The Coevolution of AI Technoscience and Librarianship: A Historiographical Synthesis of Profound Historical Significance

The intertwined histories of artificial intelligence (AI) and librarianship can only be fully understood by examining the broader constellation of technoscientific practices. These practices, which can be thought of as the various social, material, and epistemic operations that render scientific knowledge inseparable from its technological manifestations, have influenced, and been influenced by, the profession. 

By situating AI's trajectory within the library field as a technoscientific enterprise, historiography illuminates the reciprocal shaping of professional ethics, epistemic norms, research infrastructures, and cultural values that guided the development and deployment of AI-driven systems, highlighting the complex interplay between AI and librarianship.

Early Foundations: Documentation, Information Retrieval, and Proto-Technoscientific Regimes

The mid-twentieth century witnessed the formation of technoscientific ecosystems that would later underpin AI's integration into librarianship. Pioneering figures such as Vannevar Bush and Eugene Garfield innovated retrieval systems and cultivated intellectual and infrastructural milieus that linked computational methods to the scientific enterprise of managing knowledge. Initiatives like the Cranfield experiments, which were early attempts to apply statistical methods to information retrieval, and the MEDLARS project, a pioneering computerized biomedical information system, were as much about pursuing scientific rigor in organizing massive bodies of literature as they were about technological refinement. Historiographically, these developments are interpreted not as isolated "tools" grafted onto librarianship but as early manifestations of technoscience: libraries, research institutes, government agencies, and scientific publishers formed a complex web of knowledge production and dissemination.

Within these technoscientific regimes, librarians became key brokers in shaping the standards, protocols, and conceptual frameworks that would eventually inform AI-driven solutions. Historians such as Michael K. Buckland and W. Boyd Rayward have shown that the formation of machine-readable cataloging (MARC) exemplified how librarians, in concert with computer scientists and information theorists, participated in a hybrid knowledge-making process. This expertise, integrated into the evolving technoscientific infrastructure, served as a substrate upon which the rudiments of AI could later flourish, underlining their active participation in the evolution of AI-driven solutions.

From Mechanization to AI Concepts: Expert Systems and New Technoscientific Practices (1970s–1980s)

By the 1970s and 1980s, conceptions of AI in libraries began to transcend simple automation, embracing inference engines, expert systems, and knowledge representation models. The historiography of this period reveals a deepening of the technoscientific character of librarianship. This character, which is a unique form of technoscience that fuses library expertise in taxonomies and subject classification with emergent computational models designed to emulate or extend human judgment, was advanced as library professionals collaborated with computer scientists, cognitive scientists, and information theorists in developing systems that aimed not merely to retrieve information but to "understand" it.

Incorporating the works of Patrick Wilson, Marcia Bates, and early LIS theorists, historians underscore the importance of this technoscientific interplay. Libraries acted as laboratories where the feasibility and limitations of AI concepts could be tested. The decision to adopt (or resist) expert systems was not purely technical. It involved carefully negotiating professional values—neutrality, intellectual freedom, and user-centric service—within an emergent technoscientific culture that prized efficiency, scalability, and algorithmic rationality. 

Feminist and critical LIS historians emphasize that these early AI implementations were never value-neutral but carried the ideological and cultural imprints of their technoscientific origins. Classifications, subject headings, and inference rules embedded in the software reflected dominant epistemic traditions and systemic biases, demonstrating how technoscientific infrastructures reproduce and sometimes entrench social hierarchies.

The Digital Revolution, Semantic Web, and the Epistemic Reconfiguration of Technoscience (1990s–2000s)

The explosion of networked information resources in the 1990s and 2000s profoundly reshaped the technoscientific landscape in which librarians and AI developers operated. The advent of digital libraries and the Semantic Web project fostered a new breed of technoscience that combined global communication infrastructures with formal ontologies, machine-readable metadata, and algorithmic reasoning. Tim Berners-Lee's vision of the Semantic Web can be seen as a quintessential technoscientific initiative that demanded interdisciplinary collaboration to engineer a system where machines could interpret the meaning, not just the form, of information.

As historians have documented, librarians contributed crucial conceptual labor to this project. Their professional knowledge of controlled vocabularies, authority control, and stable classification systems underpinned the creation of interoperable schemas and ontologies. 

Thus, the historiography reveals that librarians functioned as epistemic gatekeepers in these technoscientific spaces. Rather than remaining peripheral, they were central actors who influenced how knowledge would be parsed, linked, and interpreted by intelligent agents. 

This was no passive role: library professionals pushed for transparency, interoperability, and long-term preservation, ensuring that the emerging AI-driven ecosystem would not solely reflect the proprietary interests of commercial entities. The tensions between open standards and private platforms, ethical imperatives, and market logic became focal points in historiographical accounts, illustrating how technoscience is always political, moral, and contested.

Machine Learning, Predictive Analytics, and the Technoscience of Data (2010s)

The 2010s brought about the rise of machine learning (ML) and data-driven research, marking a significant and impactful shift in the technoscientific character of library-AI relations. Large-scale text and data mining, predictive modeling, and personalized recommendation engines reframed the librarian's role in an era where algorithmic analyses increasingly shaped knowledge infrastructures. Historiographically, accounts of this period acknowledge librarians as crucial mediators in constructing training datasets, curators of high-quality corpora, and advocates for ethical and equitable AI usage. Libraries did not merely receive ML tools; they shaped the epistemic conditions under which these tools operate.

Critical studies by scholars such as Safiya Noble and Virginia Eubanks, although focused broadly on algorithmic society, have influenced LIS historiography by highlighting AI's socio-technical and political dimensions. Within library infrastructures, ML tools and data analytics optimized searching and resource discovery and created powerful feedback loops influencing acquisition policies, user behavior monitoring, and resource deaccessioning. 

Thus, the coevolution of AI and librarianship in this era epitomizes technoscience in action: data, algorithms, professional norms, and cultural values merged into new forms of knowledge production and curation. Historiographers point to the emergence of a reflexive stance among librarians, who increasingly interrogated the moral implications of adopting black-box recommender systems and called for accountable, explainable AI—further reinforcing the notion that technoscience is always both material and moral.

Generative AI and Future Directions: Globalization, Epistemic Plurality, and Technoscientific Inclusion (2020s–Present)

Currently, characterized by generative AI models and large language systems, the technoscientific complexity has reached new heights. Libraries now encounter AI systems that mimic human discourse, generate synthetic texts, and replicate certain facets of the reference interview. Historians and STS scholars are beginning to outline the contours of this emergent phase, noting that it underscores all the hallmarks of a technoscientific enterprise: globalized R&D pipelines, integration of computational linguistics research, dependence on massive, culturally contingent training sets, and the involvement of myriad stakeholders—from governments and corporations to scholarly communities and civil society organizations.

This evolving historiography contends with questions of epistemic diversity, postcolonial critiques, and cultural pluralism. AI-driven classification and retrieval systems risk homogenizing local knowledge traditions, imposing Anglo-European conceptual structures on global collections. Libraries, often regarded as guardians of cultural heritage, resist homogenization, promoting inclusive metadata standards and alternative ontologies reflecting indigenous and non-Western epistemologies. Technoscience, here, is revealed as a battlefield where conflicting values and visions of knowledge organizations collide. The historiographical record is moving toward acknowledging that the coevolution of AI and librarianship cannot be fully understood without considering these global technoscientific dimensions, where the geopolitical economy of knowledge, linguistic diversity, and varying resource infrastructures shape the adoption and adaptation of AI technologies.

Historiographical Methodologies and Technoscientific Frameworks

In constructing this historiography, scholars rely on a diverse array of sources: archives of library associations, internal project reports from entities like OCLC and the WorldCat global network, oral histories with pioneering information scientists and technologists, policy documents from UNESCO and IFLA, and ethnographic studies of library labs and digital scholarship centers. Methodologically, STS frameworks have proven indispensable for understanding technoscience as neither purely technical progress nor mere social construction but as an ongoing co-production of scientific knowledge, technological artifacts, social order, and cultural meaning. Feminist, critical race and postcolonial theories further illuminate how power relations and identity politics shape the technoscientific encounters that produce and implement AI in libraries.

The Current Crisis and a Call to Action

In the present era, the coevolution of AI technoscience and librarianship must be viewed in light of what many observers describe as a growing crisis within the library profession. 

Across numerous contexts—public libraries under political pressures and budgetary constraints, academic libraries contending with escalating journal costs and commercial publisher dominance, and digital libraries navigating complex intellectual property battles—librarians are increasingly challenged to justify their existence and maintain their ethical commitments. 

This environment is further complicated by the proliferation of misinformation, disinformation, and other manipulative information practices, often facilitated by robust AI-driven recommender systems and social media algorithms. As library budgets shrink, pressures mount from private sector interests, and generative AI tools promise to produce "knowledge" without human mediation, librarianship finds itself caught between its longstanding mission to ensure equitable and trustworthy access to knowledge and the encroachment of technoscientific systems that privilege efficiency, commodification, and user commoditization over the public good.

These pressures highlight a more profound paradox. 

• On the one hand, the techno-optimistic narratives that accompanied AI's integration into library infrastructures promised a future of abundant, frictionless information access, improved discoverability, and enhanced user services. 
• On the other hand, the reality of algorithmic opacity, bias in training data, surveillance-driven personalization, and platform monopolies poses grave ethical questions. 

Historiography has shown that librarians—through their role in shaping standards, metadata schemas, and ontologies—have not been passive bystanders; instead, they have built the very systems that now threaten to overshadow the profession's foundational values. 

Paradoxically, even as librarians have helped integrate AI into the global knowledge infrastructure, the technoscientific milieu of the twenty-first century has eroded public trust in objective knowledge sources and placed libraries' core values under significant strain. In other words, the coevolution documented by historians has reached a critical juncture where librarianship's identity and purpose hang in the balance.

This crisis also underscores the necessity of examining AI and librarianship through the lens of technoscience. Such a perspective reveals that the current challenges—epistemic, institutional, financial, and cultural—are not merely external factors bearing upon librarianship but are co-produced by the systems and practices that libraries helped create and sustain. 

Understanding this interplay encourages a more proactive stance. Instead of lamenting the erosion of traditional roles, librarians can leverage their deep familiarity with classification, curation, knowledge ethics, and public trust to guide the responsible deployment of AI. They can advocate for transparent algorithms, push for open-source tools, curate ethically sourced datasets, and lead public dialogues on balancing innovation with accountability and human-centric values.

Thus, the historiographical synthesis, which has detailed the reciprocal shaping of AI technoscience and librarianship, now issues a call to action. Librarians, library educators, policymakers, and allied stakeholders must collaborate to reaffirm and reimagine libraries' core mission in the face of intensifying technoscientific pressures. Concrete steps include developing participatory frameworks for evaluating AI tools, forming transnational coalitions to promote interoperable and inclusive metadata standards, and adopting stronger professional privacy and algorithmic transparency guidelines.

These efforts should be accompanied by broad-based public education initiatives, ensuring communities understand both the promise and the peril of AI-driven knowledge systems.

In embracing such strategies, librarians can restore confidence in their role as guardians of intellectual freedom and mediators of knowledge pluralism, even amid a shifting technoscientific landscape. The historiography of AI and librarianship, far from being a mere scholarly record, can serve as a source of critical insight, offering lessons on the importance of ethical stewardship, interdisciplinary collaboration, and sustained advocacy for the public interest. By reclaiming their agency and reaffirming their values, librarians can help forge a future in which AI complements rather than compromises their essential mission, ensuring that libraries remain vital democratic institutions in a world increasingly shaped by intelligent machines.