Category Archives: AI Tools

One Out of Twenty Thousand

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What the JCR Impact Factor List 2025 tells us about India’s place in global scholarly publishing

I did not plan to spend an evening doing this. A 1,218-page PDF arrived on my screen, titled the JCR Impact Factor List 2025, and what started as idle curiosity turned into a few hours of data work, a few cups of tea, and a finding that I could not quite shake off even after I closed the laptop.

The Journal Citation Reports Impact Factor is not everything in scholarly publishing. I know that. You know that. Anyone who has spent time inside the library and information science community of India knows that we have been saying it for years. And yet, the JIF remains the currency that global funders, tenure committees, university rankings, and research assessment exercises reach for first. It is the number that follows a journal around like a shadow. So when a list of over twenty thousand journals ranked by this number lands in front of you, you look.

I extracted the data, cleaned it, deduplicated it, and then started asking it questions. The first question was simple: how many of these journals are published from India? The answer, after a careful two-pass classification, was 114 out of 20,449 indexed journals. That is 0.56 percent. A country that produces roughly nine percent of the world’s research output, with one of the largest populations of PhD holders on the planet, holds a little over half a percent of the world’s JCR-indexed journals.

Then I narrowed the question further. Of the 54 core Library and Information Science journals indexed in the JCR, how many are published from India? Here the number does not even reach single digits. It is one. Just one. Annals of Library and Information Studies, published by CSIR-NIScPR in New Delhi, sitting at an impact factor of 0.4 in the fourth quartile. That is the entirety of India’s LIS contribution to the JCR universe.

I want to be clear about what this is and is not. It is not a condemnation of Indian LIS scholarship, which is substantial and growing. It is not a claim that JIF is the only measure that matters. What it is, I think, is an honest mirror. One that is worth looking into together.

Methodology

The source dataset was the JCR Impact Factor List 2025, edited by Dr. Niaz Ali and distributed as a PDF of 1,218 pages. The document recorded six fields per journal entry: global rank, journal title, publisher name, ISSN, Journal Impact Factor (JIF), and JCR quartile. Inspection of the file established that actual data was confined to pages 1 through 608, with the remaining 610 pages being blank, an artefact of the original Excel-to-PDF export.

Data were extracted using the pdfplumber Python library, processing each page sequentially in batches of 200 pages to avoid memory saturation. The raw extraction yielded 29,223 records. Exact-match deduplication across all six fields removed 8,774 duplicate rows (30 percent of the raw total) that arose from pdfplumber’s table reconstruction behaviour on multi-line cells, leaving a working dataset of 20,449 unique journal records.

A recurring extraction artefact required remediation. Where a journal title was long enough to wrap across two lines within its source cell, pdfplumber’s column-boundary algorithm occasionally interleaved characters from the adjacent Publisher, ISSN, JIF, and Quartile columns with the continuation of the title. This produced two classes of error: truncation of journal titles, and character-level corruption of numeric fields. Numeric JIF values were recovered by regular-expression search for a contiguous digit-decimal-digit pattern; where none existed, constituent digits were extracted in document order and reassembled, a heuristic validated against the monotonic ordering of JIF values within tied-rank groups. Garbled publisher strings were resolved using a letter-multiset containment test, treating each string as an unordered character bag and checking whether it contained the complete character set of a candidate publisher name. Truncated titles were corrected manually by cross-reference with the leaked characters visible in the adjacent corrupted Publisher field.

Identification of India-published journals proceeded in two passes. An initial pass flagged records whose Publisher field contained the strings INDIA, INDIAN, or MEDKNOW, excluding false positives such as Indiana University and Euskaltzaindia. This returned 44 records. A subsequent review, prompted by the absence of Annals of Library and Information Studies from this list (its publisher, CSIR-NIScPR, contains neither string), extended the classification to a three-criterion model: publisher string matching as before; publisher field matching a manually curated allowlist of known Indian institutional publishers; and journal title containing the word INDIAN or terminating in -INDIA on a word boundary. A total of 134 candidate records were individually reviewed; 20 were excluded as false positives (CSIRO via CSIR, University of Barcelona via BARC, and titles referencing Indigenous North American or West Indian contexts). The final list comprised 114 journals.

Limitations

This analysis operates entirely on publisher and title string inference because the JCR dataset carries no explicit country-of-origin field. Classification should therefore be treated as a best-effort approximation rather than authoritative country attribution. Journals are classified by the apparent national origin of the responsible academic society, not the registered domicile of the multinational publisher that may distribute them. A journal published by an Indian society but distributed by Elsevier or Springer under a generic imprint name, without India visible anywhere in the publisher string, would be missed by this methodology.

More fundamentally, the JCR list indexes only journals that receive a Journal Impact Factor through the Web of Science Core Collection. A significant body of Indian scholarly periodicals is indexed instead through Scopus, UGC-CARE, or DOAJ, and these do not appear in this dataset at all. The count of 114 should therefore be read as a floor, not a ceiling, and the analysis that follows as a measure of India’s presence in one specific tier of global scholarly publishing infrastructure, not a comprehensive census of Indian academic journals.

The World’s Top 20 Journals by JCR Rank (2025)

Before we arrive at India, it is worth stepping back and seeing what the top of this list looks like. The range of impact factors is staggering. The journal at rank one, CA-A Cancer Journal for Clinicians, carries an impact factor of 232.4, meaning that on average each of its articles is cited over two hundred times in a year. The twentieth journal on the list, Chemical Reviews, stands at 55.8. Every single entry in the top twenty is Q1, every single one is published from the United States or Western Europe, and Nature Portfolio alone accounts for eleven of the twenty slots.

What you are looking at is a concentration of publishing power that has been building for decades. Nature Portfolio, Elsevier, Wiley, and the American Chemical Society between them account for the overwhelming majority of the highest-ranked journals in the world. These are not just publishers. They are infrastructure. And access to that infrastructure, for journals from the Global South, remains uneven.

India’s 114 JCR-Indexed Journals (2025)

Of the 114 journals India contributes to the JCR list, the majority are distributed through Springer India, Wolters Kluwer’s Medknow imprint, or the Indian Academy of Sciences. The highest-ranked Indian journal is the Indian Journal of Dermatology, Venereology and Leprology, which sits at global rank 3,957 with an impact factor of 3.4, comfortably in Q1. The Journal of the Indian Chemical Society, also at rank 3,957 with a JIF of 3.4, shares that top position. Below them, the list thins rapidly into Q3 and Q4 territory.

Twelve of India’s 114 journals sit in Q1 or Q2. The remaining 102 are spread across Q3, Q4, and journals with no quartile assigned because their impact factor was either below the reportable threshold or not yet established. It is a picture of a publishing ecosystem that has genuine strengths in medicine, basic sciences, and a few applied disciplines, but has not yet built the breadth or depth that would make it a significant force in global citation metrics.

What Needs to Change: A Case for Strategic Government Intervention

The 114-journal count is not destiny. Other countries have moved this needle deliberately, through policy, funding, and institutional design. South Korea’s journal ecosystem grew substantially after the government introduced mandatory open-access infrastructure and tied research assessment to journal quality development, not just publication volume. Brazil built SciELO, a continental scientific publishing platform, in partnership with FAPESP and the Pan American Health Organisation, and today operates one of the largest open-access journal networks in the world. China’s investment in its national journal infrastructure over two decades is visible in the JCR list in ways that would have seemed implausible in 2000. India can do this. The question is whether we are ready to treat journal publishing as research infrastructure rather than an afterthought.

The following interventions, if implemented with seriousness and sustained funding, could shift India’s position in global scholarly publishing meaningfully within a decade.

1. A National Journal Quality Development Mission.

DST, UGC, and the Ministry of Education should jointly establish a Mission for Academic Journal Excellence, modelled loosely on the UK’s Research Excellence Framework but oriented toward journal development rather than research assessment. The mission would identify 50 to 100 high-potential Indian journals currently indexed in Scopus or UGC-CARE and provide them with five-year competitive grants covering editorial infrastructure, production quality improvement, international peer-review expansion, and indexing application support. The target would be JCR indexing for all mission journals within the grant period.

2. A National Open-Access Publishing Platform.

India needs its own SciELO. A government-funded, nationally administered open-access platform, hosted and maintained by an institution such as CSIR-NIScPR or IISc, would allow Indian learned societies and universities to publish their journals without dependence on expensive multinational distributor contracts. INFLIBNET has the technical capacity; what is needed is the political will to fund it at the scale required. Such a platform would also resolve the discoverability problem: Indian journals currently scatter across dozens of institutional websites with inconsistent metadata, broken DOIs, and irregular publication schedules. Centralisation would fix this.

3. Mandatory DOI Registration and Metadata Standards.

A journal without a DOI is essentially invisible to citation-tracking infrastructure. The UGC should make DOI registration mandatory for all journals seeking UGC-CARE listing, and should negotiate a bulk membership arrangement with CrossRef to subsidise registration costs for smaller Indian learned societies that currently cannot afford them. Simultaneously, a national metadata standard for Indian journals, aligned with Dublin Core and the JATS XML standard used by PubMed Central, should be mandated. Poorly structured metadata is one of the most common reasons Indian journals fail at the indexing application stage.

4. Editorial Capacity Building Programmes.

Many Indian journals have excellent content and committed editors who are simply not trained in the operational realities of international journal management: structured peer review workflows, COPE (Committee on Publication Ethics) compliance, conflict-of-interest management, retraction handling, and the technical requirements of indexing bodies. ICAR, CSIR, and the Indian National Science Academy should jointly sponsor annual editorial training programmes, and professional societies like ILA and IAMI should bring equivalent capacity-building into the LIS domain. A journal’s impact factor begins with the integrity of its editorial process.

5. Reform of Academic Promotion Criteria.

We cannot separate the journal ecosystem from the incentive structure that feeds it. As long as faculty promotion in Indian universities rewards the number of publications above the quality of the journals in which they appear, and as long as editing a national journal carries no credit in promotion committees, the pipeline of experienced editors and rigorous peer reviewers will remain thin. The UGC’s Academic Performance Indicators need a revision that explicitly values editorial contribution, peer review service, and journal development work as measurable academic outputs.

6. A Special Focus on LIS and Humanities Journals.

Science and medicine dominate India’s current JCR presence. The social sciences, humanities, and library science are almost entirely absent. ICSSR should establish a dedicated journal development fund for social science and humanities journals, with explicit performance milestones tied to Scopus and eventually JCR indexing. The LIS community in particular, with its expertise in metadata, indexing, and information architecture, is paradoxically well-placed to understand what makes a journal discoverable and citable. We should be leading this conversation, not observing it from the margins.

A Closing Thought

I started this piece with a 1,218-page PDF and ended up somewhere I did not entirely expect: thinking about what it means to build a knowledge infrastructure for a country of 1.4 billion people. Impact factors are an imperfect tool, and everyone in this room knows their limitations. But they are also a signal. And the signal from the JCR Impact Factor List 2025 is that India’s scholarly journals, for all the genuine research they carry, are not yet visible in the places where global scholarly conversations are being measured.

114 journals out of 20,449 is not failure. It is a starting point. What we do with that starting point is a policy choice, not a destiny. The researchers are here. The societies are here. The institutional memory, the networks, the LIS professionals who have spent careers building access to knowledge, all of it is here.

What we need now is for the people who control the budgets and the policy levers to look at the same number I looked at that evening, 114 out of 20,449, and decide that it is time to change it.

The Library Is Learning to Act: Agentic AI in Libraries

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From information access to intelligent task execution — what every library professional needs to know.

I still remember the days when I would walk into a library with a question in mind, move from catalogue to shelf, and slowly piece together meaning on my own, the system gave me access, but the responsibility to interpret always stayed with me. No wonder, we built our entire professional identity around this model.

But somewhere along the way, quietly and steadily, this arrangement has begun to change.

Today, we are not just talking about better search or faster discovery. We are witnessing a shift from information access to intelligent task execution. What we once navigated ourselves, machines are now beginning to navigate on our behalf.

Let me explain this journey as I have come to understand it.

In the early phase, our systems, OPACs, databases, catalogues, they were efficient, but limited. They responded to queries, but they never understood intent. That gap between what the user meant and what the system retrieved, that was our space as librarians. That was our value.

Then came the first wave of AI. Chatbots, recommendation tools, smarter interfaces. They made things easier. They reduced effort. But they still depended on the user to drive the process. They could assist, but not act.

Now, we are entering a new phase. Agentic AI.

This is where things become interesting. And slightly unsettling.

These systems do not stop at answering questions. They take a goal, break it into steps, use multiple tools, check results, and refine their own process. In simple terms, they do the work. The model has shifted from asking and receiving to planning and executing.

By the way, this does not mean humans are out of the picture. It means the role of navigation is shifting, from the user to the system, but always under human supervision.

When I first explored how these systems actually function, I realised they are not magic. They are structured, layered systems.

First, they ingest data. PDFs, scanned texts, MARC records, web content. They clean it, structure it, and make it searchable.

Second, they think, or at least simulate thinking. They take a goal and create a plan. They break tasks into steps and track progress.

Third, they connect. To Koha, DSpace, CrossRef, ORCID, and many more systems. Their power grows with every connection.

And finally, they act. Not by giving you text alone, but by producing usable outputs, catalogue records, bibliographies, reports, corrections. Outputs that enter real workflows.

Just imagine what this means for our daily work.

Cataloguing, which once demanded hours of manual effort, is now being partially handled by AI systems that extract metadata, suggest classifications, and prepare draft records. Still, human review remains essential. Accuracy without oversight is a risk we cannot afford.

Subject classification is evolving too. Tools like Annif learn from existing catalogues and apply that knowledge to new materials. But their effectiveness depends heavily on the data we provide, especially for regional and multilingual collections.

Reference services are also changing. A single query can now trigger a chain of actions, retrieving records, analysing sources, and generating structured reading lists. Yet, when it comes to deep research guidance or sensitive queries, the human touch remains irreplaceable.

Collection development has become more data-driven. AI can identify gaps, track demand, and suggest acquisitions with measurable impact. This was difficult to achieve at scale earlier.

And then there is workflow automation, the silent transformation. Deduplication, metadata enrichment, batch processing. These are not glamorous tasks, but automating them saves significant time. In some institutions, up to 50 percent efficiency gains have been reported.

Of course, with every opportunity comes risk.

I have seen how AI can confidently produce incorrect information. Fabricated references, wrong classifications. Without proper checks, this can damage trust.

Bias is another concern. Systems learn from past data, and past data carries historical bias. If we are not careful, we risk reinforcing outdated or unbalanced perspectives.

Privacy also demands attention. User queries, reading habits, borrowing patterns, these are sensitive. Libraries have always protected this data. We must continue to do so, even in AI-driven environments.

And then there is vendor lock-in. If we lose control over our systems and data, we compromise our independence. Open standards and flexible systems are not optional, they are necessary.

All this brings me to what I feel is the most important part, the human side.

AI will not replace librarians. But it will change what we do.

Routine, repetitive tasks will reduce. But roles will evolve.

Cataloguers will supervise AI outputs. Reference librarians will engage in deeper research support. Collection specialists will rely more on data and analytics.

This means we must learn new skills. Prompting, evaluating AI outputs, understanding data, working with APIs. Alongside this, we must also engage with policy, ethics, and privacy.

These are not technical add-ons. These are becoming core professional competencies.

If you ask me how to begin, I would say start small.

Pick one use case. Test it. Learn from it.

Then expand gradually. Introduce human oversight. Train your team.

Integrate systems carefully. Build connections through APIs.

And only then think about optimisation and scale.

This is not a race. It is a structured transition.

Looking ahead, I see a phased future.

In the next few years, assisted cataloguing and automated workflows will become common. Reference queries will increasingly be handled by AI systems.

A little later, we will see multiple AI agents working together across different library functions.

And in the long run, we may see deeply integrated systems with strong institutional memory, almost like AI co-librarians.

But let us be clear about one thing.

Fully autonomous libraries are not realistic. Nor are they desirable.

What will succeed is a balanced approach. AI systems that handle defined tasks. Humans who guide, supervise, and take responsibility.

In the end, I do not see this as a story of replacement. I see it as a story of augmentation.

The library has always stood for access, equity, and intellectual freedom. These values do not change. What changes is how we deliver on them.

And perhaps, for the first time, we are not just helping users navigate knowledge. We are building systems that can navigate it with us.

That is a powerful shift. And a responsible one.

Ethical Use and Disclosure of Artificial Intelligence Tools in Academic Research Writing

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Ethical Use and Disclosure of Artificial Intelligence Tools in Academic Research Writing: Evidence and Guidance from Library and Information Science

Abstract:
The use of generative artificial intelligence tools in academic research writing has become widespread across disciplines, including library and information science. While these tools are increasingly employed for drafting, language refinement, and structural assistance, disclosure practices remain inconsistent. Non-disclosure of AI use poses greater ethical and reputational risks than transparent acknowledgement. Drawing on recent published evidence from library and information science journals, this post demonstrates that ethical disclosure does not hinder publication. Further, it proposes a practical checklist to guide responsible AI use and supports the integration of AI disclosure literacy into LIS education and research practice.

Keywords:
Artificial intelligence, academic writing, research ethics, disclosure, library and information science, generative AI

Introduction:
Generative artificial intelligence tools have rapidly entered academic writing workflows. Their presence is now routine rather than exceptional. Researchers across career stages use AI-based systems to refine language, reorganise arguments, summarise notes, and support early drafting. In library and information science, a discipline grounded in information ethics and scholarly integrity, this shift raises urgent questions about responsible use and disclosure.

The central ethical challenge is not the use of AI itself, but the reluctance to acknowledge such use. A significant number of researchers employ AI tools without disclosure due to uncertainty about ethical boundaries or fear of manuscript rejection. This hesitation overlooks the greater long-term risk associated with post-publication scrutiny and potential retraction.

The Real Risk Lies After Publication:
Academic publishing has entered an era of heightened transparency and accountability. Publishers increasingly deploy detection mechanisms, reviewers are more alert to stylistic patterns associated with generative models, and post-publication review has intensified.

Retraction notices are public, permanent, and professionally damaging. They affect an author’s credibility, institutional trust, and future opportunities. In contrast, manuscript rejection is a routine academic outcome that allows revision and improvement. From both ethical and pragmatic perspectives, non-disclosure of AI use represents a higher-risk decision.

Evidence from Published Library and Information Science Research:
Concerns that disclosure leads to rejection are not supported by recent evidence. Meaningful examples from 2025 demonstrate transparent AI acknowledgement in reputable LIS publications.

Del Castillo and Kelly acknowledged the use of QuillBot for grammar, syntax, and language refinement, and Google Gemini for title formulation, in a paper published in College and Research Libraries [1].


McCrary declared the use of generative AI for initial drafting and language polishing in The Journal of Academic Librarianship, while retaining full responsibility for content accuracy and originality [2].


Islam and Guangwei reported the use of ChatGPT for data visualisation support and summary drafting in SAGE Open, explicitly accepting authorial responsibility [3].

Sebastian disclosed the use of ChatGPT-4o for drafting and refining ideas in an American Library Association publication, emphasising full human control over arguments and conclusions [4].

Aljazi acknowledged the use of ChatGPT for language refinement and summarisation in Information and Knowledge Management, in accordance with journal guidelines [5].

Beyond LIS, You et al. reported the use of generative AI for language improvement in Frontiers in Digital Health, reflecting broader acceptance of transparent disclosure across disciplines [6].

These cases share common features. AI tools are named. Tasks are clearly defined. Intellectual accountability remains with the authors. Disclosure did not prevent publication.

Ethical Use Does Not Require Avoidance: Ethical engagement with AI does not require abstention. It requires boundaries. Generative AI tools are unsuitable for disciplinary judgement, methodological reasoning, and interpretive analysis. These remain human responsibilities.

AI tools perform effectively in surface-level tasks such as grammar correction, clarity improvement, and structural suggestions. Ethical violations occur when AI is used to fabricate data, invent citations, generate unverified claims, or replace scholarly reasoning. In library and information science, where trust and attribution are foundational, such misuse directly contradicts professional values.

Disclosure as Professional Safeguard: Transparent disclosure demonstrates academic integrity, aligns with journal policies, and protects authors from allegations of misconduct. Many journals now explicitly request disclosure of AI use. Where policies are unclear, transparency remains the safer course. Silence is increasingly interpreted as concealment.

Reading and Interpreting Journal Policies: Failure to consult instructions to authors is a common cause of ethical lapses. Researchers must examine journal policies carefully, focusing on ethics statements, authorship criteria, and AI-related guidance. Key questions include permitted uses, disclosure format, and placement of acknowledgements. Policy literacy is now an essential research skill.

A Practical Ethical Checklist for Researchers:
The following checklist reflects current LIS norms and publishing expectations:

  • Conduct intellectual framing and argumentation independently
  • Use AI strictly as a support tool
  • Never use AI to invent data, results, or interpretations
  • Never allow AI to fabricate citations or references
  • Verify every reference and factual claim manually
  • Limit AI use to language clarity and structural assistance
  • Review and revise all AI-assisted text
  • Retain full responsibility for originality and accuracy
  • Read and follow journal author guidelines carefully
  • Disclose AI tools, purpose, and stage of use explicitly
  • Prefer rejection over undisclosed AI use and later retraction

Writing an Effective AI Acknowledgement:
An AI acknowledgement should be concise and factual. It should name the tool, specify the task, and indicate the stage of use. It should clearly state that the author retains responsibility for the final content. The published examples cited above [1]–[5] provide effective models.

Implications for LIS Education and Practice:

Library and information science educators and professionals play a central role in shaping ethical research behaviour. AI literacy education must extend beyond tool operation to include disclosure norms, policy interpretation, and risk awareness. Embedding these issues into research methods courses and scholarly communication training will strengthen ethical practice across the discipline.

Conclusion: Generative AI tools are now embedded in academic writing workflows. The ethical question is no longer whether researchers use them, but whether they do so transparently and responsibly. Disclosure protects scholarly credibility. Concealment exposes researchers to long-term risk.

References:

[1] M. S. Del Castillo and H. Y. Kelly, “Can AI Become an Information Literacy Ally? A Survey of Library Instructor Approaches to Teaching ChatGPT,” College & Research Libraries, vol. 86, no. 2, 2025.
Available: https://crl.acrl.org/index.php/crl/article/view/26938/34834

[2] Q. D. McCrary, “Are we ghosts in the machine? AI, agency, and the future of libraries,” The Journal of Academic Librarianship, vol. 51, no. 3, 2025.
Available: https://www.sciencedirect.com/science/article/pii/S0099133325001776

[3] M. N. Islam and H. Guangwei, “Trends and Patterns of Artificial Intelligence Research in Libraries,” SAGE Open, vol. 15, no. 1, 2025.
Available: https://journals.sagepub.com/doi/10.1177/21582440251327528

[4] J. K. Sebastian, “Reframing Information-Seeking in the Age of Generative AI,” American Library Association, 2025.
Available: https://www.ala.org/sites/default/files/2025-03/ReframingInformation-SeekingintheAgeofGenerativeAI.pdf

[5] Y. S. Aljazi, “The Role of Artificial Intelligence in Library and Information Science: Innovations, Challenges, and Future Prospects,” Information and Knowledge Management, vol. 15, no. 2, 2025.
Available: https://www.iiste.org/Journals/index.php/IKM/article/download/63557/65692

[6] C. You et al., “Alter egos alter engagement: perspective-taking can improve disclosure quantity and depth to AI chatbots in promoting mental wellbeing,” Frontiers in Digital Health, vol. 7, 2025.
Available: https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1655860/full

Why AI Needs Librarians More Than Ever

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One fine morning, a young lady called me. Her voice carried anxiety. I assumed it was the usual exam pressure. I told her honestly that I was not a psychologist and might not help her deal with anxiety. She stopped me immediately.

“No sir,” she said. “It is not about the exam.”

She told me she was in her final year of Library and Information Science. She had been watching my videos where I explain how artificial intelligence makes a researcher’s life easier. Faster discovery. Instant summaries. Easy access to information. Then she asked a question that stayed with me long after the call ended.

“If researchers get information so easily,” she asked, “who will come to libraries? And if nobody comes, who will hire librarians like me?”

That single question captures the fear many students, early-career professionals, and even senior librarians are quietly carrying today. It deserves a clear, practical answer.

To understand this properly, we need to slow down and separate hype from reality.

Artificial intelligence is powerful. It can summarise books, answer questions, and even draft research papers. It speaks confidently. That confidence often misleads us into believing the answers are correct. This brings us to the first and most important lesson.

AI gives answers.
It does not give judgement.

An AI system does not know whether information is biased, incomplete, outdated, or ethically problematic. It predicts text based on patterns in data. If the output sounds fluent, the system considers its job done. Truth, context, and consequence are not part of its thinking.

Judgement still belongs to humans. And judgement has always been at the core of librarianship.

This leads to the second lesson, one many people underestimate.

Traditional library skills are not outdated.
They are AI-era skills.

Take cataloguing. Many students see it as mechanical and irrelevant. In reality, cataloguing is structured thinking. It is about describing information so others can find it, understand it, and trust it. Today, AI systems depend on exactly this kind of structure.

AI models need clear documentation.
They need clean metadata.
They need transparency about data sources and limitations.

Without these, AI becomes a black box. Librarians have been preventing black boxes for decades.

The same applies to information retrieval. Long before AI existed, librarians taught users how to search effectively, refine queries, evaluate sources, and understand context. Modern AI search works well only when someone understands relevance and authority. That skill has not disappeared. It has become more valuable.

Then there is ethics.

Libraries have always stood for access, equity, privacy, and intellectual freedom. These are not optional values in the AI age. They are essential safeguards. AI systems amplify bias, exclude voices, and compromise privacy if left unchecked. Librarians already know how to question systems, not worship them.

This is why an important shift is taking place.

Librarians are no longer only users of AI.
They are becoming the human infrastructure behind AI.

They ensure systems are transparent.
They ensure systems are fair.
They ensure systems serve people, not mislead them.

This is not a future scenario. It is already happening.

A 2025 Clarivate report shows that 67 percent of libraries are already exploring or actively using AI. Libraries now operate in a research ecosystem where AI tools scan thousands of papers, extract data, generate cited answers, and map research connections visually.

These tools save time. They also confuse users. Researchers often do not know where answers come from, what was excluded, or what assumptions were made. Someone must explain this clearly.

That responsibility naturally falls on librarians.

Behind the scenes, AI is also reshaping library operations. Metadata creation, cataloguing, and collection management are increasingly automated. A system can generate records. A model can catalogue a book from an image. This does not remove librarians from the system. It removes repetitive labour.

What replaces it is higher-value work.

Advanced research support.
Teaching AI and information literacy.
Community programmes.
Policy guidance and ethical review.

Another fear needs addressing.

Many people assume AI will reduce the importance of libraries. In practice, it often expands access.

In India, mobile AI labs travel to remote villages. They do not replace libraries. They work alongside traditional village libraries. Technology moves, but trust remains local. Libraries become bridges between advanced tools and real communities.

At the same time, we must speak honestly about AI’s weaknesses.

One term everyone must understand is AI hallucination. This occurs when a system produces fluent but false information. There is no intent to deceive. Accuracy is sacrificed for smooth language.

The consequences are serious. Researchers have wasted hours chasing references that never existed, created entirely by AI. Proving that a source does not exist takes time and energy away from meaningful work. This feeds what many experts now call the slop problem, where low-quality AI content floods the internet and academic publishing. Trust erodes. Reviewers burn out. Good research gets buried.

So the practical question becomes unavoidable.

Why does AI still need librarians?

Because someone must teach critical evaluation.
Because someone must audit bias.
Because someone must protect privacy.
Because someone must identify fake citations.
Because someone must uphold intellectual freedom.

AI does not understand these responsibilities. Librarians do.

This brings us to the transformation of the profession.

The librarian is no longer a gatekeeper of information.
The librarian is a supervisor of AI systems.

The librarian is no longer only a reference desk expert.
The librarian is an AI literacy educator.

The librarian is no longer only a collection manager.
The librarian is an ethical evaluator of everyday tools.

The most accurate description of this role is information architect. Someone who designs, audits, and oversees how knowledge is created, accessed, and trusted.

This transformation requires investment. Not only in technology, but in people. The AI-ready workforce will be built, not bought. It will emerge through reskilling, confidence building, and empowering professionals who already understand information deeply.

When I think back to that anxious student, I no longer see a profession in danger. I see a profession at a turning point.

AI delivers answers faster than ever.
But society still needs someone to teach how to question those answers.

That responsibility has always belonged to librarians.

And it still does.

Click and Catalogue Books: Your AI-Powered Library Cataloguing Assistant

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Artificial Intelligence is transforming every profession, and librarianship is no exception. With Custom GPTs in ChatGPT, you can now create specialized AI assistants that perform targeted professional tasks. A Custom GPT is not a generic chatbot—it’s a tuned version of ChatGPT designed with specific instructions, reference data, and workflows to carry out specialized jobs efficiently.

I’ve built one such assistant, called Click and Catalogue Books, specifically for librarians and cataloguers. It automates the complete process of book cataloguing—from classification to MARC record generation—by using the power of AI.

What Makes Click and Catalogue Books Unique

This Custom GPT replicates the intellectual process of a professional cataloguer in seconds. Here’s what it does step by step:

  • Identifies bibliographic data from photos of the Title page and Verso page.
  • Classifies the book using the Dewey Decimal Classification (DDC) system. It analyses the subject, determines the correct class number, and provides it with precision.
  • Generates a Cutter number to represent the main entry (usually the author).
  • Synthesizes the call number by combining the DDC class number and the Cutter number—an operation that typically takes a trained cataloguer several minutes. Here, AI completes it instantly.
  • Assigns subject headings based on the Sears List of Subject Headings, ensuring standardization and consistency in subject access.
  • Displays metadata in AACR II format, including author, title, edition, publication details, physical description, and subject entries.
  • Generates a complete MARC record, ready for download and direct upload to your Library OPAC.

What once took hours of manual analysis and data entry is now handled in seconds with remarkable accuracy.

Traditional cataloguing is a time-consuming process that requires specialized knowledge of AACR II, DDC, Sears List, and MARC standards. Many small or rural libraries lack trained cataloguers or cannot afford expensive automation software.

Click and Catalogue Books bridges this gap by providing:

  • Instant cataloguing from mobile devices
  • Reduced cataloguing backlog for new acquisitions
  • Accurate and standardized metadata
  • Interoperability with OPAC and library management systems
  • Support for non-technical staff in rural and small institutional setups

The GPT acts like a virtual cataloguer—fast, reliable, and accessible from anywhere.

How to Use Click and Catalogue Books

  1. Open the ChatGPT app on your mobile phone.
  2. Tap Explore GPTs and select Click and Catalogue Books or go directly to:
    https://chatgpt.com/g/g-6909a9715c808191862570c20599a968-click-and-catalogue-books
  3. Take clear photos of the Title page and Verso page of the book.
  4. Submit them to the GPT.
  5. In a few seconds, you’ll receive:
  • DDC class number
  • Cutter number
  • Synthesized call number
  • Standard subject headings (Sears)
  • AACR II metadata display
  • Complete MARC record ready for download

You can then download the MARC file and upload it directly into your Library OPAC or cataloguing module.

A Step Toward AI-Integrated Librarianship

This Custom GPT is more than a tool—it’s a practical example of how AI can assist librarians in core professional tasks. It merges cataloguing standards, bibliographic intelligence, and natural language understanding into one seamless workflow.

Click and Catalogue Books shows that cataloguing no longer has to be a slow, manual process. AI now performs hours of intellectual work in seconds, with consistency and accuracy.

AI and Libraries in October 2025: Key Developments, Impacts, and Trends

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When I look back at how libraries have evolved through 2025, it feels as if artificial intelligence has quietly rewritten the script of librarianship. The year began with cautious experimentation, but by October, AI had become deeply embedded in daily operations from cataloging to digital preservation. [1]. 

Libraries today are not merely using AI; they are living with it. Academic and public institutions alike are automating repetitive workflows, freeing librarians to engage more deeply with research and pedagogy. AI chatbots now answer common queries instantly, while smart systems recommend books based on nuanced user behavior [2], [3]. Some libraries even deploy small robots to navigate aisles, performing inventory or retrieval tasks with quiet precision [2]. Behind these visible changes lies something subtler—the shift toward algorithmic decision-making in information services, where metadata creation, classification, and even preservation strategies are driven by learning models [4]. 

Of course, all this progress comes with questions. As one recent SAGE report revealed, only 7% of academic libraries are using AI tools regularly, even though over 60% are exploring adoption strategies [5]. This gap reflects both hesitation and hunger.  Frameworks like the ‘ACRL’s AI Competencies for Academic Library Workers’ [6] have become so timely. They emphasize three things , understanding the logic of AI systems, applying them ethically, and translating their potential into academic value. No wonder librarians are now seen as mediators between human inquiry and algorithmic intelligence [7]. 

What’s fascinating is how fast generative AI has entered the scholarly mainstream. Tools like ChatGPT, Gemini, and Copilot are no longer novelties—they are part of everyday academic life [1]. I’ve seen libraries incorporate AI literacy into information literacy courses, teaching students not just how to ‘use’ AI but how to ‘question’ it. The University of Michigan’s AI pedagogy model and Europe’s LIBRA.I. network exemplify this shift toward guided experimentation [8]. In parallel, the idea of ‘machine-readable scholarship’ is emerging. AI can interpret and link research dynamically [9]. 

This convergence of AI, libraries, and academia reminds me that technology alone doesn’t define progress but our response to it does. By collaborating with researchers and technologists, libraries are helping shape the ethical contours of AI use. Whether it’s enabling responsible data governance or supporting cross-disciplinary projects [10], the librarian’s role is evolving from custodian to catalyst. 

In the end, AI hasn’t replaced the librarian rather it has reimagined the profession. The library remains, as ever, a place of trust, but now it hums with the quiet intelligence of algorithms working behind the scenes. As we move toward 2026, the task is not just to deploy AI, but to ensure that it continues to serve the human spirit of curiosity and learning.  

—

*References* 

[1] Liblime, “How Libraries Are Leading the AI Revolution,” Oct. 2025. Available: https://liblime.com/2025/10/04/how-libraries-are-leading-the-ai-revolution/ 

[2] IJSAT, “Adoption of Artificial Intelligence in Academic Libraries in African Universities: A Scoping Review,” Sep. 2025. Available: https://www.ijsat.org/research-paper.php?id=8003 

[3] JST, “Survey to Measure the Effectiveness of Utilizing Artificial Intelligence and Data Analysis in Improving Knowledge Management in Omani Information Institutions and Libraries,” Apr. 2025. Available: https://journals.ust.edu/index.php/JST/article/view/2822 

[4] JKG, “Digital Preservation Strategies in Academic Libraries: Ensuring Long-Term Access to Scholarly Resources,” Apr. 2025. Available: https://jkg.ub.ac.id/index.php/jkg/article/view/31 

[5] SAGE Publishing, “New Technology from Sage Report Explores Librarian Leadership in the Age of AI,” May 2025. Available: https://www.sagepub.com/explore-our-content/press-office/press-releases/2025/05/20/new-technology-from-sage-report-explores-librarian-leadership-in-the-age-of-ai 

[6] ALA, “2025-03-05 Draft: AI Competencies for Academic Library Workers,” Mar. 2025. Available: https://www.ala.org/sites/default/files/2025-03/AI_Competencies_Draft.pdf 

[7] SAGE Journals, “Exploring the utilization of generative AI by librarians in higher education across the Gulf Cooperation Council (GCC) countries: Trends in adoption, innovative applications, and emerging challenges,” Oct. 2025. Available: https://journals.sagepub.com/doi/10.1177/09610006251372630 

[8] Emerald Publishing, “Technology trends for libraries in the AI era,” 2025. Available: https://www.emerald.com/lhtn/article/42/2/6/1268684/Technology-trends-for-libraries-in-the-AI-era 

[9] Hybridhorizons, “How AI Will Transform Libraries & Librarianship 2025-2035?,” Mar. 2025. Available: https://hybridhorizons.substack.com/p/how-ai-will-transform-libraries-and 

[10] WebJunction, “What’s on the horizon for AI and public libraries?,” Oct. 2025. Available: https://www.webjunction.org/news/webjunction/public-libraries-ai-future.html

From Stacks to Algorithms: Librarianship’s New Chapter

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I remember when the word artificial intelligence first started appearing in library journals, it felt distant, almost experimental, as if it belonged more to labs than to our reading rooms. But just yesterday I came across a note from the American Library Association — they have now published guidance for school librarians on how to use AI in their everyday work [1]. No wonder, because librarians today are juggling so many roles: teachers, mentors, administrators, sometimes even technologists. The ALA’s advice is not about replacing them, but about helping them — streamlining tasks, improving communication, and yes, teaching students how to use AI ethically (plagiarism, citations, authorship, all those tricky parts).

By the way, it is not just policy notes. At Illinois, the journal Library Trends has just completed a two-part special issue on generative AI and libraries [2]. I skimmed through some of the abstracts: studies on how students use ChatGPT, how faculty perceive these tools, case studies of AI literacy instruction. This is serious scholarship, freely available, meant to guide practice. It reminds me of my early days in the profession, when such research gave us the language to argue for budgets and staff — and sometimes, just the courage to try new things.

And then, in Prague, librarians and researchers gathered under the banner of an “AI Knowledge Café,” more than 650 participants thinking together about the place of libraries in national AI strategies [3]. Imagine that: librarians not just adopting AI tools quietly, but sitting at the policy table, influencing how society will treat knowledge, ethics, and inclusion in the age of algorithms.

When I read all this, I feel both hopeful and cautious. Hopeful, because libraries are no longer seen as passive — we are active shapers of how AI unfolds. Cautious, because guidance and journals and cafés will mean little without real resources, training, and recognition, especially in countries like ours where libraries carry such a heavy heritage burden across many languages.

Still, I like to think that this is the beginning of a new chapter. Librarianship in the AI age is not a threat to our role, but a chance to re-articulate it. And in my heart, I feel grateful to be part of this transition — from catalog cards to chatbots, from dusty stacks to digital literacy.

References

[1] American Library Association. AI Guidance for School Librarians. Published September 2025. https://www.ala.org/news/2025/09/ai-guidance-school-librarians

[2] iSchool at Illinois. Library Trends Completes Two-Part Series on AI and Libraries. Published September 2025. https://ischool.illinois.edu/news-events/news/2025/09/library-trends-completes-two-part-series-ai-and-libraries

[3] ALA / IFLA. Libraries Towards a National AI Strategy (AI Knowledge Café). September 2025. https://connect.ala.org/acrl/discussion/libraries-towards-a-national-ai-strategy

Why India Needs Libraries at the Heart of Its National AI Strategy

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Artificial Intelligence (AI) is rapidly reshaping how societies learn, work, and connect. As India builds its national AI strategy, there is an urgent need to ask: who will ensure that AI development remains ethical, inclusive, and accessible to every citizen? One powerful answer lies in our libraries.

Think about it. For decades, libraries have been safe spaces where anyone, regardless of background, could walk in and learn. Whether it was a student preparing for exams, a farmer checking market information, or a job seeker updating their resume, libraries have been bridges to opportunity. In the age of AI, they can once again be the guiding hand that helps people navigate complexity and change.

  • Guardians of Ethics and Accountability
    Libraries can champion transparency, fairness, and human oversight in AI systems adopted by public institutions.
  • Protectors of Privacy and Intellectual Freedom
    Library principles of confidentiality and equitable access align perfectly with India’s need for citizen-centric AI governance.
  • AI and Digital Literacy Hubs
    Just as libraries once taught computer literacy, they can now lead community workshops, training, and resources on AI literacy.
  • Upskilling the Workforce
    Librarians must be trained to use AI in cataloguing, research support, and community services—ensuring the profession adapts and thrives.
  • Bridging the Digital Divide
    Rural and underserved communities can access AI tools through public libraries, preventing exclusion from India’s digital transformation.
  • Policy Participation
    Libraries should have a seat at the table in national AI governance—bringing the voices of ordinary citizens into policy-making.

A Call to Action for Librarians in India

Librarians must step forward to:

  • Advocate for their role in national AI consultations.
  • Develop pilot projects that showcase responsible AI use in library services.
  • Build partnerships with universities, civil society, and government bodies to amplify their impact.

A Call to Action for the Government of India

To truly build an AI for All strategy, the Government of India should:

  • Recognise libraries as strategic partners in AI education and governance.
  • Fund training and digital infrastructure for libraries.
  • Mandate representation of library associations in AI policy consultations.

Final Word

AI is like electricity—it will power every sector of life in the coming years. Libraries are the transformers that can make this power safe, reliable, and accessible to all. If India wants an inclusive AI future, it must weave libraries into its national AI strategy.

Librarians: this is your moment to lead.

Government: this is your chance to listen.

How AI Tools Revolutionize Academic Research: Top 10 Free Tools to Boost Your Workflow

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Artificial Intelligence (AI) is transforming academic research by streamlining repetitive tasks, uncovering insights, and enhancing productivity across every stage of the research process. From conducting literature reviews to analyzing data and polishing manuscripts, AI tools save time and improve efficiency. In this blog post, we explore how AI tools can elevate your research and highlight 10 free AI tools (with free plans) that support various research stages, complete with descriptions and links to get you started.

How AI Tools Enhance Academic Research

AI tools empower researchers by automating and optimizing key research tasks. Here’s how they help at different stages:

  • Literature Review: AI tools search vast academic databases, summarize papers, and identify connections between studies, making it easier to stay updated and find relevant sources.
  • Data Collection: Extract data from PDFs, texts, or online sources quickly, reducing manual effort.
  • Data Analysis: Analyze large datasets, identify patterns, and create visualizations with minimal coding.
  • Academic Writing: Improve clarity, grammar, and academic tone while generating outlines or paraphrasing content.
  • Citation Management: Automate citation formatting and reference organization across styles like APA or MLA.
  • Collaboration: Organize research materials, visualize citation networks, and share findings with teams.
  • Translation: Break language barriers by translating papers in real-time for global accessibility.

Now, let’s dive into the top 10 AI tools with free plans that can supercharge your academic research.

Top 10 Free AI Tools for Academic Research

1. Semantic Scholar

  • What It Does: A powerful AI-driven search engine for accessing over 200 million academic papers. It generates concise summaries, recommends related studies, and highlights connections between papers, perfect for literature reviews.
  • Free Plan: Completely free with unlimited searches and access to open-access papers (paywalled papers depend on your subscriptions).
  • Best For: Finding and summarizing relevant research quickly.
  • Website: semanticscholar.org

2. Elicit

  • What It Does: An AI research assistant that searches over 125 million papers, automates literature reviews, summarizes findings, and extracts data. It’s ideal for empirical research but less suited for theoretical studies.
  • Free Plan: Free access to search, summarization, and data extraction with no strict limits (verify results due to ~90% accuracy).
  • Best For: Streamlining literature reviews and data extraction.
  • Website: elicit.com

3. Research Rabbit

  • What It Does: A free tool that creates visual citation networks, suggests related papers, and organizes research collections. It’s great for exploring research connections and collaborating with peers.
  • Free Plan: Fully free with unlimited collections and paper additions (note: the interface may take some getting used to).
  • Best For: Organizing research and discovering related studies.
  • Website: researchrabbit.ai

4. Zotero

  • What It Does: A reference management tool that uses AI to suggest papers, organize citations, and generate bibliographies in various formats. It integrates seamlessly with word processors.
  • Free Plan: Free with unlimited reference storage; cloud syncing limited to 300 MB (expandable with paid plans).
  • Best For: Managing citations and references effortlessly.
  • Website: zotero.org

5. Scholarcy

  • What It Does: Summarizes research papers, articles, and book chapters into flashcards, highlighting key findings, limitations, and comparisons. It cuts screening time by up to 70%.
  • Free Plan: Summarize up to three documents per day; includes a browser extension for open-access and subscription-based papers.
  • Best For: Quickly digesting complex papers.
  • Website: scholarcy.com

6. ChatPDF

  • What It Does: Upload PDFs and interact with them via a chatbot to extract information or summarize content. It’s a time-saver for understanding dense research papers.
  • Free Plan: Upload two PDFs per day (up to 120 pages each) and ask 20 questions daily.
  • Best For: Extracting specific data from PDFs.
  • Website: chatpdf.com

7. Paperpal

  • What It Does: An AI writing assistant tailored for academia, offering grammar checks, paraphrasing, citation generation, and journal submission checks. It also supports literature searches and PDF analysis.
  • Free Plan: Basic grammar and style suggestions, 10 AI generations daily, and limited research features.
  • Best For: Polishing academic writing and translation.
  • Website: paperpal.com

8. NotebookLM

  • What It Does: A Google-powered tool that lets you upload up to 50 documents per notebook and generates summaries, audio overviews, or study guides. It’s perfect for organizing research materials.
  • Free Plan: Free with up to 100 notebooks, 50 sources per notebook, and daily limits on queries and audio summaries.
  • Best For: Summarizing and organizing research notes.
  • Website: notebooklm.google

9. AI2 Paperfinder

  • What It Does: Developed by the Allen Institute, this tool provides access to 8 million full-text papers and 108 million abstracts. It ranks search results by relevancy and exports citations in BibTeX or other formats.
  • Free Plan: Fully free with no limits on searches or citation exports.
  • Best For: Comprehensive literature searches and citation exports.
  • Website: paperfinder.allenai.org

10. DeepSeek

  • What It Does: A free large language model that answers research queries and synthesizes information. While not as advanced as premium models, it’s a solid option for general research assistance.
  • Free Plan: Fully free with no specific query limits (performance may vary for complex tasks).
  • Best For: General research queries on a budget.
  • Website: deepseek.com

Tips for Using AI Tools in Research

  • Verify Outputs: Tools like Elicit and ChatPDF may have errors (~90% accuracy for Elicit). Always cross-check results with original sources.
  • Combine Tools: Free plans have limitations (e.g., Scholarcy’s three-document cap). Use multiple tools to cover all research needs.
  • Maintain Integrity: AI should enhance, not replace, your critical thinking. Use these tools to boost productivity while ensuring originality.
  • Explore Paid Plans: If you hit free plan limits, consider paid upgrades for heavy use or advanced features.

Conclusion

AI tools are game-changers for academic research, helping you save time, uncover insights, and produce high-quality work. The 10 free tools listed above cover everything from literature reviews to citation management, making them accessible for students, researchers, and academics on a budget. Start exploring these tools today to streamline your research process and focus on what matters most—advancing knowledge.

Have a favorite AI research tool or need help with a specific research task? Share your thoughts in the comments below!

What Would S. R. Ranganathan Do in the Age of Generative AI if He Were Alive?

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S.R. Ranganathan, the pioneering Indian librarian and mathematician, is best known for his Five Laws of Library Science and the development of the Colon Classification system. His work emphasised organising knowledge for accessibility, relevance, and user-centricity. If he were alive today, his approach to generative AI would likely be shaped by his knowledge organisation principles, focus on serving users, and innovative mindset. While it’s impossible to know exactly what he would have done, we can make informed speculations based on his philosophy and contributions.

  1. Applying the Five Laws to Generative AI
    Ranganathan’s Five Laws of Library Science (1931)—”Books are for use,” “Every reader his/her book,” “Every book its reader,” “Save the time of the reader,” and “The library is a growing organism“—could be adapted to generative AI systems, which are increasingly used to organize and generate knowledge. Here’s how he might have approached generative AI:
    – Books are for use: Ranganathan would likely advocate for generative AI to be designed with practical utility in mind, ensuring it serves real-world needs, such as answering queries, generating content, or solving problems efficiently. He might push for AI interfaces that are intuitive and accessible to all users, much like a library’s catalog.
    – Every reader his/her book: He would likely emphasise personalisation in AI systems, ensuring that generative AI delivers tailored responses to diverse users. For example, he might explore how AI could adapt outputs to different languages, cultural contexts, or knowledge levels, aligning with his goal of meeting individual user needs.
    – Every book its reader: Ranganathan might focus on making AI-generated content discoverable and relevant, developing classification systems or metadata frameworks to organise AI outputs so users can easily find what they need. He could propose taxonomies for AI-generated text, images, or code to enhance retrieval.
    – Save the time of the reader: He would likely prioritise efficiency, advocating for AI systems that provide accurate, concise, and relevant outputs quickly. He might critique models that produce verbose or irrelevant responses and push for prompt engineering techniques to streamline interactions.
    – The library is a growing organism: Ranganathan would recognise generative AI as a dynamic, evolving system. He might encourage continuous updates to AI models, integrating new data and user feedback to keep them relevant, much like a library evolves with new books and technologies.
  2. Developing Classification Systems for AI Outputs
    Ranganathan’s Colon Classification system was a faceted, flexible approach to organising knowledge, allowing for complex relationships between subjects. He might apply this to generative AI by:
    – Creating a taxonomy for AI-generated content: He could develop a faceted classification system to categorize outputs like text, images, or code based on attributes such as topic, format, intent, or audience. For example, a generated article could be tagged with facets like “subject: science,” “tone: formal,” or “purpose: education.”
    – Improving information retrieval: Ranganathan might work on algorithms to enhance the discoverability of AI-generated content, ensuring users can navigate vast outputs efficiently. He could integrate his classification principles into AI search systems, making them more precise and context-aware.
    – Addressing ethical concerns: He would likely consider the ethical implications of AI-generated content, such as misinformation or bias, and propose frameworks to tag or filter outputs for reliability and fairness, aligning with his user-centric philosophy.
  3. Advancing AI for Libraries and Knowledge Management
    As a librarian, Ranganathan would likely focus on how generative AI could enhance library services and knowledge management:
    – AI-powered library assistants: He might advocate for AI chatbots to assist patrons in finding resources, answering queries, or recommending materials, saving librarians’ time and improving user experience. For example, an AI could use natural language processing to interpret complex research queries and suggest relevant books or articles.
    – Automating cataloguing: Ranganathan could explore generative AI for automating metadata creation or cataloguing, using models to summarise texts, extract keywords, or classify resources according to his Colon Classification system. This would align with his goal of saving time and improving access.
    – Preserving cultural knowledge: Given his work in India, he might use AI to digitise and generate summaries of regional texts, manuscripts, or oral traditions, making them accessible globally while preserving cultural context.
  4. Ethical and Social Considerations
    Ranganathan’s user-focused philosophy suggests he would be concerned with the ethical and societal impacts of generative AI, as noted in sources discussing AI’s risks like misinformation and job displacement. He might:
    – Promote equitable access: He would likely advocate for open-source AI models or affordable tools to ensure generative AI benefits diverse populations, not just affluent institutions or countries.
    – Address misinformation: Ranganathan might develop guidelines for libraries to educate users about AI-generated content, helping them distinguish reliable outputs from “hallucinations” or deepfakes.
    – Mitigate job displacement: While recognising AI’s potential to automate tasks, he might propose training programs for librarians to adapt to AI-driven workflows, ensuring human expertise remains central.
  5. Innovating with Generative AI
    Ranganathan was an innovator, so he might experiment with generative AI to push boundaries in knowledge organisation:
    – AI for creative knowledge synthesis: He could use AI to generate new insights by synthesising existing literature, creating summaries or interdisciplinary connections that human researchers might overlook.
    – AI in education: Drawing from his focus on accessibility, he might develop AI tools to generate educational content tailored to different learning styles, supporting students and educators.
    – Collaborative AI systems: He might propose collaborative platforms where AI and librarians work together, with AI handling data-intensive tasks and humans providing critical judgment, aligning with his belief in human-centric systems.
  6. Critiquing and Shaping AI Development
    Ranganathan’s analytical mindset suggests he would critically examine generative AI’s limitations, such as data dependence, bias, and lack of true creativity. He might:
    – Push for transparency: Advocate for clear documentation of AI training data and processes, ensuring users understand how outputs are generated.
    – Enhance AI explainability: Develop frameworks to make AI decisions more interpretable, helping users trust and verify generated content.
    – Focus on sustainability: Given the environmental impact of AI training, he might explore energy-efficient models or advocate for sustainable practices in AI development.

Conclusion
If S.R. Ranganathan were alive today, he would likely embrace generative AI as a tool to enhance knowledge organisation and accessibility while critically addressing its ethical and practical challenges. He would adapt his Five Laws to AI, develop classification systems for AI outputs, and leverage AI to improve library services and education. His focus would remain on serving users, ensuring equity, and advancing knowledge management in an AI-driven world. His innovative spirit and user-centric philosophy would make him a key figure in shaping generative AI’s role in libraries and beyond.