西安交通大学学报（社会科学版）2025，Vol.45Issue(3)：117-128,12.DOI:10.15896/j.xjtuskxb.202503010
AI for Science:认知性协作、全过程效应与行动领域
AI for Science:Cognitive Collaboration,Lifecycle Impacts,and Action Areas
摘要
Abstract
The high-quality development of AI for Science is of crucial strategic significance for accelerating the realization of high-level scientific and technological self-reliance and self-strengthening,and for building a global science and technology powerhouse.This paper,based on the three core concepts of"AI"(Artificial Intelligence),"Science,"and"for"(empowerment),systematically explores the connotation and development strategies of AI for Science. Firstly,the role transformation of"AI"is understood from the perspective of the evolution of human-machine collaboration paradigms.By tracing the historical trajectory of human-machine collaboration in scientific knowledge production,we observe that the role of technology has undergone a phased progression from sensory collaboration,physical(somatic)collaboration,and computational collaboration to the current stage of cognitive collaboration.In the cognitive collaboration phase,AI transitions from a complex tool merely executing tasks to a cognitive partner actively participating in the research thinking process.This cognitive collaboration also exhibits a hierarchical nature:starting from accelerating traditional problem-solving,developing to proactive cognition and automated discovery where AI actively proposes hypotheses and designs experiments,and ultimately advancing to new heights of knowledge modeling where AI participates in constructing and refining scientific understanding itself.This signifies that AI is no longer confined to external empowerment but is deeply integrated into scientists'thought processes,establishing a close synergistic relationship at the cognitive level. Secondly,the scope expansion of"Science"is analyzed from the broad perspective of the entire research lifecycle.This paper advocates for moving beyond the narrow traditional focus on scientific discovery within laboratories to comprehensively examine the profound impact of AI on the entire research lifecycle,encompassing the whole process of knowledge production,dissemination,and management.In knowledge production,AI empowers automated data acquisition and analysis,hypothesis generation and validation,research methodology optimization,and even academic writing assistance,with some emerging systems already demonstrating powerful potential for automated research.In terms of knowledge dissemination,technologies represented by generative AI are driving the democratization process of content creation,overcoming language and cultural barriers through advanced translation capabilities,and enabling personalized and precise dissemination of scientific knowledge,thereby profoundly transforming the scientific communication ecosystem.Furthermore,in research management,AI significantly enhances the level of intelligence and decision-making efficiency by introducing more comprehensive and refined evaluation metrics that go beyond traditional citations,and by conducting predictive analytics on research impact and potential breakthroughs,thus optimizing resource allocation. Lastly,from a holistic perspective of a techno-social system,this paper elaborates on how to effectively unleash the immense potential of AI"for"Science.AI is not an isolated technological tool but is deeply embedded within complex socio-technical systems.Therefore,to maximize the efficacy of AI for Science,the key lies in constructing an adaptive and vibrant research ecosystem.This requires synergistic advancement in key areas such as talent cultivation,research organization models,data resource construction,and ethical governance:continuously enhancing the comprehensive AI for Science competency of researchers,encouraging cross-disciplinary and inter-institutional collaborative innovation,promoting the open sharing and efficient utilization of high-quality research data,and establishing robust,forward-looking,and adaptive ethical norms and governance frameworks for science and technology.关键词
AI for Science/人工智能/人机协作/认知性协作/全过程科研/科技伦理治理/科研生态系统Key words
AI for Science/artifical intelligence/human-machine collaboration/cognitive collaboration/full research lifecycle/ethical governance of science and technology/research ecosystem分类
信息技术与安全科学引用本文复制引用
王硕,张徐姗,武晨箫,阎妍,李正风..AI for Science:认知性协作、全过程效应与行动领域[J].西安交通大学学报（社会科学版）,2025,45(3):117-128,12.基金项目
国家社会科学基金重大项目(21ZDA017) （21ZDA017）
国家自然科学基金专项项目(L2324114) （L2324114）
北京市科技战略决策咨询委员会战略咨询专项项目(E4391Z02). （E4391Z02）
