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Research integrity extends beyond publication to include how scholarship is discovered, accessed, and used, and its societal impact depends on more than editorial practice alone. In practice, integrity and impact are shaped by a web of platforms and partnerships that determine how research actually travels beyond the press.

University press scholarship is generally produced with a clear public purpose, speaking to issues such as education, public health, social policy, culture, and environmental change, and often with the explicit aim of informing practice, policy, and public debate. 

Whether that aim is realised increasingly depends on what happens to research once it leaves the publishing workflow. Discovery platforms, aggregators, library consortia, and technology providers all influence this journey. Choices about metadata, licensing terms, ranking criteria, or the use of AI-driven summarisation affect which research is surfaced, how it is presented, and who encounters it in the first place. 

These choices can look technical or commercial on the surface, but they have real intellectual and social consequences. They shape how scholarship is understood and whether it can be trusted beyond core academic audiences. For university presses, this changes where responsibility sits. Editorial quality remains critical, but it is no longer the only consideration. Presses also have a stake in how their content is discovered, contextualised, and applied in wider knowledge ecosystems. Long-form and specialist research is particularly exposed here. When material is compressed or broken apart for speed and scale, nuance can easily be lost, even when the intentions behind the system are positive.

This is where partnerships start to matter in a very practical way. The conditions under which presses work with discovery services directly affect whether their scholarship remains identifiable, properly attributed, and anchored in its original context. For readers using research in teaching, healthcare, policy, or development settings, these signals are not decorative. They are essential to responsible use.

Zendy offers one example of how these partnerships can function differently. As a discovery and access platform serving researchers, clinicians, and policymakers in emerging and underserved markets, Zendy is built around extending reach without undermining trust. University press content is surfaced with clear attribution, structured metadata, and rights-respecting access models that preserve the integrity of the scholarly record.

Zendy works directly with publishers to agree how content is indexed, discovered, and, where appropriate, summarised. This gives presses visibility into and control over how their work appears in AI-supported discovery environments, while helping readers approach research with a clearer sense of scope, limitations, and authority.

From a societal impact perspective, this matters. Zendy’s strongest usage is concentrated in regions where access to trusted scholarship has long been uneven, including parts of Africa, the Middle East, and Asia. In these contexts, university press research is not being read simply for academic interest. It is used in classrooms, clinical settings, policy development, and capacity-building efforts, areas closely connected to the Sustainable Development Goals.

Governance really sits at the heart of this kind of model. Clear and shared expectations around metadata quality, content provenance, licensing boundaries, and the use of AI are what make the difference between systems that encourage genuine engagement and those that simply amplify visibility without depth. Metadata is not just a technical layer: it gives readers the cues they need to understand what they are reading, where it comes from, and how it should be interpreted.

AI-driven discovery and new access models create real opportunities to broaden the reach of university press publishing and to connect trusted scholarship with communities that would otherwise struggle to access it. But reach on its own does not equate to impact. When context and attribution are lost, the value of the research is diminished. Societal impact depends on whether work is understood and used with care, not simply on how widely it circulates.

For presses with a public-interest mission, active participation in partnerships like these is a way to carry their values into a more complex and fast-moving environment. As scholarship is increasingly routed through global, AI-powered discovery systems, questions of integrity, access, and societal relevance converge. Making progress on shared global challenges requires collaboration, shared responsibility, and deliberate choices about the infrastructures that connect research to the wider world. For university presses, this is not a departure from their mission, but a continuation of it, with partnerships playing an essential role.

FAQ

How do platforms and partnerships affect research integrity?
Discovery platforms, aggregators, and technology partners influence which research is surfaced, how it’s presented, and who can access it. Choices around metadata, licensing, and AI summarization directly impact understanding and trust.

Why are university press partnerships important?
Partnerships allow presses to maintain attribution, context, and control over their content in discovery systems, ensuring that research remains trustworthy and properly interpreted.

How does Zendy support presses and researchers?
Zendy works with publishers to surface research with clear attribution, structured metadata, and rights-respecting access, preserving integrity while extending reach to underserved regions.

For partnership inquiries, please contact:
 Sara Crowley Vigneau
Partnership Relations Manager
Email: s.crowleyvigneau@zendy.io

University presses play a distinctive role in advancing research integrity and societal impact. Their publishing programmes are closely aligned with public-interest research in the humanities, social sciences, global health, education, and environmental studies, disciplines that directly inform policy and progress toward the UN Sustainable Development Goals. This work typically prioritises depth, context, and long-term understanding, often drawing on regional expertise and interdisciplinary approaches rather than metrics-driven outputs.

Research integrity is traditionally discussed in terms of editorial rigour, peer review, and ethical standards in the production of scholarship. These remain essential. But in an era shaped by digital platforms and AI-led discovery, they are no longer sufficient on their own. Integrity now also depends on what happens after publication: how research is surfaced, interpreted, reduced, and reused.

For university presses, this shift is particularly significant. Long-form scholarship, a core strength of press programmes, is increasingly encountered through abstracts, summaries, extracts, and automated recommendations rather than sustained reading. As AI tools mediate more first encounters with research, meaning can be subtly altered through selection, compression, or loss of context. These processes are rarely neutral. They encode assumptions about relevance, authority, and value.

This raises new integrity questions. Who decides which parts of a work are highlighted or omitted? How are disciplinary nuance and authorial intent preserved when scholarship is summarised? What signals remain to help readers understand scope, limitations, or evidentiary weight?

This isn’t to say that AI-driven discovery is inherently harmful, but it does require careful oversight. If university press scholarship is to continue informing research, policy, and public debate in meaningful ways, it needs to remain identifiable, properly attributed, and grounded in its original framing as it moves through increasingly automated discovery systems.

In this context, research integrity extends beyond how scholarship is produced to include how it is processed, surfaced and understood. For presses with a public-interest mission, research integrity now extends across the full journey of a work, from how it is published to how it is discovered, interpreted and used.

FAQ

Can Zendy help with AI-mediated research discovery?
Yes. Zendy’s tools help surface, summarise, and interpret research accurately, preserving context and authorial intent even when AI recommendations are used.

Does AI discovery harm research, or can it be beneficial?
AI discovery isn’t inherently harmful—it can increase visibility and accessibility. However, responsible use is essential to prevent misinterpretation or loss of nuance, ensuring research continues to inform policy and public debate accurately.

How does Zendy make research more accessible?
Researchers can explore work from multiple disciplines, including humanities, social sciences, global health, and environmental studies, all in one platform with easy search and AI-powered insights.

For partnership inquiries, please contact:
 Sara Crowley Vigneau
Partnership Relations Manager
Email: s.crowleyvigneau@zendy.io

With the growing integration of digital technologies in academia, librarians are becoming facilitators of discovery. They play a vital role in helping students and researchers find credible information, use digital tools effectively, and develop essential research skills. At Zendy, we believe this shift represents a new chapter for librarians, one where they act as mentors, digital strategists, and AI collaborators.

Zendy’s AI-powered research assistant, ZAIA, is one example of how librarians can enhance their work using technology. Librarians can utilise ZAIA to assist users in clarifying research questions, discovering relevant papers more efficiently, and understanding complex academic concepts in simpler terms. This partnership between human expertise and AI efficiency allows librarians to focus more on supporting critical thinking, rather than manual searching.

According to our latest survey, AI in Education for Students and Researchers: 2025 Trends and Statistics, over 70% of students now rely on AI for research. Librarians are adapting to this shift by integrating these technologies into their services, offering guidance on ethical AI use, research accuracy, and digital literacy.

However, this evolution also comes with challenges. Librarians must ensure users understand how to evaluate AI-generated content, check for biases, and verify sources. The focus is moving beyond access to information, it’s now about ensuring that information is used responsibly and critically.

To support this changing role, here are some tools and practices modern librarians can integrate into their workflows:

1. AI-Enhanced Discovery
   Using tools like ZAIA to help researchers refine queries and find relevant studies faster.
2. Research Data Management
   Organising, preserving, and curating datasets for long-term academic use.
3. Ethical AI and Digital Literacy Training
   Teaching researchers how to verify AI outputs, evaluate bias, and maintain academic integrity.
4. Collaborative Digital Spaces
   Facilitating research communication through online repositories and discussion platforms.

In conclusion, librarians today are more than curators, they are digital navigators shaping how knowledge is accessed, evaluated, and shared. As technology continues to evolve, so will its role in guiding researchers and students through the expanding world of digital information.

In 2026, librarians who understand how AI works will be better equipped to support students and researchers, organise collections, and help patrons find reliable information faster. Developing a few key AI skills can make everyday tasks easier and open up new ways to serve your community.

Why AI Skills Matter for Librarians

AI tools that recommend books, manage citations, or answer basic questions are becoming more common.

Learning how these tools work helps librarians:

• Offer smarter, faster search results.
• Improve cataloguing accuracy.
• Provide better guidance to researchers and students.

Remember, AI isn’t replacing professional judgment; it’s supporting it.

Core AI Literacy Foundations

Before diving into specific tools, it helps to understand some basic ideas behind AI.

Machine Learning Basics:
Machine learning means teaching a computer to recognise patterns in data. In a library setting, this could mean analysing borrowing habits to suggest new titles or resources.

Natural Language Processing (NLP):
NLP is what allows a chatbot or search tool to understand and respond to human language. It’s how virtual assistants can answer questions like “What are some journals about public health policy?”

Quick Terms to Know:

• Algorithm: A set of steps an AI follows to make a decision.
• Training Data: The information used to “teach” an AI system.
• Neural Network: A type of computer model inspired by how the brain processes information.
• Bias: When data or systems produce unfair or unbalanced results.

Metadata Enrichment With AI

Cataloguing is one of the areas where AI makes a noticeable difference.

• Automated Tagging: AI tools can read through titles and abstracts to suggest keywords or subject headings.
• Knowledge Graphs: These connect related materials, for example, linking a book on climate change with recent journal articles on the same topic.
• Bias Checking: Some systems can flag outdated or biased terminology in subject classifications.

Generative Prompt Skills

Knowing how to “talk” to AI tools is a skill in itself. The clearer your request, the better the result. Try experimenting with prompts like these:

• Research Prompt: “List three recent studies on community reading programs and summarise their findings.”
• Teaching Prompt: “Write a short activity plan for a workshop on evaluating online information sources.”
• Summary Prompt: “Give me a brief overview of this article’s key arguments and methods.”

Adjusting tone or adding detail can change the outcome. It’s about learning how to guide the tool rather than letting it guess.

Ethical Data Practices

AI tools can be useful, but they also raise questions about privacy and fairness. Librarians have always cared deeply about protecting patron information, and that remains true with AI.

• Keep personal data anonymous wherever possible.
• Review AI outputs for signs of bias or misinformation.
• Encourage clear policies around how data is stored and used.

Ethical AI is part of a librarian’s duty to maintain trust and fairness.

Automating Everyday Tasks

AI can take over some of the small, routine jobs that fill up a librarian’s day.

• Circulation: Systems can send overdue reminders automatically or manage renewals.
• Chatbots: Basic questions like “What are the library hours?” can be handled instantly.
• Collection Management: AI can spot patterns in borrowing data to suggest which books to keep, reorder, or retire.

Building Your Learning Path

Getting comfortable with AI doesn’t have to mean earning a new degree. Start small:

• Take short online courses or micro-certifications in AI literacy.
• Join librarian groups or online forums where people share practical tips.
• Block out one hour a week to try out a new tool or attend a webinar.

A little consistent learning goes a long way.

Making AI Affordable

Many smaller libraries worry about cost, but not every tool is expensive.

• Free Tools: Some open-access AI platforms, like Zendy, offer affordable access to research databases and AI-powered features.
• Shared Purchases: Partnering with other libraries to share licenses can cut costs.
• Cloud Services: Pay-as-you-go plans mean you only pay for what you actually use.

There’s usually a way to experiment with AI without stretching the budget.

Showing Impact

Once AI tools are in use, it’s important to show their value. Track things like:

• Time saved on cataloguing or circulation tasks.
• Patron feedback on new services.
• How often are AI tools used compared to manual systems?

Numbers matter, but so do stories. Sharing examples, like a student who found research faster thanks to a new search feature, can make your case even stronger.

And remember, the future of librarianship is about using AI tools in libraries thoughtfully to keep libraries relevant, reliable, and welcoming spaces for everyone.

The integration of Generative AI into academic life appears to be a significant moment for university libraries. As trusted guides in the information ecosystem, librarians are positioned to help researchers explore this new terrain, but this transition requires developing a fresh set of skills.

Training your library team on AI-powered research tools could move beyond technical instruction to focus on critical thinking, ethical understanding, and human judgment.

Here is a proposed framework for a training program, organised by the new competencies your team might need to explore.

Foundational: Understanding Access and Use

This initial module establishes a baseline understanding of the technology itself.

• Accessing the Platform: Teach the technical steps for using the institution’s approved AI tools, including authentication, subscription models, and any specific interfaces (e.g., vendor-integrated AI features in academic databases, institutional LLMs, etc.).
• Core Mechanics: Explain what a Generative AI platform (like a Large Language Model) is and, crucially, what it is not. Cover foundational concepts like:
  • Training Data: Familiarise staff with how to access the institution’s chosen AI tools, noting any specific authentication requirements or limitations tied to vendor-integrated AI features in academic databases.
  • Prompting Basics: Introduce basic prompt engineering, the art of crafting effective, clear queries to get useful outputs.
  • Hallucinations: Directly address the concept of “hallucinations,” or factually incorrect/fabricated outputs and citations, and emphasise the need for human verification.

Conceptual: Critical Evaluation and Information Management

This module focuses on the librarian’s core competency: evaluating information in a new context.

• Locating and Organising: Train staff on how to use AI tools for practical, time-saving tasks, such as:
  • Generating keywords for better traditional database searches.
  • Summarising long articles to quickly grasp the core argument.
  • Identifying common themes across a set of resources.
• Evaluating Information: This is perhaps the most critical skill. Teach a new layer of critical information literacy:
  • Source Verification: Always cross-check AI-generated citations, summaries, and facts against reliable, academic sources (library databases, peer-reviewed journals).
  • Bias Identification: Examine AI outputs for subtle biases, especially those related to algorithmic bias in the training data, and discuss how to mitigate this when consulting with researchers.
• Using and Repurposing: Demonstrate how AI-generated material should be treated—as a raw output that must be heavily edited, critiqued, and cited, not as a final product.

Social: Communicating with AI as an Interlocutor

The quality of AI output is often dependent on the user’s conversational ability. This module suggests treating the AI platform as a possible partner in a dialogue.

• Advanced Prompt Engineering: Move beyond basic queries to teach techniques for generating nuanced, high-quality results:
  • Assigning the AI a role (such as a ‘sceptical editor’ or ‘historical analyst’) to potentially shape a more nuanced response.
  • Practising iterative conversation, where librarians refine an output by providing feedback and further instructions, treating the interaction as an ongoing intellectual exchange.
• Shared Understanding: Practise using the platform to help users frame their research questions more effectively. Librarians can guide researchers in using the AI to clarify a vague topic or map out a conceptual framework, turning the tool into a catalyst for deeper thought rather than a final answer generator.

Socio-Emotional Awareness: Recognising Impact and Building Confidence

This module addresses the human factor, building resilience and confidence

• Recognising the Impact of Emotions: Acknowledge the possibility of emotional responses, such as uncertainty about shifting professional roles or discomfort with rapid technological change, and facilitate a safe space for dialogue.
• Knowing Strengths and Weaknesses: Reinforce the unique, human-centric value of the librarian: critical thinking, contextualising information, ethical judgment, and deep disciplinary knowledge, skills that AI cannot replicate. The AI could be seen as a means to automate lower-level tasks, allowing librarians to focus on high-value consultation.
• Developing Confidence: Implement hands-on, low-stakes practice sessions using real-world research scenarios. Confidence grows from successful interaction, not just theoretical knowledge. Encourage experimentation and a “fail-forward” mentality.

Ethical: Acting Ethically as a Digital Citizen

Ethical use is the cornerstone of responsible AI adoption in academia. Librarians must be the primary educators on responsible usage.

• Transparency and Disclosure: Discuss the importance of transparency when utilizing AI. Review institutional and journal guidelines that may require students and faculty to disclose how and when AI was used in their work, and offer guidance on how to properly cite these tools.
• Data Privacy and Security: Review the potential risks associated with uploading unpublished, proprietary, or personally identifiable information (PII) to public AI services. Establish and enforce clear library policies on what data should never be shared with external tools.
• Copyright and Intellectual Property (IP): Discuss the murky legal landscape of AI-generated content and IP. Emphasise that AI models are often trained on copyrighted material and that users are responsible for ensuring their outputs do not infringe on existing copyrights. Advocate for using library-licensed, trusted-source AI tools whenever possible.
• Combating Misinformation: Position the librarian as the essential arbiter against the spread of AI-generated misinformation. Training should include spotting common AI red flags, teaching users how to think sceptically, and promoting the library’s curated, authoritative resources as the gold standard.