Nov 13, 2025 |
14 Mins Read
Zendy and Oxford University Press Sign Agreement to Broaden Access to Academic Research
Oxford, UK – Sep, 2025 – Zendy, an AI-powered research library, and Oxford University Press (OUP), a department of the University of Oxford and one of the world’s largest university presses, have signed a licensing agreement to increase the accessibility and visibility of OUP’s academic publications. This collaboration will bring a selection of OUP’s highly regarded research and scholarly journals to agreed territories within Zendy’s user base, supporting students, researchers, and professionals across diverse fields. With over 700,000 users in 191 countries and territories, Zendy continues to grow as a trusted destination for research and discovery. By integrating a subset of OUP journals into its platform, Zendy is advancing its mission to ensure that high-quality scholarly resources are available to the people who need access the most. Oxford University Press has been publishing academic and educational resources for more than 500 years, making it one of the most established and respected publishers worldwide. Its catalogue includes thousands of journals, books, and digital resources spanning disciplines such as humanities, social sciences, medicine, law, science, and technology. These resources are central to advancing knowledge, shaping academic dialogue, and supporting evidence-based research. OUP’s owned-journals will now be available on Zendy, complementing the platform’s growing collection of journals, articles, and reports. This agreement will support researchers, educators, and policymakers by improving the discoverability of essential academic content, furthering the shared goal of building inclusive knowledge societies. For more information, please contact: Lisette van Kessel Head of Marketing Email: l.vankessel@knowledgee.com .wp-block-image img { max-width: 75% !important; margin-left: auto !important; margin-right: auto !important; }
Zendy to Showcase AI-Powered Library Innovations at the Charleston Conference 2025
We’re thrilled to announce that Zendy will be taking the stage at this year’s Charleston Conference, one of the most anticipated gatherings for librarians, publishers, and information professionals worldwide. Join us on November 4 at 11:30 AM in Salon 2, Gaillard Centre, for our live demo session titled: “Transforming Your Library Services with Zendy AI Tools.” In this interactive session, Mike Perrine (VP of Sales and Marketing, WT Cox) and Kamran Kardan (Co-Founder, Zendy) will demonstrate how Zendy’s innovative AI-driven tools are revolutionising the way libraries manage content, empower discovery, and enhance user engagement. Zendy helps solve one of the biggest challenges libraries face today, providing users with faster, smarter access to research insights. Our platform enables instant article summarisation, concept extraction, and trusted AI-powered answers through our intelligent assistant, ZAIA. With Zendy, libraries can streamline their services and give researchers a more intuitive, efficient way to interact with scholarly information. We’re also proud to share that Zendy has been selected for the prestigious Charleston Premiers, a showcase recognising the most innovative and forward-thinking products reshaping scholarly communication. Representing Zendy at the Premiers will be Kamran Kardan (Co-Founder) and Lisette van Kessel (Head of Marketing), who will present how Zendy’s mission to make knowledge accessible and affordable continues to evolve through technology and partnership. The Charleston Conference has long been a hub for meaningful dialogue and collaboration in the world of academic information services, and we’re excited to be part of shaping its future. Event Details: Session: Transforming Your Library Services with Zendy AI Tools Date: November 4, 2025 Time: 11:30 AM Location: Salon 2, Gaillard Centre We look forward to connecting with fellow innovators, librarians, and partners, and showcasing how Zendy AI is redefining what’s possible for libraries and researchers alike. Don’t miss it, see how Zendy is shaping the future of knowledge discovery. To register and learn more about the Charleston Conference, please visit: https://www.charleston-hub.com/the-charleston-conference/about-the-conference/ .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
From Boolean to Intelligent Search: A Librarian’s Guide to Smarter Information Retrieval
As a librarian, you’ve always been the person people turn to when they need help finding answers. But the way we search for information is changing fast. Databases are growing, new tools keep appearing, and students expect instant results. Only then will you know the true benefit of AI for libraries, to help you make sense of it all. From Boolean to Intelligent Search Traditional search is still part of everyday library work. It depends on logic and structure, keywords, operators, and carefully built queries. But AI adds something new. It doesn’t just look for words; it tries to understand what someone means. If a researcher searches for “climate change effects on migration,” an AI-powered tool doesn’t just pull results with those exact words. It also looks for studies about environmental displacement, regional challenges, and social impacts. This means you can spend less time teaching people how to “speak database” and more time helping them understand the research they find. The Evolution of Library Search Traditional search engines focus on matching keywords, which often leads to long lists of results. With AI, search tools can now read queries in natural language, just the way people ask questions, and still find accurate, relevant material. Natural language processing (NLP) and machine learning (ML) make it possible for search systems to connect related ideas, even when the exact words aren’t used. Features like semantic search and vector databases help AI recognise patterns and suggest other useful directions for exploration. Examples of AI Tools Librarians Can Use Tool / PlatformWhat It DoesWhy It Helps LibrariansZendyA platform that combines literature discovery, AI summaries, keyphrase highlighting, and PDF analysisHelps librarians and researchers access, read, and understand academic papers more easilyConsensusAn AI-powered academic search engine that summarises findings from peer-reviewed studiesHelps with literature reviews and citation managementEx Libris PrimoUses AI to support discovery and manage metadataImproves record accuracy and helps users find what they need fasterMeilisearchA fast, flexible search engine that uses NLPMakes it easier to search large databases efficiently The Ethics of Intelligent Search Algorithms influence what users see and what they might miss. That’s why your role is so important. You can help users question why certain results appear on top, encourage critical thinking, and remind them that algorithms are not neutral. Digital literacy today isn’t just about knowing how to search, it’s about understanding how the search works. In Conclusion AI tools for librarians are becoming easier to use and more helpful every day. Some platforms now include features like summarisation, citation analysis, and even plans to highlight retracted papers, something Zendy is working toward. Trying out these tools can make your work smoother: faster reference responses, smarter cataloguing, and better guidance for researchers who often feel lost in the flood of information. AI isn’t replacing your expertise, it’s helping you use it in new ways. And that’s what makes this moment exciting for librarians everywhere. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Why AI like ChatGPT still quotes retracted papers?
AI models like ChatGPT are trained on massive datasets collected at specific moments in time, which means they lack awareness of papers retracted after their training cutoff. When a scientific paper gets retracted, whether due to errors, fraud, or ethical violations, most AI systems continue referencing it as if nothing happened. This creates a troubling scenario where researchers using AI assistants might unknowingly build their work on discredited foundations. In other words: retracted papers are the academic world's way of saying "we got this wrong, please disregard." Yet the AI tools designed to help us navigate research faster often can't tell the difference between solid science and work that's been officially debunked. ChatGPT and other assistants tested Recent studies examined how popular AI research tools handle retracted papers, and the results were concerning. Researchers tested ChatGPT, Google's Gemini, and similar language models by asking them about known retracted papers. In many cases, they not only failed to flag the retractions but actively praised the withdrawn studies. One investigation found that ChatGPT referenced retracted cancer imaging research without any warning to users, presenting the flawed findings as credible. The problem extends beyond chatbots to AI-powered literature review tools that researchers increasingly rely on for efficiency. Common failure scenarios The risks show up across different domains, each with its own consequences: Medical guidance: Healthcare professionals consulting AI for clinical information might receive recommendations based on studies withdrawn for data fabrication or patient safety concerns Literature reviews: Academic researchers face citation issues when AI assistants suggest retracted papers, damaging credibility and delaying peer review Policy decisions: Institutional leaders making evidence-based choices might rely on AI-summarised research without realising the underlying studies have been retracted A doctor asking about treatment protocols could unknowingly follow advice rooted in discredited research. Meanwhile, detecting retracted citations manually across hundreds of references proves nearly impossible for most researchers. How Often Retractions Slip Into AI Training Data The scale of retracted papers entering AI systems is larger than most people realise. Crossref, the scholarly metadata registry that tracks digital object identifiers (DOIs) for academic publications, reports thousands of retraction notices annually. Yet many AI models were trained on datasets harvested years ago, capturing papers before retraction notices appeared. Here's where timing becomes critical. A paper published in 2020 and included in an AI training dataset that same year might get retracted in 2023. If the model hasn't been retrained with updated data, it remains oblivious to the retraction. Some popular language models go years between major training updates, meaning their knowledge of the research landscape grows increasingly outdated. Lag between retraction and model update Training Large Language Models requires enormous computational resources and time, which explains why most AI companies don't continuously update their systems. Even when retraining occurs, the process of identifying and removing retracted papers from massive datasets presents technical challenges that many organisations haven't prioritised solving. The result is a growing gap between the current state of scientific knowledge and what AI assistants "know." You might think AI systems could simply check retraction databases in real-time before responding, but most don't. Instead, they generate responses based solely on their static training data, unaware that some information has been invalidated. Risks of Citing Retracted Papers in Practice The consequences of AI-recommended retracted papers extend beyond embarrassment. When flawed research influences decisions, the ripple effects can be substantial and long-lasting. Clinical decision errors Healthcare providers increasingly turn to AI tools for quick access to medical literature, especially when facing unfamiliar conditions or emerging treatments. If an AI assistant recommends a retracted study on drug efficacy or surgical techniques, clinicians might implement approaches that have been proven harmful or ineffective. The 2020 hydroxychloroquine controversy illustrated how quickly questionable research spreads. Imagine that dynamic accelerated by AI systems that can't distinguish between valid and retracted papers. Policy and funding implications Government agencies and research institutions often use AI tools to synthesise large bodies of literature when making funding decisions or setting research priorities. Basing these high-stakes choices on retracted work wastes resources and potentially misdirects entire fields of inquiry. A withdrawn climate study or economic analysis could influence policy for years before anyone discovers the AI-assisted review included discredited research. Academic reputation damage For individual researchers, citing retracted papers carries professional consequences. Journals may reject manuscripts, tenure committees question research rigour, and collaborators lose confidence. While honest mistakes happen, the frequency of such errors increases when researchers rely on AI tools that lack retraction awareness, and the responsibility still falls on the researcher, not the AI. Why Language Models Miss Retraction Signals The technical architecture of most AI research assistants makes them inherently vulnerable to the retraction problem. Understanding why helps explain what solutions might actually work. Corpus quality controls lacking AI models learn from their training corpus, the massive collection of text they analyse during development. Most organisations building these models prioritise breadth over curation, scraping academic databases, preprint servers, and publisher websites without rigorous quality checks. The assumption is that more data produces better models, but this approach treats all papers equally regardless of retraction status. Even when training data includes retraction notices, the AI might not recognise them as signals to discount the paper's content. A retraction notice is just another piece of text unless the model has been specifically trained to understand its significance. Sparse or inconsistent metadata Publishers handle retractions differently, creating inconsistencies that confuse automated systems: Some journals add "RETRACTED" to article titles Others publish separate retraction notices A few quietly remove papers entirely This lack of standardisation means AI systems trained to recognise one retraction format might miss others completely. Metadata، the structured information describing each paper, often fails to consistently flag retraction status across databases. A paper retracted in PubMed might still appear without warning in other indexes that AI training pipelines access. Hallucination and overconfidence AI hallucination occurs when models generate plausible-sounding but false information, and it exacerbates the retraction problem. Even if a model has no information about a topic, it might confidently fabricate citations or misremember details from its training data. This overconfidence means AI assistants rarely express uncertainty about the papers they recommend, leaving users with no indication that additional verification is needed. Real-Time Retraction Data Sources Researchers Should Trust While AI tools struggle with retractions, several authoritative databases exist for manual verification. Researchers concerned about citation integrity can cross-reference their sources against these resources. Retraction Watch Database Retraction Watch operates as an independent watchdog, tracking retractions across all academic disciplines and publishers. Their freely accessible database includes detailed explanations of why papers were withdrawn, from honest error to fraud. The organisation's blog also provides context about patterns in retractions and systemic issues in scholarly publishing. Crossref metadata service Crossref maintains the infrastructure that assigns DOIs to scholarly works, and publishers report retractions through this system. While coverage depends on publishers properly flagging retractions, Crossref offers a comprehensive view across multiple disciplines and publication types. Their API allows developers to build tools that automatically check retraction status, a capability that forward-thinking platforms are beginning to implement. PubMed retracted publication tag For medical and life sciences research, PubMed provides reliable retraction flagging with daily updates. The National Library of Medicine maintains this database with rigorous quality control, ensuring retracted papers receive prominent warning labels. However, this coverage is limited to biomedical literature, leaving researchers in other fields without equivalent resources. DatabaseCoverageUpdate SpeedAccessRetraction WatchAll disciplinesReal-timeFreeCrossrefPublisher-reportedVariableFree APIPubMedMedical/life sciencesDailyFree Responsible AI Starts with Licensing When AI systems access research papers, articles, or datasets, authors and publishers have legal and ethical rights that need protection. Ignoring these rights can undermine the sustainability of the research ecosystem and diminish trust between researchers and technology providers. One of the biggest reasons AI tools get it wrong is that they often cite retracted papers as if they’re still valid. When an article is retracted, e.g. due to peer review process not being conducted properly or failing to meet established standards, most AI systems don’t know, it simply remains part of their training data. This is where licensing plays a crucial role. Licensed data ensures that AI systems are connected to the right sources, continuously updated with accurate, publisher-verified information. It’s the foundation for what platforms like Zendy aim to achieve: making sure the content is clean and trustworthy. Licensing ensures that content is used responsibly. Proper agreements between AI companies and copyright holders allow AI systems to access material legally while providing attribution and, when appropriate, compensation. This is especially important when AI tools generate insights or summaries that are distributed at scale, potentially creating value for commercial platforms without benefiting the sources of the content. in conclusion, consent-driven licensing helps build trust. Publishers and authors can choose whether and how their work is incorporated into AI systems, ensuring that content is included only when rights are respected. Advanced AI platforms, such as Zendy, can even track which licensed sources contributed to a particular output, providing accountability and a foundation for equitable revenue sharing. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Zendy Sponsors the 4th Annual Forum for Open Research in MENA
This October, American University of Sharjah will host the 4th Annual Forum for Open Research in MENA (FORM), a leading regional event dedicated to advancing open access, collaboration, and community capacity building across the Arab world. Zendy is proud to be among the official sponsors of this year’s Forum, reinforcing our ongoing commitment to making research more accessible and equitable for all. Organised by the non-profit Forum for Open Research in MENA, the fourth edition will bring together: 26 sessions across four days 88 expert speakers Representation from 60 global institutions Delegates from 27 countries Dozens of formal and informal networking opportunities, connecting thought leaders, practitioners, and advocates throughout the MENA region Participants will explore strategies to strengthen open science practices, build sustainable infrastructures, and promote shared learning across borders. A Shared Vision for Accessible Knowledge Zendy’s mission to make academic knowledge affordable and accessible worldwide aligns closely with FORM’s goal of fostering open, collaborative research communities in the region. As a sponsor, Zendy supports initiatives that not only expand access to scholarly literature but also empower researchers, educators, and students to participate in a more transparent and connected research ecosystem. Hosted by the American University of Sharjah This year’s Forum, hosted by the American University of Sharjah, arrives in the UAE under the national theme, “The Year of Community.” The theme underscores the importance of collective progress and shared learning—values that sit at the heart of both open science and Zendy’s approach to research discovery. Theme Spotlight: “Becoming Open—Capacity Building and Community Collaboration” The 2025 theme, “Becoming Open,” highlights the human side of open science: community collaboration, capacity building, and sustainable growth. Through workshops, panels, and discussions, the Annual Forum will address how regional institutions can implement open research policies, share resources effectively, and strengthen local research infrastructures. Join us in Sharjah from October 20–23, 2025, as we celebrate and support the growing movement toward open science across the Arab world. The registration is now open, visit https://forumforopenresearch.com/registration/ .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
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