Intelligent Restructuring of Knowledge Infrastructure: How Artificial Intelligence Reshapes the Future of Academic Communication and Information Management

The preservation, organization, and dissemination of human knowledge have long depended on a seemingly silent but vital system—libraries, academic databases, information classification schemes, and publishing workflows. Like a city’s water supply and roads, they constitute the public infrastructure of knowledge. Today, artificial intelligence is quietly entering these domains, bringing not only gains in operational efficiency but also a deep structural reset. The question is no longer whether AI should participate in knowledge management, but rather by what logic, under what governance, and in service of what purposes it will reshape the Babel library of this digital age.

Recent research trajectories indicate that the application of AI in library and information science (LIS) is moving from peripheral experimentation to core processes. A mixed-methods study published in Humanities and Social Sciences Communications mapped global research trends through bibliometrics and web-based sentiment analysis, finding that automated metadata indexing, citation analysis, and AI-driven recommendation systems are currently the most consequential areas of impact. These technologies are not mere tool upgrades—they are redefining the pathways of knowledge discovery, the benchmarks of academic evaluation, and the ways researchers interact with information systems. When recommendation algorithms decide whether a paper is seen, and when citation analysis influences grant funding and career advancement, AI is no longer just an assistant but becomes a distributor of epistemic power.

However, the study also reveals significant cognitive and scholarly gaps. Although discussions of AI in commercial digital media and social platforms have reached near saturation, academic libraries, digital repositories, and scholarly publishing systems have yet to enter public debate at a comparable depth. This misalignment is disquieting: precisely these institutions carry the public record of human civilization, yet their technological transformation is the least subjected to panoramic scrutiny. The study’s combination of dynamic and static methods was designed precisely to capture this blind spot—identifying research hotspots through quantitative mapping of the literature, then presenting information professionals’ and scholars’ genuine attitudes toward AI through user sentiment analysis. The results show that user perception is not a one-dimensional embrace of technical efficiency, but is interwoven with complex concerns about algorithmic bias, data privacy, and system transparency. This reminds us that the implementation of AI in knowledge management is, in essence, a social process of trust-building rather than a mere competition in computational performance.On a broader scale, AI-driven knowledge infrastructure is facing three structural tensions. The first is the conflict between efficiency and equity. Automated indexing and citation analysis can significantly reduce the cost of knowledge processing, but if training data embeds geographical or disciplinary biases from the existing academic system, AI may reinforce rather than alleviate the knowledge marginalization of the Global South. Countries with weak digital infrastructure may be reduced to passive data providers in the new AI knowledge system, rather than equal knowledge producers. The second tension lies between automation and humanistic judgment. Knowledge management is not just pattern recognition; it also involves deep understanding of context, history, and values. Knowledge classification left entirely to algorithms may dissolve the distinctive hermeneutic character of the humanities. The third tension is the contest between openness and privacy. The academic system increasingly emphasizes open access, while AI-driven user analytics requires collecting large amounts of usage behavior data. This contradiction calls for a more sophisticated governance framework to balance.

Research calling for interdisciplinary collaboration, greater AI transparency, and addressing bias and privacy issues is not mere general talk, but points to a new set of governance principles. In the future, the intellectualization of knowledge infrastructure must move beyond the single dimension of "technology adoption" and enter a more mature institutional design. Specifically, user perception should be treated as a core input for system iteration, not a footnote in post-hoc evaluation; explainable AI decision-making mechanisms should be established so that scholars and information users can understand the underlying logic of recommendations and classifications; and sustained collaboration among academic publishers, libraries, technology developers, and public policy departments should be promoted to ensure that what AI enhances is always the public nature of knowledge, not closed proprietary assets. In this sense, the application of AI in LIS is no longer a peripheral issue within a discipline, but a future narrative concerning the democratization of knowledge, academic autonomy, and the global information order.

From a longer historical perspective, libraries and archives have always been instruments of civilization's self-understanding. Every generational shift in media technology—from manuscripts to print, from card catalogs to digital databases—has redefined the social relations of knowledge. Artificial intelligence is both a continuation of this lineage and a rupture. For the first time, it endows knowledge management systems with proactive prediction and recommendation capabilities, transforming them from passive "warehouse keepers" into "active participants" in knowledge production and dissemination. This requires us to examine this moment with the vision of a strategist, not just the mindset of a technician. As research institutions, policymakers, and information professionals around the world confront this transformation together, the real challenge lies not in making machines smarter, but in making institutions wiser. Only with long-termism as the framework, ethics as the boundary, and knowledge sharing as the purpose can artificial intelligence truly become a partner in safeguarding humanity's civilizational memory, rather than a cold engine accelerating the divide between the knowledge-rich and the knowledge-poor.