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Beyond Intermediaries: The "Bilateral Extension" of Library Value Reconstruction in the AI Era

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In the context of artificial intelligence reshaping knowledge production, libraries face a critical challenge regarding their core value as knowledge intermediaries. A new perspective suggests that libraries should undergo a strategic transformation, extending their services in two directions: forward to become architects of high-quality data and backward to guide the application of machine-generated insights, thus positioning themselves as navigators of critical thinking. The rise of AI technologies, particularly large language models, challenges the traditional DIKW (Data-Information-Knowledge-Wisdom) model, which relies on human cognition to process data. Instead, AI demonstrates a "Direct-to-Cognition" potential, extracting insights directly from raw data, thus redefining the roles in the value chain. To adapt, libraries must shift from managing finished knowledge products to curating knowledge "raw materials," ensuring data is "AI-ready." This involves evolving their roles in compliance guidance, data strategy consulting, proactive data curation, and developing metadata that serves both humans and machines. Moreover, as machines generate insights, libraries must also extend their focus to help users navigate, validate, and ethically apply these outputs. This includes educating users on critical AI literacy, providing interpretative services for AI-generated insights, and advocating for responsible innovation in AI ethics. Ultimately, this dual approach positions libraries as dynamic hubs within the knowledge production ecosystem, actively connecting researchers, data, machines, ethics, and society, ensuring their relevance and value in the AI era.

In today's world, where artificial intelligence technology is increasingly reshaping the paradigm of knowledge production, a profound issue stands before the library science community: as machines can directly generate insights and decisions from vast amounts of data, what will become of the core value of libraries—long regarded as knowledge intermediaries? An emerging perspective suggests that the key question is not whether libraries will be "bypassed," but whether they can actively undergo a profound strategic transformation to achieve a "bidirectional extension" of their service focus: forward, extending to the source of data, becoming architects of high-quality data; and backward, extending to the application end of machine-generated cognition, becoming navigators of critical thinking. This concept of "bidirectional extension" provides a penetrating path for the reconstruction of library value in the age of AI.

The Impact of "First Principles": From the Twilight of DIKW to the Dawn of "Direct-to-Cognition"#

To understand the urgency of this transformation, one must first examine the theoretical impact behind it. Traditionally, the service logic of library science is deeply rooted in the DIKW (Data-Information-Knowledge-Wisdom) model. This model depicts a linear, hierarchical value chain, where human cognition and judgment are the core engines driving the transformation of data into information, knowledge, and ultimately wisdom, with libraries playing a key role in organization, filtering, and dissemination.

However, the rise of AI technologies such as large language models is challenging the foundations of the DIKW model with their powerful pattern recognition and content generation capabilities. They demonstrate a potential for "Direct-to-Cognition," which extracts patterns, insights, and even decision recommendations directly from raw data, bypassing traditional human information processing steps to some extent. This phenomenon can be seen as a new "first principle": in data-intensive knowledge production, machines are becoming the primary data processing entities.

This principle does not signal the end of human roles but forces us to rethink the starting and ending points of value creation. If the starting point of the value chain is raw data, and machines are its main processors, then the initial quality, structure, and "machine-friendliness" of the data directly determine the upper limits of subsequent cognitive outputs. This is precisely the logical starting point for the "forward" shift in service focus.

Shifting the Service Focus Forward: Becoming Architects of Data Readiness#

The "forward" strategy requires libraries to transform from managers of knowledge "finished products" to strategic curators of knowledge "raw materials." Its core mission is to ensure that data reaches an "AI-ready" state before entering AI models. This involves deepening roles at three levels:

  1. From compliance guidance to data strategy consulting. Library services are no longer limited to assisting researchers in writing compliant data management plans (DMPs) but must become data strategy advisors in the early stages of research projects. Librarians need to proactively engage in discussions about how data structure, annotation methods, and collection design can better serve future machine learning and AI analysis, enhancing the potential value of data from the source.

  2. From passive archiving to proactive data curation. The function of institutional repositories (IR) will upgrade from "digital preservation" to "AI-friendly data centers." This not only means storage but also involves enriching data context through proactive curation activities such as data cleaning, standardization, and linking (e.g., connecting knowledge graphs and ontologies), enhancing its machine discoverability and understandability.

  3. From serving "humans" to accommodating "machines" in metadata strategy. Metadata design must transcend traditional human-centric retrieval services and adopt more machine-interactive semantic standards. High-quality, machine-readable metadata is a key infrastructure for activating data value, achieving cross-domain data integration, and enabling efficient AI analysis.

By shifting the focus forward, libraries strategically deploy their traditional advantages in information organization, standardization, and long-term preservation to the forefront of knowledge production, thereby establishing an indispensable foundation in the human-machine collaborative knowledge ecosystem.

Shifting the Service Focus Backward: Becoming Navigators of Machine-Generated Cognition#

When machines output "cognition," a new and significant service gap emerges—explanation, validation, trust, and ethics. AI outputs are often probabilistic, lacking deep context, and can even be "black boxes." Users face these seemingly authoritative "insights" and urgently need critical navigation and support. Therefore, the service focus of libraries must extend to the backend of the value chain.

The "backward" strategy requires libraries to transform from "providers" of information to "navigators" of cognition, with the core mission of helping users master, interrogate, and responsibly apply machine-generated cognition. This also involves the evolution of roles at three levels:

  1. From information literacy to critical AI literacy education. The focus of library literacy education must expand from assessing the authority of traditional information sources to examining the reliability of AI outputs. Content should cover algorithmic bias identification, understanding model limitations, result validation, and triangulation, cultivating users' ability to collaborate effectively and cautiously with AI systems.

  2. From reference consultation to explanatory and contextual services. In the face of complex insights obtained from AI, librarians need to play the role of "AI result interpreters." This includes introducing and applying explainable AI (XAI) tools, and more importantly, placing AI outputs within the specific disciplinary knowledge systems and ethical frameworks for in-depth contextual analysis, helping users understand their true significance and potential impact.

  3. From collection development to advocating responsible innovation. As a symbol of social credibility and a neutral knowledge sanctuary, libraries are ideal platforms for discussing AI ethics and advocating responsible innovation. By organizing interdisciplinary academic discussions, formulating ethical guidelines for AI applications, and promoting privacy protection technologies, libraries can safeguard the core values of the academic community and society while advancing technological progress.

Conclusion: The Birth of an Ecosystem Hub#

"Shifting the service focus forward and backward" is not two separate actions but a complementary dynamic closed loop. High-quality data curation at the front end is the foundation for the trustworthiness of cognitive results at the back end; conversely, critical reflection on AI results at the back end will guide the optimization of data collection and processing strategies at the front end.

This strategic concept of "bidirectional extension" paints a new development blueprint for libraries. They are no longer passive knowledge repositories but actively embedded hubs in the entire cycle of knowledge production. In this hub, libraries connect researchers, data, machines, ethics, and society, ensuring a solid foundation through "forward" shifts and a correct course through "backward" shifts. This is not only a profound reflection and development of traditional library science theory but also points to a clear and determined path for libraries to thrive and demonstrate their value in the intelligent era.

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