Constructing China's Autonomous Knowledge System in the Era of Artificial Intelligence
人工智能时代中国自主知识体系建构
Source: Study Times (学习时报), May 25, 2026, page 1 | Author: Zhang Donggang (张东刚), Party Secretary of Renmin University of China
Original: https://paper.studytimes.cn/cntheory/2026-05/25/content_9961688.html
At present, the tide of intelligent technology represented by generative artificial intelligence is penetrating all spheres of human society with unprecedented breadth and depth, triggering profound transformations in productive forces and relations of production. General Secretary Xi Jinping has profoundly pointed out that "philosophy and the social sciences are important instruments by which people come to understand and transform the world, and an important force for driving historical development and social progress." Artificial intelligence, as the core driver of this transformation, in combining with philosophy and the social sciences, goes beyond the mere instrumental application of a tool and reaches into the deep structure of research paradigms, the logic of knowledge production, and the academic ecology. This requires us to grasp deeply the dialectical unity of instrumental rationality and value rationality, and to grasp deeply the inner logic of "two-way empowerment and mutual shaping" between the two: on the one hand, consciously employing artificial intelligence, this revolutionary force, to provide entirely new energy for constructing China's autonomous knowledge system; on the other hand, leading the healthy development of artificial intelligence through philosophy and the social sciences, ensuring that it serves Chinese modernization and the great practice of creating a new form of human civilization.
当前，以生成式人工智能为代表的智能技术浪潮，正以前所未有的广度与深度渗透至人类社会各领域，引发生产力与生产关系的深刻变革。习近平总书记深刻指出，“哲学社会科学是人们认识世界、改造世界的重要工具，是推动历史发展和社会进步的重要力量”。人工智能作为这一变革的核心驱动力，与哲学社会科学的结合，超越了简单的工具性应用，触及研究范式、知识生产逻辑与学术生态的深层结构。这要求我们深刻理解工具理性和价值理性的辩证统一，深刻把握二者“双向赋能、相互塑造”的内在逻辑：一方面，自觉运用人工智能这一革命性力量，为建构中国自主的知识体系提供全新动能；另一方面，以哲学社会科学引领人工智能的健康发展，确保其服务于中国式现代化与创造人类文明新形态的伟大实践。
Looking across the history of human civilization, every epoch-making technological revolution has not only been a leap forward in productive forces, represented by the means of production, but also a profound transformation in the way of thinking by which the world is understood and transformed. Seen from a Marxist viewpoint, artificial intelligence, as one of the most active and most penetrating forms of contemporary "general intellect," represents a qualitative breakthrough in productive forces. This leap will inevitably require and drive philosophy and the social sciences, as an important component of the superstructure, to undergo systematic and structural reshaping in their research presuppositions, methodological pathways, and evaluation standards.
纵观人类文明史，每一次划时代的技术革命，都不仅是以生产工具为代表的生产力飞跃，更是认识世界、改造世界的思维方式的深刻变革。从马克思主义的观点看，人工智能作为当代最活跃、最具渗透性的“一般智力”形态之一，代表着生产力的质变性突破。这种跃升必将要求并推动作为上层建筑重要组成部分的哲学社会科学，在研究预设、方法路径、评价标准等方面，发生系统性、结构性的重塑。
Expanding cognitive frontiers and reshaping research paradigms. A research paradigm is the basic beliefs, theoretical framework and methodological norms commonly followed by a specific academic community in a given historical period; it constitutes the deep foundation of knowledge production and theoretical construction. The transformation of a paradigm typically precedes the reconstruction of the knowledge system and provides preconditions for it. Therefore, analyzing the influence of artificial intelligence on research paradigms is the logical starting point for understanding how it empowers the construction of an autonomous knowledge system. From the philosophical foundations, the reshaping of research paradigms by artificial intelligence covers three levels: ontology, epistemology and methodology. At the ontological level, AI is driving structural change in the picture of social reality, giving rise to new social realities urgently in need of being understood; the objects of study are no longer confined to human behavior in physical space, but extend to such new types of social phenomena as human-machine interaction, algorithm-driven behavior, and data trails. At the epistemological level, AI introduces new modes of cognition such as data-driven inquiry, pattern recognition and correlational analysis, allowing knowledge to "emerge" from massive data and challenging the traditional credo that there can be "no cognition without theory." At the methodological level, AI greatly extends the spectrum of research tools and methods available to philosophy and the social sciences; new approaches such as computational social science, generative social simulation, natural language processing and graph-learning models are rising rapidly, complementing and converging with traditional qualitative and quantitative methods.
拓展认知疆域，重塑研究范式。研究范式是特定学术共同体在某一历史时期所共同遵循的基本信念、理论框架与方法规范，构成知识生产与理论建构的深层基础。范式的转型往往先行于知识体系的重构，并为后者提供前提性条件。因此，剖析人工智能对研究范式的影响，是理解其赋能自主知识体系建构的逻辑起点。从哲学基础来看，人工智能重塑研究范式涵盖本体论、认识论与方法论三个层面。在本体论范式上，人工智能推动社会实在图景发生结构性变化，催生了新的、亟待认识的社会实在，研究对象不再局限于物理空间中的人类行为，而是扩展至人机交互、算法驱动、数据留痕等新型社会现象。在认识论范式上，人工智能通过引入数据驱动、模式识别、关联分析等新型认知方式，使知识可以从海量数据中“涌现”出来，挑战了“无理论不认知”的传统信条。在方法论范式上，人工智能极大拓展了哲学社会科学的研究工具与方法谱系，计算社会科学、生成式社会模拟、自然语言处理、图学习模型等新型研究方法迅速兴起，与传统定性、定量方法形成互补与融合。
Renewing modes of cognition and transforming knowledge production. Knowledge production is the creative activity by which human society cognitively processes the objective world and produces systematized knowledge; its scale, speed and quality directly determine the completeness of the knowledge system and its capacity for renewal. From the standpoint of historical evolution, the knowledge production of traditional philosophy and the social sciences has chiefly taken the form of an individualized "handicraft workshop" model: scholars draw on personal scholarly accumulation, raise questions, form views and produce books through such means as document study and field research; knowledge dissemination depends on journals, books and the classroom; knowledge acquisition takes place chiefly within the academic community; and the whole process is marked by long cycles, high thresholds and narrow audiences. The rise of artificial intelligence is driving knowledge production to transform in the direction of intelligence, collaboration and universal access. In the question-raising stage, AI greatly enhances the capacity to discover problems and generate hypotheses, providing data-driven topic suggestions to researchers through large-scale literature mining, analysis of academic trends and monitoring of social public opinion. In the dissemination stage, AI uses intelligent recommendation systems, academic knowledge graphs and multilingual translation tools to deliver knowledge instantly, match it precisely, and disseminate it without barriers. In the reception stage, AI provides intelligent abstracts, knowledge Q&A, related-content recommendations, interactive learning and other new modes of reception, enabling researchers to grasp the contours of knowledge quickly and locate core viewpoints precisely, accelerating internalization, transformation and the reproduction of new knowledge.
革新认知方式，变革知识生产。知识生产是人类社会对客观世界进行认知加工、形成系统化知识成果的创造性活动，其规模、速度与质量直接决定着知识体系的完备程度与更新能力。从历史演进来看，传统哲学社会科学的知识生产主要采取个体化的“手工作坊”模式，学者凭借个人学养积累，通过文献研读、田野调查等方式提出问题、形成观点并著书立说，知识传播依赖于期刊、著作与课堂，知识获取主要发生在学术共同体内部，呈现出周期长、门槛高、受众窄等特征。人工智能的兴起正在推动知识生产向智能化、协同化、普惠性方向转型。在知识的提出环节，人工智能极大地增强了问题发现与假设生成的能力，通过大规模文献挖掘、学术趋势分析和社会舆情监测，为研究者提供数据驱动的选题建议。在知识的传播环节，人工智能利用智能推荐系统、学术知识图谱与多语言互译工具，实现了知识的即时推送、精准匹配与无障碍传播。在知识的接受环节，人工智能通过提供智能摘要、知识问答、关联推荐、交互式学习等新型接受方式，使研究者能够快速把握知识脉络、精准定位核心观点，从而加速知识的内化、转化与新知识的再生。
Restructuring the academic ecology and rebuilding the evaluation system. The academic evaluation system is the set of standards and methods for making value judgments about research outputs, research personnel and academic institutions in philosophy and the social sciences; in the construction of the knowledge system, it plays the key role of "baton" and "weathervane." The academic evaluation system sets the criteria for judging what counts as "good research," "real questions" and "high-level results," and subtly guides researchers' choice of topic, methodological orientation and value pursuits, thereby profoundly shaping path selection and resource allocation in the construction of the knowledge system. Under the empowerment of AI, the academic evaluation system displays three distinct features. First, the subjects of evaluation are diversifying. Intelligent algorithms can carry out automated normative checks, innovation assessment and impact measurement of research outputs, forming a model of human-machine collaborative evaluation. Second, the dimensions of evaluation are converging. Through automated collection and intelligent analysis of massive data, the academic innovation, social influence, cultural-transmission power and international visibility of research outputs can be considered together, offering a technical possibility for overcoming the abuses of "judging an article by the journal" and "judging quality by the numbers." Third, the evaluation process is becoming dynamic. By tracking research progress and interim results in real time, researchers receive continuous suggestions for improvement; at the same time, evaluation data can accumulate into a scholar's "academic growth file," providing more three-dimensional and longitudinal process support for personnel evaluation.
重构学术生态，重建评价体系。学术评价体系是对哲学社会科学研究成果、研究人才及学术机构进行价值判断的标准与方法集合，在知识体系建构中发挥着“指挥棒”与“风向标”的关键作用。学术评价体系设定何为“好研究”“真问题”“高水平成果”的判断标准，潜移默化地引导着研究者的选题方向、方法取向和价值追求，从而深刻影响着知识体系建构的路径选择与资源分配。在人工智能赋能下，学术评价体系呈现出三个鲜明特征。一是评价主体多元化。智能算法可以对研究成果进行自动化的规范性检测、创新性评估和影响力测算，形成人机协同的评价模式。二是评价维度融合化。通过对海量数据的自动采集与智能分析，综合考量研究成果的学术创新性、社会影响力、文化传承力以及国际能见度等多个维度，为克服“以刊评文”“以数论质”的弊端提供了技术可能。三是评价过程动态化。通过实时追踪研究进展及阶段性成果，为研究者提供持续的优化建议；同时，评价数据可以累积形成学者的“学术成长档案”，为人才评价提供更加立体、长时段的过程支撑。
While artificial intelligence empowers innovative development and stimulates paradigm change, it also conceals risks and challenges that cannot be ignored. For example, dependence on algorithms may weaken the subjectivity of researchers; abuse of intelligently generated content may erode the spirit of academic originality; and differences in technical thresholds may exacerbate the "Matthew effect" in the distribution of academic resources. Therefore, AI's empowering role must serve the core goal of constructing an autonomous knowledge system, ensuring that the empowerment proceeds steadily, securely and under control. In essence, an autonomous knowledge system intrinsically contains construction standards of systematization, scholarly grounding, and the establishment of signature concepts. Systematization is the precondition: without it, knowledge is fragmentary and scattered, unable to form theoretical synergy. Scholarly grounding is the core: without it, the system is a hollow shell with form but no soul, lacking depth of thought. The establishment of signature concepts is the marker: without it, scholarly outputs struggle to have influence in domestic and international academic arenas. Under the conditions of deep AI involvement, systematization, scholarly grounding and signature concepts have all acquired new requirements for the times and display new developmental features.