How AI is Reshaping Knowledge Management: Academic Perspectives on the Frontiers of Digital Media and Information Science

Under the wave of artificial intelligence technological leaps, the fields of digital media, humanities and social sciences, and information science are undergoing a profound structural transformation. From machine learning and natural language processing (NLP) to computer vision, AI is no longer a distant science fiction concept but has permeated every microscopic level of academic research, reshaping the entire lifecycle of knowledge—from generation to organization to dissemination.

The most direct impact of AI intervention is felt at the level of "infrastructure" for academic information. Automated metadata indexing, complex citation analysis, and personalized AI recommendation systems are revolutionizing the operational models of traditional libraries, digital archives, and even the entire academic publishing ecosystem with exponential efficiency. This is not merely a technical optimization; it is a fundamental restructuring of knowledge power distribution and information access pathways.

However, despite significant technological progress, systematic research by the academic community on the role of AI in knowledge management (KM) remains insufficient. Existing research often relies on traditional bibliometric analysis, which, while clearly mapping research hotspots and academic network structures, struggles to capture the dynamic changes brought by AI and the emotional perception at the user level due to its static, quantitative perspective.

The true strategic challenge lies in moving beyond purely quantitative statistics to achieve a "systemic" and "user-centric" hybrid understanding of the knowledge dissemination process. This demands an upgrade in research paradigms, shifting the focus from "what has been discovered" to exploring "how it is discovered" and "how users perceive this discovery." Therefore, future research priorities must pivot towards integrating dynamic data analysis and qualitative user sentiment analysis to comprehensively assess the actual effectiveness of AI in knowledge distribution.

A deeper consideration is ethical governance. As AI algorithms gain decision-making power over information filtering, evaluation, and even knowledge presentation, issues of algorithmic transparency, data privacy protection, and potential bias are no longer mere technical details but matters concerning academic fairness and the continuation of civilization. Establishing an interdisciplinary collaborative mechanism to enhance AI's transparency and effectively mitigate algorithmic entrenchment of social biases is an urgent task for the academic community today.

In short, artificial intelligence is driving the "automation" and "personalization" transformation of knowledge. The perspective of urban studies might offer a reference point: just as the upgrading of urban infrastructure reshapes social space and power structures, the reshaping of the knowledge infrastructure by AI is not just about the efficiency of information retrieval, but about who holds the power to define knowledge and how that power is amplified or suppressed by algorithms. The academic community must embrace technological innovation while anchoring itself to a solid ethical course, ensuring this knowledge revolution is both inclusive and responsible.