，要求关于哪些问题重要以及研究结果意味着什么的判断，必须由负责任的人类来做出。

Applying the test to each phase of research in the Contributor Roles Taxonomy (CRediT) yields recommendations that fields can tailor to their questions and methods. The framework differs from existing guidance in three ways. It rests on an account of what science is for rather than on integrity norms. It holds even if AI outperforms human researchers, rather than depending on AI’s current limitations. And it judges each use of AI by its effects on human understanding and direction rather than by the type of tool, across every phase of research, including peer review and the training of new researchers.

将该测试应用于“贡献者角色分类法”（CRediT）中的每个研究阶段，可以得出各领域能够根据自身问题和方法进行调整的建议。该框架与现有指导方针有三点不同：它基于对科学目的的阐述，而非仅仅基于诚信规范；即使人工智能的表现优于人类研究人员，该框架依然适用，而不依赖于人工智能当前的局限性；它通过人工智能对人类理解和导向的影响来评估其每一次使用，而非根据工具类型，且涵盖了包括同行评审和培养新研究人员在内的所有研究阶段。

It closes with the reforms needed to keep science aimed at human understanding and direction.

文章最后提出了确保科学研究始终以人类理解和导向为目标的必要改革措施。