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科学研究智能化转型：基于AI的新范式及其深远影响

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朱鹏飞 , 姚鑫杰 , 姜国崧 , 范妍 , 曹海芳 , 高西远 , 徐兴歆 , 陶柏安 , 李维浩 , 武嘉和 , 胡清华

科技导报 | 科技评论 2025,43(18): 16-22

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科技导报 |科技评论 2025 , 43 (18) : 16 -22

科学研究智能化转型：基于AI的新范式及其深远影响

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朱鹏飞, 姚鑫杰, 姜国崧, 范妍, 曹海芳, 高西远, 徐兴歆, 陶柏安, 李维浩, 武嘉和, 胡清华

作者信息

天津大学智能与计算学部，天津 300072

The intelligent transformation of scientific research: A new paradigm driven by artificial intelligence and its profound impact

Pengfei ZHU, Xinjie YAO, Guosong JIANG, Yan FAN, Haifang CAO, Xiyuan GAO, Xingxin XU, Boan TAO, Weihao LI, Jiahe WU, Qinghua HU

Affiliations

College of Intelligence and Computing, Tianjin University, Tianjin 300072, China

出版时间: 2025-09-28 doi: 10.3981/j.issn.1000-7857.2025.03.00022

文章导航

摘要

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人工智能（AI）的快速发展正在推动科学研究迈向以“智能驱动”为核心的第5科研范式，这是继经验、理论、计算与数据驱动之后的新一轮科研模式变革。回顾了科研范式的历史演进，分析了AI推动科研从“人类主导”向“人机协同”的转型逻辑，论述了大模型时代的发展与现状，并探讨了AI在科研管理、科学假设生成、论文与项目中的应用场景。研究发现，AI不仅能够重塑科研流程和提升科研效率，而且能够推动跨学科知识重组与创新，逐步形成以大模型和生成式AI为核心的智能科研生态，同时也带来了模型幻觉、可解释性不足和伦理风险等问题。基于此，提出构建可信科研AI体系，推进人机协同创新模式，完善科研治理与政策框架，加强跨学科融合，并健全科研伦理与社会责任机制，以确保AI驱动科研范式的可持续发展与有序演进。

关键词

科研范式 / 大模型 / 生成式AI / 跨模态智能系统

Abstract

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The rapid development of artificial intelligence (AI) is propelling scientific research toward the fifth paradigm, characterized by intelligence−driven inquiry, following the earlier paradigms of experiment, theory, computation, and data−driven science. This paper reviews the historical evolution of research paradigms, analyzes the logic of transformation from "human−centered" to "human–AI collaborative" research, discusses the development and current state of the large model era, and examines the applications of AI in research management, hypothesis generation, and academic writing and projects. The findings indicate that AI not only reshapes research processes and improves efficiency but also facilitates cross−disciplinary knowledge recombination and innovation, gradually forming an intelligent research ecosystem centered on large models and generative AI. At the same time, challenges such as hallucinations, poor interpretability, and ethical risks cannot be overlooked. Accordingly, this paper proposes building a trustworthy AI system for scientific research, advancing innovative models of human–AI collaboration, improving governance and policy frameworks, strengthening interdisciplinary integration, and enhancing mechanisms of research ethics and social responsibility, in order to ensure the sustainable development and orderly evolution of AI−driven research paradigms.

Key words

scientific research paradigm / foundation models / generative AI / cross−modal intelligent systems

引用本文

朱鹏飞, 姚鑫杰, 姜国崧, 范妍, 曹海芳, 高西远, 徐兴歆, 陶柏安, 李维浩, 武嘉和, 胡清华. 科学研究智能化转型：基于AI的新范式及其深远影响. 科技导报, 2025 , 43 (18) : 16 -22 . DOI: 10.3981/j.issn.1000-7857.2025.03.00022

Pengfei ZHU, Xinjie YAO, Guosong JIANG, Yan FAN, Haifang CAO, Xiyuan GAO, Xingxin XU, Boan TAO, Weihao LI, Jiahe WU, Qinghua HU. The intelligent transformation of scientific research: A new paradigm driven by artificial intelligence and its profound impact[J]. Science & Technology Review, 2025 , 43 (18) : 16 -22 . DOI: 10.3981/j.issn.1000-7857.2025.03.00022

正文

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1 AI驱动的第5科研范式

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1.1 科研范式的历史演进

科学研究的演进从未停歇。Jim Gray提出的科研范式框架^[1]指出，传统科研经历了4次主要的范式跃迁：经验驱动范式、理论驱动范式、计算驱动范式和数据驱动范式，每一次变革都突破了人类认知边界。而当下AI技术正催化第5范式的诞生，推动科研逐步迈向AI驱动的第5科研范式（图1）^[2]。

1.2 科研范式进步的原因

在迈向第5范式的转型过程中，理论数学、量子物理学、神经科学及语言学等领域推动了AI技术的发展（图2）。科研范式转变的背后是算力的提升、AI技术的发展、AI for Science（AI4S）与AI for Research（AI4R）的兴起以及大模型的推动。

1.3 第5科研范式的界定

第5科研范式是以人工智能为核心驱动力，实现科研全流程智能化重构的研究体系，体现如下。（1）主体转变：从人类主导到人机协同，形成“人类直觉+机器智能”的双引擎驱动。（2）流程重构：打破传统线性研究路径，构建“假设生成—实验设计—验证迭代”的智能闭环。（3）知识生产：基于大模型的涌现能力实现跨学科知识重组，突破人类认知局限与领域知识壁垒。

1.4 AI驱动的科研流程重构

1）假设生成。AI模型通过跨学科知识融合突破传统思维局限，通过知识重组，将离散的科研发现编织成创新理论框架，使假设生成从依赖灵感的偶然行为转变为系统性知识涌现的科学过程。AI自动生成假设，并通过计算模拟进行初步验证。例如，A−Lab系统^[3]基于海量交叉学科文献数据，AI假设合成配方，合成41种新材料。

2）实验设计。AI构建数字实验系统，进行虚实融合的智能优化。AI模型将物理约束编码为多维参数空间，在虚拟环境中并行探索数百万种实验方案，动态优化实验设计。谷歌RoboChem平台^[4]在有机合成实验中自主完成超过6000次反应条件优化。

3）验证迭代。AI模型对海量数据进行自动化处理、分析和优化，建立“理论—实验—数据”的三角验证体系，构建动态“评估—反馈”的强化学习闭环，进行假设的自我证伪机制。AI模型采用主动学习策略，使科研验证从单次确认升级为持续认知迭代的智能进化系统。

AI驱动的第5科研范式不仅体现在技术手段的革新上，更在于对整个科研生态的重构。由此形成的新型科研环境，将引发科研模式的变革，重塑科学研究的格局。

2 百花齐放的大模型时代

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2.1 大模型发展历程

人工智能自1956年达特茅斯会议诞生以来，经历了“三起两落”——3次技术高峰与2次低谷交替（图3）。从早期符号逻辑的逻辑主义，到神经网络与专家系统，再到深度学习与大数据驱动的变革，每次浪潮均推动技术突破，同时暴露方法和应用局限，引发研究热情波动。

21世纪，尤其是2014—2024年，生成式AI快速发展。深度学习生成模型在图像、语音、文本等领域取得突破，催生多样创新应用，既延续了技术积累，又为人工智能带来新的活力与可能（图4）。

2.2 大模型百花齐放

大模型发展迅猛，尤其是大语言模型（LLM）推动了文本生成和对话系统的变革，并自然延伸至多模态领域。2022年底ChatGPT问世，标志生成式AI新高峰，随后国内外LLM竞相涌现，呈现百花齐放局面（图5）^[5]。

综合来看，当前的大模型能力存在但不局限于以下方面。（1）语言理解与生成：大模型能够精准理解复杂语言结构和语义。（2）逻辑推理与交互：大模型能够根据输入的信息进行归纳推理、演绎推断，完成复杂的多步推理任务。（3）知识存储与检索：大模型可以高效地从庞大的知识库中提取相关信息，精准地回答问题。（4）视觉与多模态：大模型的视觉理解能力使其能够精准解析图像内容，进行物体识别、语义分割等任务（图6）。

2.3 大模型的能力与不足

大模型通过其强大的能力为各种复杂任务提供了支持。然而，大模型仍面临其特有的局限性与伦理问题（图7）^[6]。

1）幻觉现象。LLM的幻觉是指模型生成的内容可能与实际情况不符或出现逻辑上的错误。

2）可解释性差。可解释性差意味着无法理解模型是如何从输入数据中提取特征，以及如何根据这些特征作出预测。

3）高级推理能力弱。LLM在涉及需要推理和抽象思维的复杂任务时，它们显得力不从心。

4）伦理问题。LLM可能在无意识间吸收并传播有害内容，使输出结果带有倾向性、不公平性。

展望未来，大模型将持续引领人工智能的革命，推动各行各业的深度发展。在科研领域，生成式AI（如GPT−4、Gemini）已逐渐成为学术研究的重要工具，极大提升了科研效率与创新能力。

3 大模型辅助的智能科研管理

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传统科研管理受经验依赖、学科壁垒和人力密集限制。AI大模型作为第5科研范式核心，推动科研管理变革，形成“AI4X”与“X4AI”双向赋能：AI技术渗透学科管理，学科反馈推动AI迭代，构建多学科融合的智能管理网络（图8）。

1）科研管理建议：从“经验”建议到“智能”建议。（1）校级层级（战略智能）：通过数字孪生校园平台模拟科研资源配置方案，动态优化“双一流”学科布局，实现了科研资源与学科发展的精准匹配。（2）院系层级（战术智能）：基于资源智能配置算法预测学科趋势，开发ESI学科阈值预警系统，提前预判学科排名变化，避免资源错配。（3）部门层级（操作智能）：利用多智能体仿真构建科研动态平衡算法，提升突发任务响应效率。通过数字沙盘模拟科研成果转化路径，缩短技术落地周期。

2）科研平台建设：从“定制”建设到“智能”建设。（1）跨学科知识装配引擎：通过DeepSeek模型^[⁷^]，不同领域的知识可以无缝衔接，为科研团队提供快速解决方案。（2）动态数据湖：Gemini多模态系统可同时处理文本、图像、音频输入，实时生成实验方案并优化步骤，加速复杂实验进程。（3）智能实验工坊：AI大模型还可以为复杂实验方案的自动生成与优化提供帮助，避免了繁琐的手动干预。

3）前瞻科研布局：生成式AI的预见性推演。（1）开设复合课程：推动AI与各学科的深度融合，培养复合型创新人才^[8]。（2）校企联合培养：共同开展AI大模型的研究与人才培养，助力高校优化教学体系。（3）攻关核心技术：通过人工智能创新与产业研究院，专注于AI技术的原创研发和应用转化，服务国家战略需求。

4）科研人才管理：从“师徒制”管理到“全球化”管理。（1）构建动态图谱：构建全球顶尖学者动态图谱，实现人才引进的智能匹配与风险评估。（2）团队智能诊断：GPT−4驱动的智能诊断系统自动生成包含知识结构补全方案的人才重组建议。（3）打造产学研人才旋转门：通过华为联合实验室实现工业界专家与学术人才的动态流转，缩短技术转化周期^[9]。

随着具身智能、量子计算与AI融合，科研管理将进入“人机共智”时代。生成式AI不仅重构管理流程，更催生持续进化的智能生态，引领人类探索认知边界的新纪元。

4 大模型辅助的科学假设生成

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1）人类的创造力密码。过去几十年里，科学哲学、心理学和相关领域的研究者一直关注创造力的来源，试图找出涌现智慧背后的机制。以知识重组理论为基石的研究揭示，跨领域知识的碰撞往往能催生超线性价值^[10]。例如，中国科学家将稀土氧化物防护技术与铂基催化材料结合，研发出全球首个兼具超高活性和稳定性的甲醇制氢催化剂，改写“活性与寿命不可兼得”的百年魔咒。

2）打破传统创新瓶颈。在经验驱动的科研模式中，研究者需耗费大量时间来构建学科知识体系，凭借个体智慧提出假设并实验验证。这种“单线程”研究流程面临双重困境：人类知识储备难以突破学科壁垒，而创造性思维的随机性更导致研究进程充满不确定性和风险。近几年更多研究通过AI驱动的数据整合打破专业壁垒，加速创新性想法建立与迭代。例如，《Nature》的研究展示了AI如何开辟发现抗生素的全新途径，实现耐药性细菌的重要突破^[11]。

3）构建人机协作模式。为了更好地利用大模型的潜力实现具备创新性和可行性的科学假设生成，大量研究提出人机协作的科学探索模式，引领科学探索更加精准地实现系统性突破。通常由5个步骤实现：问题定义与领域聚焦—文献搜索与关键背景知识获取—基于科学知识库生成假设—强化学习迭代细化假设—假设评估（图9）。

4）生成式AI助力学术创造。科研变革动力的讨论主要集中在算力提升与深度学习技术上，较少涉及生成式AI和跨模态智能系统在科研辅助过程中的应用。在材料与生命科学等领域（图10），以AlphaFold3和GNoME为代表的生成式大模型凭借并行处理数据能力，以及主动学习和迭代优化策略，显著加快了科研进程，推动新药研发周期从10 a缩短至2~3 a。《Nature Methods》的一项研究发现前沿LLMs，如GPT2.0，在基因功能发现中不仅能够提供与现有数据库一致的基因功能描述，还在某些情况下提出了更广泛合理的生物学解释^[12]。Gemini 2.5 Pro内置思维链机制革新了深度推理架构，其在分子动力学、天文数据等前沿领域的应用正在推动 AI 从工具属性向科研合作者跃迁。

人类智慧与机器计算智能深度耦合的研究范式已成为不可逆的学术革命。研究者亟需构建“领域特异性认知+机器泛化推理”的双引擎驱动模式——通过领域知识引导下的参数微调，实现假设空间的定向拓展，高效构建可行假设，加速跨学科创新进程，开拓科研新境界。

5 大模型辅助的论文与项目

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随着人工智能技术的快速发展，尤其是大模型技术的崛起，科研界和工业界正逐步将其应用于科研工作的各个环节。大模型不仅显著提升了科研效率，也为学术交流和跨学科合作注入了新的活力^[13]。

1）提高科研效率。在论文写作中，大模型在文献综述、数据分析与写作辅助等方面表现出色。其可自动检索并总结文献，帮助科研人员迅速把握领域前沿，尤其在跨学科研究中，可以提供融合多领域知识的全面视角^[14]。同时，大模型亦能协助实验设计、构建研究框架与提出创新假设，推动科研高效进展。

2）高效推动申请书撰写。在科研项目申请中，大模型为撰写提供智能化支持。以基金申请书为例，其可依据申请要求优化结构与内容，增强逻辑性与表达清晰度，助力突出研究重点、提升选题创新性与可行性，并减少重复内容。这种辅助显著提高了申请书撰写效率与系统化水平，进而增加申请成功率。

3）学位论文写作支持。学位论文体现学生的学术能力，大模型可在文献综述、方法论与结果讨论等环节提供专业支持。借助自动化摘要与内容整理，学生能更高效地梳理研究内容、构建论文框架，并提升语言表达与整体质量，从而缩短写作时间，更好地符合学术规范与评审要求。

4）智能化论文与项目评审。面对科研项目与论文数量的快速增长，传统人工评审模式在效率与质量上面临挑战。大模型技术通过智能筛查与分析，能基于创新性、技术路线等核心指标生成评审建议，提升公正性与准确性，减少人为偏见。同时，其还可承担初步筛选任务，优化评审资源分配，增强整体评审效率^[15]。

5）生成式AI与跨模态智能的应用前景。随着生成式AI（如GPT−4、Gemini）与跨模态智能系统的进步，科研辅助正由传统深度学习向复杂跨模态数据处理拓展。生成式AI在自然语言生成与理解之外，还能融合多模态信息，为科研提供更全面支持。在跨学科合作中，其推动信息融合与智能推理，助力实现科研创新突破。

大模型技术显著提升了科研效率和质量，随着生成式AI与跨模态智能的进一步发展，科研领域的智能化水平将持续提高，为学术创新和跨学科协作提供更广阔的空间。

6 未来科研范式展望

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生成式人工智能的崛起与跨模态智能系统的快速发展，为科研范式带来了深远的变革。这些先进技术跨越了文本、图像与数据之间的模态界限，高效地实现知识的智能检索与推理，辅助研究人员在多个科研环节中实现深度交互与协同创新。其中，“黑灯实验室”是一种近年来兴起的、融合AI与自动化技术的新型生化实验室模式，具备在无光、无人环境下由机器人和AI系统自主完成实验设计、数据采集和结果分析等全流程科研任务的能力。未来，这种模式有望实现全流程人机协同闭环，并广泛应用于各科研领域。

科学问题就是建模问题。纵观人类的科学历史，就是从简单建模走向复杂建模的过程。从古希腊数学研究简单的三角关系开始，到现代生命科学研究序列长度达到万级别的蛋白质结构，到现代宏观经济学研究由上亿人口组成的社会的经济结构，到现代天文学研究由无数星系团组成的超大尺度宇宙结构。未来更加复杂系统的建模势必会对人类专家有限的科研精力和科研速度带来非常严峻的挑战。而AI以其海量的知识储备，卓越的计算能力和智能水平，可以将人类专家从底层的推理设计实验中解放出来，从而更专注于顶层的科学问题凝练和科研路线把握。与此同时，科学家需要对AI的过程和产出的规范性、合法性和安全性保持足够的警惕。AI的滥用也会对科研，甚至社会造成严重的威胁。AI是一把双刃剑，广大科研工作者需要学会用并且用好AI，科学未来才能走上新的高速路。

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2025年第43卷第18期

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doi: 10.3981/j.issn.1000-7857.2025.03.00022

接收时间：2025-03-07
首发时间：2025-12-18
出版时间：2025-09-28

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收稿日期：2025-03-07
修回日期：2025-05-06
录用日期：2025-09-08

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天津大学智能与计算学部，天津 300072

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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