Raymond Uzwyshyn Ph.D.
Introduction: Tensions, AI, the Evolving Landscape
In a recent professional forum on academic libraries, several voices illustrated the fundamental tensions now reshaping research and academic libraries in the age of artificial intelligence:
Library Director: "Our print circulation statistics have plummeted 45% in the past three years, electronic resource usage skyrocketing. Students come to the library but rarely interact with the physical collections. Meanwhile, faculty are increasingly asking about AI tools for AI, data visualization, text mining our subscription databases, digital scholarship. The university administration is questioning our space allocations and staffing models. What is our value proposition when information isn't just abundant but can be synthesized by AI tools more rapidly than humans read or write?"
AI/Data Librarian: "We're at an inflection point where our traditional expertise in information organization and retrieval is being superseded by AI capabilities. Students no longer need our reference services to find information—they need guidance on evaluating AI-synthesized information, effective prompts, basic knowledge so they can enable their research data into insight and discovery."
Faculty Member: "I send my students to the library's information literacy workshops especially with all the fake information, perhaps they can also introduce learning how to use these new AI tools more effectively with AI literacy. The library needs to fundamentally remix its instructional role—teaching not just information literacy but AI literacy. I know they are teaching digital literacy and source checking literature reviews AI or other?"
Digital Humanities Librarian: "This transition presents a lot of great opportunities. We've begun partnering with faculty to use large language models for analyzing historical archives across multiple disciplines simultaneously—research that would have taken years through traditional methods. The library is evolving from an information repository to a laboratory for AI-enhanced scholarship. But this requires entirely new skills and infrastructures that weren't part of our professional training."
These perspectives encapsulate a pivotal moment in library history. Just as the transition from manuscript to print transformed libraries from cloistered scriptoria to public repositories of knowledge, and the digital revolution transformed libraries from physical collections to hybrid information gateways, the integration of artificial intelligence necessitates a profound reimagining of how libraries serve academic and research communities. This article examines the historical evolution of libraries, analyzes the medium specificity of AI in the library context, and proposes a comprehensive transformation of academic and research libraries for an AI-integrated future.
I. Historical Evolution of Library Paradigms
A. Ancient Libraries and Manuscript Cultures
The earliest libraries functioned primarily as repositories of scarce knowledge artifacts. The Library of Alexandria (c. 300 BCE) represented perhaps the most ambitious attempt to centralize the world's knowledge in a single physical location. In manuscript cultures, libraries served as centers for preservation, copying, and controlled access rather than broad dissemination.
The scarcity of manuscripts made early libraries necessarily exclusive institutions. Access was typically limited to scholars, religious authorities, or political elites. The librarian's primary expertise lay in knowing what texts existed in the collection and locating them within the physical space—a form of human search engine in an era of extreme information scarcity.
Medieval monastic libraries continued this preservation-focused paradigm while adding significant organizational innovations. The development of catalogs, classification systems, and physical arrangements reflected an increasing concern with findability as collections grew. The librarian's role expanded from preservation to organization, though access remained highly restricted.
B. The Gutenberg Revolution and Early Modern Libraries
The printing press transformed libraries from preservers of scarce manuscripts to collectors of increasingly abundant printed works. This shift necessitated new approaches to selection, organization, and service. As printed books became more widespread, libraries began to emphasize collection breadth rather than simply preservation of rare items.
"The invention of typography confirmed and extended the new visual stress of applied knowledge, providing the first uniformly repeatable commodity, the first assembly line, and the first mass production" (McLuhan, 1964). This technological revolution fundamentally altered how information was transmitted and accessed.
The role of librarians evolved from guardians of scarce texts to curators of expanding collections, requiring new expertise in bibliography, organization, and increasingly, subject knowledge. Libraries remained relatively exclusive institutions, but their user base gradually expanded beyond religious and aristocratic elites to include scholars, professionals, and eventually, broader publics.
C. Enlightenment and the Public Library Movement
The Enlightenment ideal of knowledge accessibility found expression in the development of more public-oriented libraries. The founding of the British Museum Library (1753) and the Library of Congress (1800) represented national commitments to knowledge collection and preservation on unprecedented scales.
The 19th century public library movement, championed by figures like Melvil Dewey and Andrew Carnegie, dramatically expanded access to information resources. This democratization of knowledge required innovations in both technical organization (e.g., the Dewey Decimal System) and service models. Libraries increasingly emphasized instruction in library use alongside collection development.
D. Research Libraries in the Industrial Age
The late 19th and early 20th centuries saw the rise of the modern research library, exemplified by institutions like Harvard, Yale, and the University of Chicago. These libraries emphasized comprehensive collection development across disciplines, sophisticated organizational systems, and specialized services for researchers.
Research libraries developed specialized services including in-depth reference consultation, interlibrary loan, and eventually, bibliographic instruction. These services positioned the library as essential infrastructure for scholarly communication—a role that would become increasingly complex in the digital age.
E. Digital Transformation and the Hybrid Library
The late 20th century brought perhaps the most significant transformation since Gutenberg—the digital revolution in information. Beginning with online catalogs and bibliographic databases in the 1970s-80s, accelerating with CD-ROMs and internet connectivity in the 1990s, and fundamentally transforming with mass digitization and born-digital resources in the 2000s, library collections rapidly evolved from purely physical to increasingly digital.
This transformation created what became known as "hybrid libraries"—institutions managing both physical and digital collections through increasingly sophisticated technical infrastructures. Library expertise expanded to include digital resource management, electronic resource licensing, digital preservation, and technical systems administration.
User services similarly transformed, with reference assistance extending to database searching, information literacy instruction, and eventually, digital scholarship support. Libraries increasingly emphasized teaching search strategies, evaluation skills, and ethical information use rather than simply providing access to materials.
F. Information Commons, Learning Commons, and Network-Era Libraries
The early 21st century saw the rise of the Information Commons and Learning Commons movements in academic libraries. As collections increasingly moved to digital formats, libraries repurposed physical spaces to support new forms of collaborative learning, technology access, and integrated academic support services. These commons integrated tutoring centers, writing support, media production facilities, and technology assistance into library spaces, reflecting a shift from collection-centered to learning-centered models.
This transformation represented a fundamental reimagining of the library's physical presence on campus. These new spaces blended "high tech and high touch" approaches, combining advanced technologies with human-centered service models that emphasized collaboration, creativity, and interdisciplinary engagement (Uzwyshyn, 2016).
Simultaneously, academic libraries increasingly shifted emphasis from local collections to networked services. Lorcan Dempsey's concept of the "inside-out library" described this transition—from libraries primarily collecting external publications for local use to libraries actively disseminating locally-produced scholarship to the wider world (Dempsey, 2016).
This shift manifested in new service areas including:
- Institutional repositories for capturing and disseminating scholarly outputs
- Research data management services supporting the full research lifecycle
- Scholarly communications services addressing open access, copyright, and publishing
- Digital scholarship centers supporting computational research methods
These developments positioned libraries less as information warehouses and more as active partners in knowledge creation. As collection development increasingly focused on licensing access to external resources rather than ownership, libraries emphasized distinctive local services and special collections as key value propositions.
The networked era also brought unprecedented collaboration across institutions. Consortial resource sharing, collaborative digital libraries, shared print repositories, and collective licensing transformed libraries from standalone institutions to nodes in larger information networks. Organizations like SPARC, HathiTrust, and the Digital Public Library of America exemplified this network-scale thinking about library functions.
II. Medium Specificity of AI: Transforming the Library Paradigm
A. From Information Scarcity to AI-Processed Abundance
Academic libraries developed in conditions of information scarcity, where physical collections represented significant institutional investments and specialized expertise was required to navigate bibliographic systems. This scarcity paradigm shaped core library functions including:
- Collections as valuable institutional assets requiring protection
- Discovery systems designed for precision in resource location
- Reference services mediating between users and scarce resources
- Library instruction focused on navigating complex retrieval systems
- Physical spaces organized around collection storage and consultation
The digital transition began challenging this paradigm through networked information access, but AI represents a more fundamental shift—not just from scarcity to abundance but from unprocessed to pre-processed information. When AI systems can not only locate but read, analyze, and synthesize information at superhuman speeds, libraries must reconsider their value proposition.
We now inhabit an "abundance paradigm" characterized by exponential rather than linear information growth (Diamandis & Kotler, 2012). In this environment:
- Information itself has diminishing value
- Filtering and evaluation skills become paramount
- Creativity in information application increases in importance
- Interdisciplinary synthesis creates greater value than domain expertise
- Unique human perspectives and ethical reasoning gain premium value
This shift from scarcity to abundance necessitates library approaches that emphasize wisdom over knowledge, application over access, and synthesis over organization. The traditional library model optimized for information stewardship becomes increasingly misaligned with an information environment characterized by abundance, accessibility, and AI processing capabilities.
.AI's Unique Affordances in the Library Context
Marshall McLuhan's tetrad framework provides a valuable lens for understanding how AI transforms library services and functions. His analytical model examines what a medium enhances, obsolesces, retrieves, and reverses into—revealing the multidimensional impact of technological change. Applied to AI in libraries, this framework illuminates both opportunities and challenges:
1. Computational Knowledge Processing
AI fundamentally transforms how research content is processed and synthesized. It enhances our ability to integrate disparate information sources into coherent knowledge products and process information at scales beyond human capacity. Simultaneously, it obsolesces traditional literature reviews performed manually and sequential reading of individual sources. This capability retrieves the ancient ideal of universal knowledge access (like the Alexandrian Library), but with both access and synthesis capabilities built in. However, it potentially reverses into information homogeneity and decreased critical engagement with primary sources as researchers rely more on AI-synthesized outputs than direct examination of original materials.
Traditional reference services focused on connecting users with appropriate information sources. When AI can not only locate but synthesize information across thousands of sources simultaneously, reference must shift from source identification to critical evaluation and contextual understanding of AI-processed information.
2. Personalization at Scale
AI enables libraries to provide tailored experiences that adapt to individual researcher needs, learning styles, and information-seeking behaviors. This enhances our ability to provide highly individualized research assistance and recommendations based on user history, preferences, and needs. It obsolesces standardized reference interactions and generic instruction sessions that treat all users identically. The technology retrieves the personalized attention of traditional scholarly mentorship within scalable systems that can serve entire academic communities. Yet it risks reversing into privacy concerns and information filter bubbles that limit intellectual exploration as systems increasingly predict and shape user information pathways.
Libraries have historically struggled to scale personalized services beyond individual reference consultations. AI enables personalization at scale but requires thoughtful implementation to respect privacy and avoid constraining intellectual discovery through over-personalization.
3. Multimodal Collection Analysis
Academic libraries have traditionally managed different media types through separate systems with distinct metadata standards and access methods. AI enhances our ability to analyze text, images, audio, and video collections through a single interface with sophisticated cross-format pattern recognition. It obsolesces format-specific analytical tools and siloed collection management approaches that treat each media type in isolation. This capability retrieves an integration of multimedia understanding within computational frameworks that mirrors how humans naturally process information across sensory channels. However, it potentially reverses into a flattening of format-specific characteristics important to scholarly analysis as diverse media types are processed through standardized computational approaches.
Academic libraries increasingly manage multiformat collections including text, image, audio, and video materials. AI's multimodal capabilities enable unified analysis across these formats, creating new possibilities for scholarship while raising questions about format-specific scholarly methodologies.
4. Collaborative Intelligence
AI transforms research from a primarily individual cognitive activity to a partnership between human and machine intelligences with complementary strengths. It enhances human-AI partnerships that leverage our respective capabilities—human creativity and contextual understanding paired with machine processing power and pattern recognition. This obsolesces complete human autonomy in information processing and solo knowledge work as exclusively human domains. The technology retrieves the ancient conception of tools as extensions of human capability, but now in the cognitive domain rather than merely the physical. Yet it risks reversing into potential loss of certain cognitive skills through atrophy and dependency on AI systems as humans outsource more intellectual functions to machines.
The collaborative nature of AI transforms libraries from facilitating information access to developing effective symbiosis between human and machine intelligence. This shift requires redefining expertise from "knowing things" to "knowing how to work with AI to accomplish complex cognitive tasks"—a profound change in information service philosophy.
C. From Content to Process
The abundance paradigm shifts library focus from content to process—from what information exists to how information is used. This transition parallels previous media revolutions; just as writing outsourced memory to physical media and print democratized access to recorded knowledge, AI outsources certain cognitive processes while creating new requirements for effective thinking.
Expertise itself requires redefinition. In traditional libraries, experts possessed information others lacked. In an AI world, expertise increasingly means understanding how to effectively direct, evaluate, contextualize, and apply AI-processed information toward meaningful goals. This shift challenges traditional library metrics focused on collection size or access statistics rather than impact on knowledge creation processes.
The core question becomes not "what information should libraries collect?" but "what cognitive processes should remain human, which should leverage AI, and how should these integrate?" This question has no historical precedent, as previous media extensions primarily affected information storage and transmission rather than cognitive processing itself.
The library must become a key enabler of what might be called "integrated intelligence"—the ability to combine human creativity, ethical reasoning, and contextual understanding with AI analytical capabilities toward meaningful research goals (Uzwyshyn, 2023).
D. Libraries as Cognitive Environments
McLuhan's insight that "we shape our tools and thereafter our tools shape us" has profound implications for how libraries function as cognitive environments. Just as physical libraries shaped thinking through classification systems, collection arrangements, and reference services, AI-integrated libraries create new cognitive contexts that influence research processes in fundamental ways.
Traditional libraries embodied what information scientist Marcia Bates called the "berrypicking" model of research—an iterative process where researchers gather information progressively, with each source influencing subsequent direction. AI-integrated libraries potentially transform this model through what might be called "parallel processing"—simultaneous engagement with hundreds or thousands of sources through AI synthesis.
This shift from serial to parallel information processing represents not just a quantitative change in research efficiency but a qualitative transformation in how knowledge is constructed. The library's role evolves from facilitating berrypicking to guiding effective parallel processing—helping researchers leverage AI's synthetic capabilities while maintaining critical awareness of how these systems shape understanding.
Walter Ong's analysis of the transition from orality to literacy provides another valuable framework for understanding AI's potential cognitive impact. Ong described how writing transformed human consciousness, enabling abstract thinking, categorical organization, and sequential logic unavailable to purely oral cultures (Ong, 1982). AI potentially represents a similar cognitive watershed—moving from literacy-based sequentiality to a more networked, associative form of thinking that integrates human and machine cognition.
The physical library as an "information space" designed for browsing, concentration, and serendipitous discovery finds new expression in AI-mediated digital environments. Libraries must consider how these environments shape cognitive processes, social interactions, and knowledge construction—designing not just for information access but for thought patterns that balance AI efficiency with human creativity and critical thinking.
III. A New Library Paradigm for the AI Era
A. Collections: From Ownership to Strategic Curation
The AI era requires a fundamental rethinking of collection development:
Strategic Focus: Develop computational collections suitable for text and data mining, with licensing explicitly permitting AI processing. Enhance metadata for AI discoverability and processing capability.
Expanded Definition: Collections now include research datasets, software, computational methods, AI models, training data, and dynamic collections updated through continuous data gathering.
Primary Assessment Criteria: Evaluate collections not just for content relevance but for AI processability, considering text quality, licensing terms, metadata completeness, and format compatibility.
Key Implementation: "The significant opportunities for digital transformation in a library setting are found through the use of specific AI technologies to improve... collections description and discoverability" (Pirgova-Morgan, 2023).
Institutions must build new digital infrastructures that support "open science and open datasets" as foundational elements for enabling AI discovery across disciplines. This requires creating interoperable systems where "the sum of the system's capabilities exceeds" what any individual component could provide (Uzwyshyn, 2023).
Following Ivan Illich's concept of "convivial tools" that enhance human autonomy rather than creating dependency, libraries should design collection systems that empower users to direct AI processing rather than constraining them within predetermined pathways (Illich, 1973). This approach aligns with the vision of "Digital Scholarship Ecosystems for Open Science" that maximize researcher agency while providing robust infrastructure support (Uzwyshyn, 2020).
B. Services: From Information Provision to Intelligence Partnership
Library services must evolve to incorporate AI across all functions:
AI-Enhanced Reference: Replace factual question-answering with expert guidance in effective AI interaction, prompt engineering, critical evaluation of AI outputs, and ethical AI use.
AI Literacy Instruction: Develop programs teaching the critical evaluation of AI-generated content, understanding how AI systems process information, ethical considerations in AI applications, and appropriate attribution practices.
Digital Scholarship Support: Create dedicated services for AI-enhanced research, including technical infrastructure, consultation on AI integration in research projects, and training in computational methods.
Implementation Example: The McGill University Library has developed a workshop series on AI literacy, ethics and bias using their "ROBOT test" (Reliability, Objective, Bias, Ownership, Type) to help users critically evaluate AI technologies and information about them (Hervieux & Wheatley, 2022).
Current frameworks for library AI development position the academic library as the ideal "third interdisciplinary space" for enabling AI services, bridging technical expertise with domain knowledge through "algorithmic literacy" programs that make these capabilities accessible to researchers outside computer science fields (Uzwyshyn, 2022).
This approach reflects the shift from content to process in the broader educational landscape—focusing not on what information users can access but on how they can effectively engage with AI-processed knowledge. The library becomes not just an information provider but an intelligence partner, supporting the development of "integrated intelligence" that combines human and machine capabilities.
C. Spaces: From Collections to Creation
Physical library spaces must be reimagined for the AI era:
Knowledge Creation Centers: Design collaborative environments for team-based computational projects, with technology-rich infrastructure supporting AI-enhanced research alongside spaces for human reflection and interaction.
Makerspaces and AI/Robotics Labs: Develop specialized facilities where students and faculty can experiment with emerging technologies, blending physical and digital creation. These spaces serve multiple purposes:
- Educational laboratories where students gain hands-on experience with technologies reshaping their fields
- Research and development centers fostering interdisciplinary collaboration and innovation
- Corporate partnership hubs connecting academic expertise with industry needs
- Community engagement spaces introducing broader publics to emerging technologies
In academic libraries, makerspaces have evolved beyond 3D printing to include AI and robotics components. Recent studies of academic makerspaces note these facilities now frequently incorporate "programming a human robot" exercises that teach coding principles and "robot hours" where students learn to control and program robots (EdTech Update, 2024). These activities build critical thinking and problem-solving skills while making AI concepts tangible.
The incorporation of robotics into library makerspaces represents a particularly valuable bridge between technical capabilities and public understanding. Libraries with robotics programs "provide opportunities for people to learn with their hands" in ways that develop the critical thinking skills essential for an AI-integrated world (ACRL, 2012).
These spaces reflect the emphasis on "embodied learning" in the AI era—recognizing that as information interaction becomes increasingly digitally mediated, physical engagement with technology becomes more, not less, important. The library makerspace provides crucial opportunities for sensory and tactile interaction with technologies that might otherwise remain abstract and mysterious.
Implementation Strategy: The University of Texas at Austin transformed a makeshift project into an institutional success by expanding its library makerspace into a program called the Foundry, demonstrating how physical innovation spaces can attract significant support and funding.
D. Expertise: New Professional Competencies
The AI era requires new skills and organizational structures:
Emerging Professional Roles:
- Data librarians specializing in computational collections
- AI literacy specialists developing critical educational programs
- Research software specialists supporting computational methods
- Digital ethics experts addressing responsible technology application
Hybrid Expertise Teams: Develop cross-functional groups combining traditional library expertise with data science skills, subject specialist knowledge, technical capabilities, and user experience design.
Continuous Learning Culture: Create formal professional development programs, communities of practice, experimental initiatives, and research partnerships to build capacity for ongoing adaptation.
Industry Challenge: "Today's rapidly evolving landscape requires transformational leadership that fosters innovative thinking and creates a culture of experimentation, where trials and errors are not just accepted but encouraged" (Davis, 2025).
Library professionals must develop forms of "meta-learning"—explicit strategies for learning how to learn with and about AI technologies (Uzwyshyn, 2022). This approach acknowledges that specific technical skills quickly become outdated while learning frameworks maintain relevance through technological evolution.
IV. Implementation Strategy: Pragmatic Steps Forward
A. Strategic Planning for AI Integration
Develop comprehensive strategies addressing:
Vision and Mission Alignment: Clearly articulate how AI capabilities connect to institutional mission and user needs, identifying priority areas for integration.
Ethical Frameworks: Establish guidelines for responsible AI application, including data privacy, algorithmic bias mitigation, and appropriate transparency.
Assessment Models: Design evaluation frameworks measuring impact rather than activity, with metrics connected to institutional goals.
B. Infrastructure Development
Invest in critical technological foundations:
Computational Resources: Provide access to necessary computing power through local infrastructure or cloud services.
Data Management Systems: Ensure information resources are structured for AI processability with appropriate metadata and formats.
API Access: Develop interfaces allowing computational use of library collections with authentication systems balancing access and security.
"Developing an Open Source Digital Scholarship Ecosystem" provides a practical blueprint for such infrastructure, identifying key components including research data repositories (e.g., Dataverse), digital collections systems (e.g., DSpace), identity management (e.g., ORCID), and user interface frameworks (e.g., Omeka) that together create a comprehensive ecosystem for AI-enhanced scholarship (Uzwyshyn, 2020).
C. Partnership Development
Create strategic relationships to extend capabilities:
Academic Departments: Partner with computer science, data science, and domain disciplines to leverage complementary expertise.
Industry Connections: Develop relationships with technology providers and potential employers of graduates.
Community Organizations: Engage with broader communities to address AI equity concerns and provide public education.
Multi-Institutional Collaborations: Participate in shared resource development, standards creation, and best practice identification.
D. Human-Centered Implementation
Ensure technology serves human needs through:
Staff Development: Provide comprehensive training in AI capabilities, limitations, ethical implications, and discipline-specific applications.
User-Centered Design: Involve diverse stakeholders in developing services and spaces that address actual needs rather than technological possibilities.
Progressive Rollout: Start with high-impact pilot projects demonstrating value before expanding to comprehensive implementation.
This human-centered approach reflects the emphasis on developing "convivial tools"—technologies that enhance human autonomy and creativity rather than creating dependency (Illich, 1973). By focusing on empowerment rather than replacement, libraries can develop AI implementations that augment rather than diminish human capabilities.
V. Conclusion: From Information Providers to Intelligence Partners
The dialogue that opened this article illustrates the tension between traditional library paradigms and emerging AI capabilities. This tension creates unprecedented opportunities for transformation.
Throughout history, libraries have evolved through technological revolutions—from manuscript to print, card catalog to database, physical to digital collections. Each transition required rethinking fundamental purposes while maintaining core values of knowledge access, critical inquiry, and preservation.
The AI revolution represents perhaps the most profound transformation yet, extending beyond information access to information processing itself. This requires reimagining libraries not as information providers but as intelligence partners—institutions helping scholarly communities leverage artificial intelligence while maintaining human values in knowledge creation.
The most promising direction is neither uncritical embrace of AI as universal solution nor resistance to technological change, but thoughtful integration that leverages AI capabilities while preserving distinctly human aspects of scholarship. Libraries can become laboratories for what might be called "augmented intelligence"—approaches that combine human creativity, ethical reasoning, and contextual understanding with AI analytical capabilities toward meaningful knowledge creation.
"This does change things, but in a very good way. Librarians, every decade or so, are getting good at dealing with an existential crisis of 'Do we need librarians?' But with this one they've been very open to embrace, discuss and analyze this" (Lankes, 2023).
In this vision, libraries transform from repositories of information to centers for intelligence—both artificial and human—working in partnership to address complex research questions. This transformation represents perhaps the most significant opportunity for academic libraries since the advent of digital information—the chance to redefine their essential role in scholarly communities for a new technological era.
"Most university faculty, graduate students and library staff working outside of Computer Science disciplines will require help to enable their data and research towards new AI possibilities," emphasizing the academic library's vital role as an interdisciplinary "third space" bridging technical capabilities with domain knowledge across disciplines (Uzwyshyn, 2022).
The historical perspective reveals that educational paradigms typically lag technological change by decades or even centuries. Libraries cannot afford a similar lag in adapting to AI. The pace of technological change and its implications for knowledge creation make rapid transformation an imperative rather than an option.
The most profound opportunity lies in leveraging AI to create library experiences more aligned with human cognitive needs than previous models ever achieved. Personalization, continuous feedback, multimodal interaction, and interdisciplinary integration represent not just technological possibilities but approaches better matched to how humans naturally learn and create knowledge.
In this sense, AI may enable not just library adaptation but library liberation—freeing information services from industrial-era constraints while expanding possibilities for creativity, critical thinking, and authentic contribution. This liberation, however, requires deliberate design rather than passive acceptance of technological change.
This redesign represents perhaps the most significant opportunity for academic libraries since the development of writing itself—the chance to develop human potential more fully by outsourcing certain cognitive functions while focusing attention on what makes us uniquely human: our creativity, ethical reasoning, interpersonal connection, and capacity for meaning-making in an increasingly complex world.
There are two versions of the article, the shorter version that just sticks to the main points
Shorter Version: https://lnkd.in/egxEc9QH
Recent Research Bibliography (2022-2025)
Association of College and Research Libraries. (2012). Makerspaces Move into Academic Libraries. ACRL TechConnect Blog. https://acrl.ala.org/techconnect/post/makerspaces-move-into-academic-libraries/
Association of College and Research Libraries. (2022). The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries. Edited by Sandy Hervieux and Amanda Wheatley. https://www.ala.org/news/2022/04/new-acrl-rise-ai-implications-and-applications-artificial-intelligence-academic
Association of Research Libraries. (2024). Research Libraries Guiding Principles for Artificial Intelligence. https://www.arl.org/news/association-of-research-libraries-releases-guiding-principles-for-artificial-intelligence/
Balnaves, E., Butrini, L., Cox, A., & Uzwyshyn, R. (Eds.). (2025). New Horizons in AI for Libraries. De Gruyter. [Open Access] https://www.degruyter.com/document/doi/10.1515/9783110774306/html
Boateng, F. (2025). The transformative potential of Generative AI in academic library access services: Opportunities and challenges. Journal of Academic Librarianship. https://journals.sagepub.com/doi/10.1177/18758789251332800
Cox, A. M. (2022). How artificial intelligence (AI) might change academic library work: applying the competencies literature and the theory of the professions. Journal of Documentation. https://www.researchgate.net/publication/359009445_How_artificial_intelligence_AI_might_change_academic_library_work_applying_the_competencies_literature_and_the_theory_of_the_professions
Davis, J. (2025). Artificial Intelligence (AI) and Academic Libraries: A Leadership Perspective. C&RL News, Association of College and Research Libraries. https://crln.acrl.org/index.php/crlnews/article/view/26490/34417
Dempsey, L. (2016). Library collections in the life of the user: Two directions. LIBER Quarterly, 26(4), 338-359. https://www.liberquarterly.eu/articles/10.18352/lq.10170/
Diamandis, P. H., & Kotler, S. (2012). Abundance: The future is better than you think. Free Press.
EdTech Update. (2024). Libraries and Robotics. https://www.edtechupdate.com/libraries/robotics/
Hervieux, S., & Wheatley, A. (2022). Separating Artificial Intelligence from Science Fiction: Creating an Academic Library Workshop Series on AI Literacy. https://www.choice360.org/libtech-insight/creating-an-academic-library-workshop-series-on-ai-literacy/
Illich, I. (1973). Tools for Conviviality. Harper & Row.
Lewis, D. W. (2020). The reimagined research library in the post-COVID era. EDUCAUSE Review.
McLuhan, M. (1964). Understanding media: The extensions of man. McGraw-Hill.
Molaudzi, A. I., & Ngulube, P. (2025). Use of artificial intelligence innovations in public academic libraries. Journal of Librarianship and Information Science. https://journals.sagepub.com/doi/full/10.1177/03400352241301780
Ong, W. J. (1982). Orality and literacy: The technologizing of the word. Methuen.
Pacific University Libraries. (2024). Navigating the Future: The Role of AI in Academic Libraries. https://www.lib.pacificu.edu/navigating-the-future-the-role-of-ai-in-academic-libraries/
Pirgova-Morgan, L. (2023). Looking towards a brighter future: the potentiality of AI and digital transformations to library spaces. University of Leeds. https://library.leeds.ac.uk/info/1607/projects/240/ai-in-libraries
Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
Tella, A., & Ajani, Y.A. (2022). Robots and public libraries. Library Hi Tech News, 39(7), 15-18. https://www.emerald.com/insight/content/doi/10.1108/lhtn-05-2022-0072/full/html
Uzwyshyn, R. (2016). Back to the Future: From Book Warehouse to Library Learning Commons. Informed Librarian, October 2016.
Uzwyshyn, R. (2020). Developing an Open-Source Digital Scholarship Ecosystem. International Conference on Education and Information Technology. https://www.researchgate.net/publication/336923249_Developing_an_Open_Source_Digital_Scholarship_Ecosystem
Uzwyshyn, R. (2022). New January 2022 Issue of Trends and Issues in Library Technology: Special Issue on Big Data. LinkedIn. https://www.linkedin.com/posts/rayuzwyshyn_trends-and-issues-in-library-technology-activity-6886318935759831040-usof
Uzwyshyn, R. (2022). Steps Towards Building Library AI Infrastructures: Research Data Repositories, Scholarly Research Ecosystems and AI Scaffolding. https://www.researchgate.net/publication/361425886_Steps_Towards_Building_Library_AI_Infrastructures_Research_Data_Repositories_Scholarly_Research_Ecosystems_and_AI_Scaffolding
Uzwyshyn, R. (2023). Academic Research Libraries and Enabling Artificial Intelligence: From Open Science and Datasets to AI and Discovery. https://www.researchgate.net/publication/366781239_Academic_Research_Libraries_and_Enabling_Artificial_Intelligence_From_Open_Science_and_Datasets_to_AI_and_Discovery
Uzwyshyn, R. (2023). Trends and Issues in Library Technology: AI, Cultural Heritage, Open Science. International Federation of Libraries Association (IFLA). https://www.researchgate.net/publication/366702047_Trends_and_Issues_in_Library_Technology_AI_Cultural_Heritage_Open_Science
Wilson, E. O. (1998). Consilience: The unity of knowledge. Knopf.