The Restructuring of AI and Knowledge Infrastructure: Deep Transformation from Academic Dissemination to Global Cognition

The Invisible Restructuring of Knowledge Infrastructure

While global cities race to establish computing centers, data exchanges, and AI industry clusters, a more fundamental change is taking place deep within scholarly communication. Artificial intelligence is no longer just a concept in technical papers; it has quietly become the central nervous system of digital knowledge management systems. A recent study published in Humanities and Social Sciences Communications, using a hybrid method of bibliometric and online sentiment analysis, systematically reviews the research trajectory and user perceptions of AI in digital media, humanities, and information science. Its core finding—that AI has the most significant impact on automated metadata indexing, citation analysis, and recommendation systems—may seem like a technical detail, but it actually points to a larger structural fact: the infrastructure of human knowledge is undergoing a silent algorithmic reconstruction.

Knowledge infrastructure, like roads, power grids, and communication base stations, determines how a civilization produces, stores, distributes, and consumes information. Libraries, academic databases, knowledge repositories, and publishing systems constitute the underlying infrastructure of the global knowledge economy. Over the past few decades, the appearance of these facilities has remained almost unchanged: search boxes, catalog cards, citation indexes. But beneath the surface, AI-driven classification, association, recommendation, and evaluation mechanisms are replacing manual indexing and knowledge organization. This study reminds us that the depth of this transformation far exceeds the typical narrative of technological upgrading.

From Tools to Order: Three Core Interventions of AI

The study identifies three core areas where AI intervenes in academic knowledge management: automated metadata indexing, citation analysis, and AI-driven recommendation systems. These three correspond precisely to three key stages of knowledge dissemination: organization, evaluation, and distribution.

Automated metadata indexing changes the way knowledge is discovered. In the past, document classification relied on the manual labor of professionals and exhibited obvious disciplinary path dependence. AI, by contrast, can generate multidimensional tags through semantic understanding, breaking down traditional disciplinary boundaries. This means that connections between interdisciplinary knowledge no longer depend on human-preset categories; instead, algorithms mine associations from the text itself. In the long run, this may reshape the sense of boundaries in academic discourse—the genealogy of knowledge is no longer jointly shaped by the academic community but is partly ceded to the latent statistical structures in model training.

Citation analysis, meanwhile, concerns the power of academic evaluation. AI can more accurately identify citation contexts, distinguish positive citations from critical ones, and even assess emotional tendencies in academic influence. This goes beyond the crude framework of traditional impact factors and citation counts. The study points out that the application of AI in academic evaluation is moving from "quantitative evaluation" toward "semantic evaluation." But this also raises a core question: Are evaluation criteria truly more objective, or are they merely embedding old biases into more complex algorithms?Recommendation systems constitute the most direct impact on the user side. AI-driven content recommendation determines what researchers see and what they overlook, thereby shaping the paths of knowledge consumption. As academic platforms worldwide widely adopt personalized recommendation, the "known world" that researchers face is increasingly filtered by algorithms. In essence, this is no different from how social media algorithms shape public opinion. It is only that in the academic sphere, it is more covert and harder to detect.

Redrawing the Global Knowledge Landscape

The most noteworthy global significance of this study is that AI's impact on academic knowledge management is not evenly distributed. Differences in digital infrastructure across countries and institutional environments are being translated into structural gaps in the capacity for knowledge production and dissemination. High-impact journals and database platforms in the Global North have long embedded AI tools into publishing, peer review, and recommendation; while a large number of academic institutions in the Global South remain at the stage of basic digitization.

This forces us to re-examine the global landscape of "knowledge cities." Over the past few decades, the competitiveness of global cities has depended on financial capital, talent mobility, and legal systems. In the AI era, however, the sophistication of knowledge infrastructure—including the intelligence of academic databases, the algorithmic capacity of knowledge management systems, and the degree of automation in digital libraries—is becoming a new dimension of urban competitiveness. Cities that can take the lead in integrating AI into their academic infrastructure will gain significant advantages in attracting global research talent and capital.

The study uses bibliometric data and online social media text from around the world, revealing differences in attitudes among users in different regions toward the application of AI in academia. These differences are not only reflected in technology acceptance, but are also rooted in different institutional expectations regarding knowledge production, privacy protection, and algorithmic transparency. In a global academic system that should be converging, AI instead amplifies the effects of institutional divergence.

Governance Deficit and Trust Crisis

The study particularly highlights two major challenges: insufficient algorithmic transparency, and issues of bias and privacy protection. These are not specific engineering bugs, but institutional deficiencies in the governance of global knowledge infrastructure.

When AI systems determine which papers are recommended, which metadata is generated, and which citations are given weight, the algorithmic logic behind them is rarely subject to public scrutiny by the academic community. The "black-boxing" of knowledge infrastructure has far more profound implications for civilization than the black-boxing of financial algorithms, because knowledge is the foundation of all other decisions.

The bias problem is even more sensitive. AI training data often comes from academic databases that have already been shaped by human power structures. If these data already contain biases in terms of region, language, gender, and discipline, AI will only replicate these biases into the new knowledge order at lower cost and greater efficiency. The study calls for the establishment of interdisciplinary collaboration mechanisms and for placing AI ethics at the design stage. But this requires institutional design that goes beyond any single institution, forming a governance framework for knowledge infrastructure at the global level.At a deeper level, this concerns the autonomy of human cognition. As the power to organize, evaluate, and distribute knowledge shifts partially from human experts to AI systems, we must ask: do humans still retain the right to define what constitutes "meaningful knowledge"? This is a question that threatens civilization's very capacity for self-reflection.

Long-term Trend: Cognitive Autonomy in the Digital Humanities Era

In the long run, AI's role in academic knowledge management will not remain at the instrumental level. It will give rise to a new epistemology: humans and algorithms jointly producing, classifying, evaluating, and discovering knowledge. This "hybrid cognition" is likely to become the foundational condition for the future social sciences and humanities.

But this does not mean that humans should be relegated to the role of assistants to algorithms. On the contrary, the value of research lies in reminding us that, in the algorithmic transformation of knowledge infrastructure, we must re-establish the centrality of human agency. Interdisciplinary collaboration is not an empty phrase; it means that computer scientists, library scientists, philosophers, sociologists, and policymakers must all be involved in system design. AI transparency is not merely a technical parameter but an institutional capacity—something that can be understood, questioned, and corrected.

The evolution of global knowledge infrastructure will set new standards for the integrated competitiveness of the next generation of cities and nations. Those academic systems that take the lead in establishing responsible AI governance frameworks will reap the dividends of global trust. In an era of information overload and algorithmic manipulation, trust—not content—is the scarcest resource in knowledge infrastructure.

This study does not offer a grand strategic blueprint, but through rigorous data analysis, it has charted the coordinates for knowledge management in the digital age. It shows us that AI is not only changing how we obtain information, but also how we define knowledge itself. In this sense, the "marginal research" of library and information science touches precisely on the most central question of digital civilization: once machines are deeply embedded in cognitive processes, how can humans maintain a genuine grasp of their own intellectual genealogy?

Global cities and academic institutions must recognize that the intelligent transformation of knowledge infrastructure is already irreversible. The question is no longer whether to embrace AI, but with what values and what institutional forms this transformation should be shaped. The answer will determine the fundamental contours of the global knowledge order for decades to come.