[Exclusive] Luo Yiwen: Paradigm Transformation and Normative Approaches to Marxist Research in the Digital-Intelligence Era
General Secretary Xi Jinping has profoundly noted: "History shows that eras of great social transformation must be eras of great development in philosophy and social sciences." The "Proposals of the Central Committee of the Communist Party of China on Formulating the 15th Five-Year Plan for National Economic and Social Development" propose leading the transformation of scientific research paradigms through artificial intelligence. The year 2026 marks both the beginning of the "15th Five-Year Plan" and the tenth anniversary of General Secretary Xi Jinping’s "May 17" important speech [1]. Standing at this historical juncture, the cause of philosophy and social sciences welcomes new development opportunities while shouldering even greater responsibilities of the times. Marxism is an open theoretical system that develops continuously alongside the evolution of the era, practice, and science; its vitality lies in its constant response to the questions of the age and its absorption of the finest achievements of civilization. In the face of a new round of technological revolution and industrial transformation, how can we maintain the scientific and revolutionary nature of Marxist theoretical research amidst technological empowerment? How can we uphold the seriousness of academic norms while innovating research paradigms? These are major issues that urgently require answers as we advance Marxist research during the "15th Five-Year Plan" period.
I. Opportunities of the Era: Digital-Intelligence Technology Empowering Theoretical Research
"The distinction between various economic epochs consists not in what is produced, but in how it is produced, and with what instruments of labor." As a new type of instrument of labor, digital-intelligence technology is profoundly changing the mode of academic production in Marxist theoretical research.
Text mining technology broadens the research horizon of classic texts. It is true that classic literature is the "source of living water" [2] for Marxist theory. However, traditional philological methods are limited by the processing capacity of individual researchers, making it difficult to grasp the evolution of the system of thought as a whole. By leveraging digital-intelligence technologies such as natural language processing, text mining, and semantic retrieval, researchers can achieve retrieval, comparison, and correlation analysis of classic texts on a much larger scale. For example, using the "Golden Code Semantic" (Jindian Yuanyi) search function of the CPC Ideological and Theoretical Resource Database, one can input "machines are not capital nor will they lose their use-value" with a 60% similarity threshold to link directly to the original statement on page 777 of Volume 2 of the Selected Works of Marx and Engels regarding how "when the system of machinery is no longer capital, it will not lose its own use-value." Based on this technology, researchers can not only precisely locate original sources through semantic correlation but also access citation version information, publication dates, and contextual environments on a single interface, greatly enhancing the efficiency and precision of textual research.
Computational social science provides new means for theoretical verification. In Capital, Marx revealed the law of the tendency of the rate of profit to fall. Yet, past research struggled to test this theoretical logic within the real world. Computational social science in the digital-intelligence era provides a digitized and virtualized experimental field for theoretical testing. For example, by introducing multi-agent modeling and simulation (MAS) technology, researchers can set up capital entities with differentiated characteristics in virtual space. Following the profit rate formula $P' = m / (c + v)$, and setting initial conditions where the rate of surplus value remains relatively stable while the organic composition of capital gradually increases, researchers can dynamically simulate the evolutionary trajectory of the general rate of profit. This technical method possesses advantages such as repeatability, adjustability, and low cost. It can simulate the generative conditions, operational logic, and evolutionary processes of theoretical propositions in virtual space. This not only helps deepen the understanding of the important conclusions of classic authors but also provides methodological support for grasping the new changes and characteristics of contemporary capitalism.
Generative artificial intelligence improves the efficiency of knowledge production. The widespread application of generative AI marks the entry of knowledge production into a new stage of "human-machine collaboration." AI can not only improve the efficiency with which researchers retrieve literature and extract summaries but also inspire innovative points through continuous dialogue. More importantly, AI can help researchers break down language barriers and disciplinary silos, allowing them to focus their energy on refining core viewpoints—truly "using the best steel for the blade of the knife" [3]. However, the advanced nature of technical tools does not directly equate to the scientific nature of theoretical output. While embracing the dividends of digital-intelligence technology, we must remain vigilant against the risks it may bring.
II. Multiple Academic Risks Brought by the Encroachment of Technical Rationality
General Secretary Xi Jinping has profoundly pointed out: "Artificial intelligence brings unprecedented development opportunities, but also brings unprecedented risks and challenges." For Marxist research, the encroachment of technical rationality has sounded the alarm in three dimensions: epistemology, ideology, and subjectivity.
First is the epistemological risk. Marx clarified that the essence of the dialectic is that "in its positive understanding of what exists, it includes in its understanding the negation of that existence." When discussing the behavior of machines imitating human complex computational actions, the British mathematician and logician Alan M. Turing pointed out: "If a machine is to imitate the behavior of a human computer in some complex operation, one must ask how the human does it, and then translate the answer into the form of an instruction table." This means that the operation of machine intelligence essentially converts complex behavior into executable programs. Digital-intelligence technology follows the formal logic of "A = A," pursuing certainty and linear causality. However, the essence of materialist dialectics emphasizes the dialectical movement of contradiction where "A is both A and not-A." If researchers rely excessively on algorithmic models, they often unconsciously use static mathematical functions to analyze socio-historical processes full of contradictory tension, thereby neglecting the laws of quantitative-to-qualitative change and the negation of the negation that drive social development. Furthermore, digital-intelligence technology often dismantles complex social organisms into isolated, calculable individual data points. This fragmented research severs the internal connection between data indicators and relations of production, class structures, and historical backgrounds, blocking the Marxist cognitive path from the abstract to the concrete, causing research to fall into an epistemological trap of "seeing the trees of data examples but missing the forest of historical materialism."
Second is the ideological risk. "It is not the consciousness of men that determines their existence, but their social existence that determines their consciousness." Digital-intelligence technology is not entirely value-neutral; its application in academic research is often accompanied by ideological risks. In December 2022, the independent US investigation website The Intercept disclosed that agencies under the US Department of Defense had intervened in the public opinion and perception of people in the Middle East through topic manipulation and deceptive propaganda on social media (Twitter, now X), continuously promoting narratives beneficial to the US and its allies. Taking recently popularized overseas open-source AI agents as an example: these tools possess strong autonomous decision-making and system-deployment capabilities. If introduced into the academic research process without discrimination, they may amplify risks such as technological dependence, data distortion, and value bias. Entrusting key research links—such as literature search, data scraping, and even viewpoint extraction—exclusively to overseas tools whose underlying logic, training corpora, and platform rules are non-transparent is tantamount to surrendering the power of academic filtering to their built-in mechanisms. This machine-proxied academic behavior can easily marginalize Marxist positions and viewpoints, continuously "feeding" researchers corpora with specific political orientations. Blindly relying on such tools preset with specific ideologies will not only lead to a gradual loss of political discernment but also directly threaten the security of the national ideological and academic spheres. Furthermore, algorithmic recommendation mechanisms based on user habits easily form closed "information cocoons." Long-term reliance on this method to obtain academic resources will narrow the researcher’s horizon and petrify their thinking.
Third is the subjectivity risk. Academic research is essentially a creative spiritual activity of human beings. With the popularization of AI writing tools, the phenomenon of "outsourcing thought" has begun to spread. Some researchers attempt to replace humanistic concern and dialectical thinking with machine computing power. In the long run, this will inevitably lead to the degeneration of researchers' independent thinking and theoretical innovation capabilities, rendering them adjuncts to technology. More importantly, the underlying logic of generative AI is probabilistic generation based on existing corpora; the authenticity of its output requires careful scrutiny. Currently published online literature varies in quality; during the generation process, AI can easily produce "machine hallucinations" that fabricate facts and forge citations. If cited without verification, this will not only trample the bottom line of academic truth-seeking—to "have a basis for every word"—but will fundamentally shake the scientific and normative nature of Marxist theory. Identifying risks is not for the sake of stopping; only by knowing where to stop can one proceed steadily. Only by clearly demarcating the boundaries of technical application can we truly master and correctly use these tools.
III. Establishing Marxist-Guided Norms for Technological Application
In the twentieth collective study session of the Political Bureau of the CPC Central Committee, General Secretary Xi Jinping emphasized: "We must grasp the development trends and laws of artificial intelligence, accelerate the formulation and improvement of relevant laws, regulations, policies, application norms, and ethical standards, and build a system for technical monitoring, risk early warning, and emergency response to ensure that artificial intelligence is safe, reliable, and controllable." This important discourse provides the fundamental guidance for us to respond to technological challenges and establish academic norms in the digital-intelligence era.
Uphold the guiding position of Marxism in technical applications. When the roots are firm, the flowers and fruit will flourish [4]. Marxism is the fundamental guiding ideology upon which our Party and country are founded and thrive. The core of grasping the dialectical relationship between technological empowerment and theoretical research lies in clarifying the distinction between Ti (substance) and Yong (utility) [5]: Marxism is the Ti that guides the direction, while digital-intelligence technology is the Yong that innovates the paradigm. In project approvals and logical demonstrations, we must adhere to the principle of selecting topics based on problems rather than technology, resolutely discarding the techno-determinist mindset of "holding a hammer and looking for a nail." True academic innovation must not only pursue the "novelty" of technical application but must also cultivate the "depth" of theoretical interpretation. Furthermore, we must adhere to a comprehensive path that combines qualitative interpretation with quantitative analysis. A standardized digital-intelligence research paradigm should use qualitative research to establish the framework, quantitative analysis to provide support, and then return to qualitative interpretation for theoretical sublimation and value judgment. This is precisely the epistemological requirement of "practice, knowledge, again practice, and again knowledge."
Establish academic norms for generative AI-assisted research. In response to the academic crisis triggered by the intervention of generative AI in research, red lines for technical use must be drawn at the institutional level. On one hand, a strict manual verification mechanism must be established to eliminate academic misconduct. Facing the "machine hallucinations" of AI, any historical materials, data, or citations generated through algorithmic retrieval must be manually verified by the researcher against original sources. In Marxist theoretical research, the accuracy of citations is directly related to the scientific nature of truth; unconfirmed machine-generated content must never serve as academic evidence. On the other hand, a declaration system for technical assistance should be actively promoted to ensure academic transparency. Researchers should truthfully explain in their works the use of AI tools for auxiliary tasks such as data organization or text polishing, clearly demarcating the boundary between human intellectual contribution and machine assistance. The core viewpoints, value judgments, and logical architecture of a paper must be completed independently by the researcher, who bears full responsibility.
Strengthen theoretical consciousness and political responsibility in a human-machine collaborative environment. The more intelligent the tool, the higher the requirements for the user’s comprehensive quality. Enhancing the subjective position and capability of researchers is the fundamental strategy for dealing with the crisis of norms in the digital-intelligence era. Researchers in the New Era must both cultivate a deep foundation in the original works of Marx and Lenin and overcome "technophobia," improving their composite literacy to command big data using the scientific Marxist worldview and methodology. Only by mastering the principles can one possess "wisdom-eyes" [6] to distinguish truth from falsehood amidst massive information junk and algorithmic ideological bias, thereby activating the theoretical value of data. More importantly, no matter how technology iterates, researchers must always nurture an academic sentiment of concern for reality and service to the people. One must avoid hiding in an ivory tower of data for self-amusement; instead, remember the original aspiration of academia for the people, "writing scholarship into the hearts of the masses and writing papers on the land of the motherland" [7]. This value-driven sentiment and sense of responsibility is the source of the enduring vitality of Marxist theory and is irreplaceable by any advanced algorithm.
Norms restrain conduct; standards guide direction. Only by releasing technological potential within a framework of norms can Marxist research complete its tool-based empowerment and paradigm upgrading.
IV. Conclusion
The era is the mother of ideas; practice is the source of theory. The arrival of the digital-intelligence era provides unprecedented opportunities for Marxist theoretical research while posing severe questions of the times. Digital-intelligence technology can assist researchers in broadening the horizon of text mining, improving the empirical validity of research, and boosting the efficiency of knowledge production, injecting strong momentum into Marxist research. At the same time, we must recognize that technological empowerment is accompanied by new challenges in cognitive methods, value orientations, and subjective capabilities. The key to solving this proposition of the era lies in always adhering to the guiding position of Marxism in technical applications, transforming the "maximum variable" of digital-intelligence technology into the "maximum increment" for theoretical innovation.
Standing at the historical intersection of the start of the "15th Five-Year Plan" and the tenth anniversary of the "May 17" important speech, the paradigm innovation of Marxist research needs to focus further on the following directions: first, in interdisciplinary methods, promote the deep integration of computational social science and materialist dialectics, exploring prospective deduction, digital-intelligence analysis, and virtualized empirical verification for classic texts; second, in international comparative research, analyze and compare the value orientations of Chinese and foreign AI tools and the dissemination paths of Marxism, elucidating how Marxism realizes its own continuous development in the digital-intelligence era by absorbing new knowledge and responding to new questions, maintaining its scientific and revolutionary nature, and thereby enhancing the effectiveness of external communication; third, in institutional norms and talent cultivation, construct an academic norm system for the digital-intelligence era dominated by Marxism, cultivating composite research talents who possess "foundation in original works + tool operation skills + critical thinking." Only by adhering to the unity of "upholding the fundamentals" and "breaking new ground," and realizing true academic freedom under the constraints of academic norms, can we accelerate the construction of an autonomous knowledge system of philosophy and social sciences with Chinese characteristics, Chinese style, and Chinese spirit. This will allow for the creation of outstanding academic achievements worthy of this great era, injecting powerful theoretical momentum into the cause of comprehensively advancing the building of a strong country and the rejuvenation of the Chinese nation through Chinese-path modernization.