1. Direction of Travel: Libraries as AI‑Mediated Infrastructures
AI will not replace libraries; it will turn them into AI‑mediated public infrastructures for knowledge, learning, and civic life. The core shift is:
- from managing collections to orchestrating data, algorithms, and human expertise
- from teaching “how to search” to teaching “how to think with and about AI”
This happens under strong governance pressures: libraries sit at the intersection of public trust, commercial platforms, and fast‑moving AI regulation, so they must actively shape what “responsible AI” looks like in everyday information use, not just consume vendor tools passively.[1]
2. Discovery and Access: From Catalogs to Explainable, Conversational Systems
2.1 Semantic and conversational discovery
Discovery will move toward:
- natural‑language and semantic search across catalogs, repositories, and licensed resources
- conversational help that can reformulate questions, suggest terms, and guide users through complex databases
But future systems will need to explain why they surface particular items to maintain trust, especially as automated decisions become more opaque; work on algorithmic transparency shows that explainability, not just access to an algorithm, strongly shapes perceived trustworthiness in public services.[2]
2.2 Personalization without surveillance
AI‑driven recommendations (reading suggestions, “similar items,” tailored search filters) will increase, but library norms of privacy and intellectual freedom require:
- strict limits on individual tracking
- options to use non‑personalized search
- clear communication about what is logged and why
This is where libraries can model “minimal data, maximal benefit” practices that contrast with commercial platforms.
3. Collections, Repositories, and Platformization
3.1 AI‑assisted collection analytics and metadata
AI will support:
- evidence‑based acquisitions and cancellations via use, citation, and cost patterns
- automated extraction and enrichment of metadata (entities, topics, research methods)
- large‑scale classification and authority control across formats
The librarian role shifts toward validating machine outputs, curating vocabularies, and handling edge cases and special collections.
3.2 Repositories as platforms—and the risk of outsourcing
Research data and institutional repositories are already being “platformized” through commercial systems that centralize workflows, APIs, and analytics. This can free librarians from routine technical work but also shifts core preservation and data management functions to private vendors, with risks of deskilling, dependency, and precarity in library labor.[3] The future likely mixes:
- some local/open infrastructure (for mission‑critical or sensitive content)
- some vendor platforms (for scale and convenience)
- stronger contract clauses on data control, model training, and exit strategies
4. Services, Instruction, and Learning: Libraries as AI‑Literacy Hubs
4.1 AI‑augmented reference and research support
Reference services will become hybrid:
- AI chat for routine questions and wayfinding
- human librarians for complex search, synthesis, and sensitive topics
Evidence from other professional domains suggests that AI tools are accepted only when they are carefully fitted to existing values and workflows and when human judgment remains central.[4] Libraries will need close collaboration between technologists and frontline staff to avoid tools that look impressive but don’t match real reference work.
AI will also support
- semi‑automated screening and deduplication in systematic reviews
- basic text mining for humanities and social science projects
- summarization and highlighting of institutional research outputs
4.2 AI literacy and critical information skills
Libraries are well placed to lead AI literacy, understood as the ability to understand what AI systems do, how they work at a conceptual level, and how to use them critically and ethically.[5] Emerging syntheses highlight:
- growing consensus on core AI‑literacy concepts
- usable tools that do not require programming
- a gap around integrating ethics, policy, and social implications consistently[5]
Future library instruction will likely:
- teach how generative systems are trained, where hallucinations arise, and how bias enters
- distinguish between primary sources, human syntheses, and AI‑generated overviews
- address citation, authorship, and appropriate use of AI in academic work
- involve interdisciplinary collaborations with CS, education, and ethics units
Research from other education sectors shows that thoughtfully designed AI curricula and tools can both improve knowledge and reduce anxiety, including for under‑represented groups, when they emphasize collaboration and tangible, accessible activities.[6]
4.3 AI‑mediated learning materials
Libraries that support teaching will increasingly grapple with AI‑generated tutorials, videos, and exhibits. Early evidence suggests that AI‑generated voices and avatars can raise engagement and reduce extraneous cognitive load in some learning contexts, when designed coherently as a full AI package rather than piecemeal add‑ons.[7] Libraries will need media‑literacy frameworks for evaluating such content and deciding when to adopt, adapt, or reject it.
5. Assessment, Analytics, and “Invisible AI”
Behind the scenes, AI will:
- optimize room bookings, help‑desk routing, and staffing
- forecast demand for services or courses
- support learning analytics and automated assessment tools adopted by the institution
Work on AI‑assisted assessment shows these systems can approximate human ratings but often vary in reliability across categories and still require human oversight and calibration.[8] Libraries that integrate such tools into information‑literacy or research‑skills programs will need clear policies for:
- when humans must review or override automated judgments
- how students are informed about algorithmic scoring
- how bias and error are monitored over time
6. Public Service Mission: Digital Care, Inclusion, and Social Good
6.1 Digital care work in public libraries
Public libraries already act as “digital care” institutions—helping patrons navigate government portals, devices, and basic digital tasks, often as low‑status, emotionally demanding work.[9] As AI permeates welfare systems, education, and health, this care role will expand to:
- helping users understand and contest AI‑mediated decisions (benefits, visas, grading)
- translating opaque algorithmic processes into understandable terms
- supporting those excluded or misclassified by automated systems
6.2 Inclusive and participatory AI for social good
Libraries can champion participatory governance—involving communities in decisions about what AI tools are deployed, how data are handled, and what values guide system design—to ensure AI serves social good rather than deepening inequalities.[10] This may include:
- co‑design workshops with patrons around new discovery tools
- advisory boards that include marginalized communities
- public events on AI, democracy, and rights
Because libraries are among the most trusted civic institutions, they are natural venues for such conversations.
7. Governance, Policy, and Professional Ethics
7.1 Governing generative AI in library settings
Generative AI raises distinct issues—hallucinations, data leakage, IP conflicts, bias amplification, and concentration of power in a few vendors—that require proactive governance frameworks.[11] For libraries, this implies:
- explicit risk assessments before deploying generative features in discovery or reference
- data‑governance rules for what content and logs can be used to fine‑tune models
- labeling of AI‑generated content and clear user guidance on verification
- coordination with institutional legal and ethics bodies on copyright and fair use
More broadly, AI governance research stresses that governance must be adaptive and multi‑layered, combining regulation, organizational policies, technical controls, and professional norms.[1] Librarianship’s long‑standing commitments—to privacy, intellectual freedom, preservation, and openness—can anchor such frameworks.
7.2 Transparency and trust
Libraries will need to operationalize algorithmic transparency:
- documenting data sources, objectives, and known limitations
- offering user‑friendly explanations for recommendations or alerts
- providing appeal and feedback mechanisms
Research on public trust in algorithmic decisions indicates that explanations—clear reasons and criteria—matter more for perceived trustworthiness than simply disclosing that an algorithm is used.[2] This aligns well with librarians’ educative mission.
8. Evolving Professional Roles and Capacity Building
8.1 New competencies
Future librarians will need:
- baseline AI and data literacy (conceptual understanding of models, training data, and evaluation)
- skills in negotiating licenses that cover AI training and use of licensed content
- familiarity with platform architectures, APIs, and interoperability
- comfort with evaluating quantitative evidence on usage, effectiveness, and equity impacts
Experiences from fields like nursing education show that AI tools are increasingly used for profiling, prediction, and performance evaluation, and that research in these areas is dominated by quantitative assessment of system effectiveness.[12] Libraries should borrow methodological rigor but also insist on qualitative, user‑centered evaluation that surfaces unintended consequences.
8.2 Role reframing, not role loss
The profession will move:
- from doing (manual cataloging, routine reference)
- to designing, governing, and teaching (shaping systems, policies, and literacies)
To avoid deskilling and precarity, libraries will need explicit strategies for:
- recognizing and rewarding AI‑related expertise
- integrating “digital care” and AI‑literacy work into formal job descriptions
- ensuring that staff are co‑designers of AI implementations, not just end‑users
9. Strategic Priorities for the Next Decade
Libraries that want to shape, rather than simply absorb, AI developments can focus on:
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Values‑first AI strategy
- Articulate how AI supports access, equity, privacy, and intellectual freedom.
- Define red lines (e.g., no surveillance‑heavy personalization; no black‑box grading without appeal).
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Participatory design and governance
- Include staff, users, and community partners in tool selection and policy making.
- Use pilot projects with clear evaluation metrics (usefulness, bias, trust, inclusion).
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Infrastructure and openness
- Invest in open standards, APIs, and, where feasible, local or consortial AI infrastructure.
- Guard against lock‑in and loss of control over collections and user data.
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Capacity and culture
- Build AI literacy across all staff roles, not only in IT.
- Encourage experimentation within ethical and legal boundaries.
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Research and evidence
- Treat the library as a living lab for studying AI in public, educational, and research contexts.
- Publish on user experiences, equity impacts, and novel governance approaches.
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