How AI in Higher Education Is Helping Libraries Support Research
Oct 1, 2025 |
5 Mins Read
Libraries have always been at the centre of knowledge in higher education. Beyond curating collections, librarians guide researchers and students through complex databases, teach research skills, and help faculty navigate publishing requirements. They also play a key role in managing institutional resources, preserving archives, and ensuring equitable access to information. These days, libraries are facing new challenges: huge amounts of digital content, tighter budgets, and more demand for remote access. In this environment, AI in higher education is starting to make a real difference.
How AI Makes Life Easier for Librarians
Improving Discovery
AI-powered search tools don’t just look for keywords, they can understand the context of a query. That means students and researchers can find related work they might otherwise miss. It’s like having an extra set of eyes to point them toward useful sources.
Helping with Curation
AI can go through thousands of articles and highlight the ones most relevant to a specific course, project, or research topic. For example, a librarian preparing a reading list for a history class can save hours by letting AI suggest the most relevant papers or reports.
Supporting Remote Access
Students, researchers and faculty aren’t always on campus. AI can summarise long articles, translate content, or adjust resources for different reading levels. This makes it easier for people to get the information they need, even from home.
Working Within Budgets
Subscriptions remain a major expense for libraries, and ongoing budget cuts are forcing many academic institutions to make difficult choices about which resources to keep or cancel. For example, recent surveys show that around 73% of UK higher education libraries are making budget cuts this year, sometimes slashing up to 30% of their overall budgets, and collectively spending £51 million less than the previous year. This trend is not limited to the UK, universities in the U.S. and elsewhere are also reducing library funding, which has dropped by nearly 20% per student over recent years. Even top institutions like Princeton have cut library hours and student staffing to save on costs.
Subscriptions can be expensive, and libraries often have to make tough choices. AI tools that work across large collections help libraries give students and researchers more access without adding extra subscriptions.
Trusted Content Still Matters
AI is helpful, but the resources behind it are just as important. Librarians care about trusted, peer-reviewed, and varied sources.
Librarians and AI: A Partnership
AI isn’t replacing librarians. Instead, it supports the work they already do. Librarians are the ones who guide researchers, check the quality of sources, and teach information skills. By using AI tools, librarians can make research easier for students, researchers and faculty, and they can help their institutions make the most of the resources they have.
Final Thoughts
AI in higher education is making it easier for libraries to support students and faculty, but librarians are still at the centre of the process. By using AI tools alongside strong content collections, libraries can save time, offer more resources, and help researchers find exactly what they need. With the right AI support, research becomes easier to navigate and more accessible without overcomplicating the process.
5 Tools Every Librarian Should Know in 2025
The role of librarians has always been about connecting people with knowledge. But in 2025, with so much information floating around online, the challenge isn’t access, it’s sorting through the noise and finding what really matters. This is where AI for libraries is starting to make a difference. Here are five that are worth keeping in your back pocket this year. 1. Zendy Zendy is a one-stop AI-powered research library that blends open access with subscription-based resources. Instead of juggling multiple platforms, librarians can point students and researchers to one place where they’ll find academic articles, reports, and AI tools to help with research discovery and literature review. With its growing use of AI for libraries, Zendy makes it easier to summarise research, highlight key ideas, and support literature reviews without adding to the librarian’s workload. 2. LibGuides Still one of the most practical tools for librarians, LibGuides makes it easy to create tailored resource guides for courses, programs, or specific assignments. Whether you’re curating resources for first-year students or putting together a subject guide for advanced research, it helps librarians stay organised while keeping information accessible to learners. 3. OpenRefine Cleaning up messy data is nobody’s favourite job, but it’s a reality when working with bibliographic records or digital archives. OpenRefine is like a spreadsheet, but with superpowers, it can quickly detect duplicates, fix formatting issues, and make large datasets more manageable. For librarians working in cataloguing or digital collections, it saves hours of tedious work. 4. PressReader Library patrons aren’t just looking for academic content; they often want newspapers, magazines, and general reading material too. PressReader gives libraries a simple way to provide access to thousands of publications from around the world. It’s especially valuable in public libraries or institutions with international communities. 5. OCLC WorldShare Managing collections and sharing resources across institutions is a constant task. OCLC WorldShare helps libraries handle cataloguing, interlibrary loans, and metadata management. It’s not flashy, but it makes collaboration between libraries smoother and ensures that resources don’t sit unused when another community could benefit from them. Final thought The tools above aren’t just about technology, they’re about making everyday library work more practical. Whether it’s curating resources with Zendy, cleaning data with OpenRefine, or sharing collections through WorldShare, these platforms help librarians do what they do best: guide people toward knowledge that matters. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Balancing AI Efficiency with Human Expertise in Libraries
AI in libraries is making some tasks quicker and less repetitive. However, even with these advances, there’s something irreplaceable about a librarian’s judgment and care. The real question isn’t whether AI will take over libraries, it’s how both AI and librarians can work side by side. How AI Helps in Libraries Among 2,000 academic library professionals globally, many said they don’t have enough time or budget to learn new tools or skills, a challenge made even harder as global digital content is projected to double every two years. Here’s where AI tools for librarians prove useful: Cataloguing: AI can scan metadata and suggest subject tags in minutes. Search: Smarter search systems help students and researchers find relevant materials without digging through dozens of irrelevant results. Day-to-day tasks: Think overdue notices, compiling basic reading lists, or identifying key sources and trends to support literature reviews. This is where library automation with AI comes in handy. Instead of replacing people, these tools free up time. A librarian who doesn’t have to spend hours sorting through data can focus on supporting students, curating collections, analysing usage statistics to make informed decisions or tracking resource usage against budgets. Where Human Expertise Still Matters AI is fast, but it’s not thoughtful. A student asking, “I’m researching migration patterns in 19th-century Europe, where do I start?” gets much more from a librarian than from a search algorithm. Librarians bring context, empathy, and critical thinking that machines can’t replicate. This is why human-AI collaboration in libraries makes sense. AI takes care of the routine. Humans bring the nuance. Together, they cover ground neither could manage alone. Finding the Balance So how do libraries get this balance right? A few ideas: Think of AI as a helper – not a replacement for staff. Invest in training – librarians need to feel confident using AI tools and knowing when not to rely on them. Keep the focus on people – the goal isn’t efficiency for its own sake, it’s about better service for students, researchers, and communities. Final Thoughts By using AI to handle routine administrative tasks like cataloguing, managing records, or tracking resource usage, librarians free up time to focus on the part of the job that drew them to this profession in the first place: supporting researchers and students, curating meaningful collections, and fostering learning. Combining the efficiency of AI in libraries with the expertise of librarians creates a future where technology supports the human side of education. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Best AI Productivity Tools for Students and Researchers
AI productivity tools are digital platforms that use artificial intelligence to help researchers work more efficiently. Unlike traditional software, these tools use algorithms and machine learning to automate routine tasks, process large amounts of information, and generate insights. Traditional productivity apps rely on manual input. AI-powered tools can learn from user habits, interpret natural language, and offer smart suggestions. For researchers, this means tasks like transcription, organisation, and project management happen faster with less effort. The benefits of AI-powered productivity tools for students to enhance academic workflows include: Time efficiency: Automated transcription and summarisation Accuracy: Reduced manual errors in data processing Organisation: Smart categorisation of notes, tasks, and references Collaboration: Real-time sharing and editing of documents and projects Quick comparison of Otter.AI, Bit.ai, Notion, and Todoist AI productivity tools offer different features for research, writing, collaboration, and task management. Understanding which tool handles which function helps you choose the right combination. ToolTranscriptionDocument CollaborationTask ManagementKnowledge OrganizationOtter.AIYesLimited (shared notes)NoKeyword search, highlightsBit.aiNoYesLimitedCentralized workspaceNotionNoYesYesDatabases, linked notesTodoistNoLimited (shared tasks)YesProject lists Each tool provides a free version, making them accessible to students and researchers who want to try basic features. Advanced features for collaboration, automation, and AI-powered suggestions are available in paid plans. Best-fit scenarios for each tool: Otter.AI: Recording and transcribing interviews, lectures, or meetings Bit.ai: Collaborative writing, team documentation, and organising research materials Notion: Managing literature reviews, creating structured research databases, and planning projects Todoist: Tracking deadlines, managing tasks for long-term research projects Where Otter.AI fits in the research workflow Otter.AI uses speech-to-text technology to convert spoken words into written text. In research, it captures and documents conversations, meetings, interviews, and lectures automatically. The tool processes audio in real time and generates a digital transcript that can be reviewed and edited after the session. The platform provides real-time transcription, converting speech into text as it happens. This works during interviews or classroom lectures, recording and transcribing spoken content simultaneously. The tool identifies and labels different speakers, helping track who is talking in group settings. Transcription accuracy depends on audio quality, background noise, and speaker clarity. Once a transcript is created, it becomes a searchable text document. You can search for specific phrases, topics, or keywords within the transcript to locate information quickly. The platform highlights keywords or important sections, making it easier to analyse large volumes of qualitative data. This searchable database supports reviewing, coding, and referencing spoken information during research analysis. How Bit.ai streamlines collaborative writing Bit.ai is a document collaboration platform that uses AI to help research teams and co-authors work together on academic projects. It creates a single online space for groups to create, edit, and organise research documents. The platform allows users to embed rich media such as images, videos, and interactive charts directly into documents. So as a team, you can edit the same document simultaneously, and changes appear instantly for everyone. AI features suggest content improvements, recommend citations, and help organise ideas as users write. Bit.ai provides a centralised workspace where teams can store and arrange research materials, references, and notes. Users create folders for different projects or topics, making it easier to locate specific files and information. All team members can access shared resources and contribute to the collective knowledge base. Managing projects and deadlines with Todoist AI Todoist AI handles project management for research workflows that include multiple deadlines, contributors, and project phases. The platform helps with planning and tracking ongoing or long-term academic projects, such as group research papers, lab work, or thesis development. The AI task management tools use AI to rank tasks according to their deadlines, dependencies, and importance within each stage of a research project. The system analyses which tasks are most urgent, identifies which activities rely on others being completed first, and adjusts priorities as new information is added or project phases change. Smart scheduling features include intelligent allocation of time blocks for each task based on deadlines and workload. The platform generates automated reminders for important milestones, such as draft submissions, experiment dates, or meetings. When timeline changes occur, Todoist AI updates the schedule and sends notifications to keep team members aware of upcoming deadlines. Organising knowledge bases in Notion AI Notion AI combines note-taking, databases, and task management in one platform. Researchers use Notion AI to organise articles, research notes, and project documents in a single, structured environment. This tool supports literature management and research organisation for individuals and teams. The AI processes and summarises text from research notes, meeting minutes, or uploaded literature. It generates concise overviews of long passages and extracts main ideas from academic content. The system answers user questions by searching through stored notes and documents, providing relevant information based on previous entries. Notion AI offers database templates designed for academic workflows: Literature review templates: Fields for citation details, summaries, and key findings Data collection templates: Record variables, sources, and results Research planning templates: Structure timelines, objectives, and progress trackers Each template can be customised to meet the requirements of a specific research process. Integrating tools with reference managers and libraries Best AI tools for students often work together with reference managers and digital research libraries. This setup helps researchers organise sources and manage citations more efficiently. Many tools support direct or indirect connections to widely used academic platforms. Zotero and Mendeley are reference management systems that collect, organise, and cite academic sources. Both platforms have integration options with AI productivity tools. Some document collaboration platforms and note-taking apps allow users to export references in formats compatible with these reference managers. Browser plugins and word processor add-ons let users insert citations and bibliographies into research documents. Zendy's AI-powered research library works alongside productivity and reference management tools. Users can discover and access full-text articles through Zendy, then export citations to reference managers. Zendy's platform supports AI summarisation, key phrase highlighting, and organised reading lists, which streamline literature reviews and project planning. When used with collaborative writing or task management tools, Zendy provides a central source for reliable academic content and citation data. Choosing the right tool mix for your research Selecting AI productivity tools for students and research involves matching tool features to specific project requirements. The best combination depends on research objectives, group size, and preferred working methods. Each tool offers different functions, so understanding your workflow is the first step. Assessment criteria include research type, collaboration needs, and technical requirements. Qualitative research involving interviews and discussions often uses transcription tools like Otter.AI, while quantitative projects may focus on organisation and project management. Research conducted in teams benefits from document collaboration platforms that support shared editing and centralised knowledge. Technical requirements include compatibility with institutional systems, device support, integration with reference managers, and data privacy standards. Consider whether the tool works on preferred devices and integrates with other software used for citations or data storage. Many AI productivity tools offer free versions with core features suitable for individual students or small projects. Larger teams or advanced projects may use paid plans that unlock collaboration, automation, or additional storage. Institutional licenses sometimes provide access to premium features at no individual cost. Implementation tips for secure compliant use Academic and institutional environments require careful management of data privacy and security when using AI productivity tools. Each tool interacts with research data differently, so understanding how information is handled protects both individual and institutional interests. GDPR compliance applies to any tool that processes or stores personal information of individuals in the European Union. Institutional data policies often include guidelines on where research data may be stored, who can access it, and how long it can be retained. Secure handling involves using encrypted connections, selecting tools with end-to-end encryption, and ensuring sensitive files are shared only within approved platforms. Introducing AI tools to research teams involves several steps: Testing phase: Select a small group to test the tool and provide feedback Documentation: Create clear guidelines for using tools within research workflows Training: Help team members understand secure and responsible usage Role establishment: Set up administrators, data managers, and regular users Regular reviews: Assess whether tools continue to meet privacy requirements Discover Zendy for limitless research access Zendy, AI AI-powered research library, acts as a central research hub that connects with AI productivity tools used in academic work. The platform provides access to scholarly articles, journals, and academic resources across disciplines. Features such as ZAIA, AI assistant for research, AI-powered summarisation, key phrase highlighting, and organised reading lists help manage literature and support research projects. You can export citations to reference managers and create structured workflows for academic tasks. For researchers looking to integrate comprehensive literature access with their productivity workflow, Zendy's AI-powered research library provides the foundation for efficient academic research. FAQs about AI productivity tools for students and researchers How do AI transcription tools handle sensitive interview recordings? Most AI productivity tools use encryption and privacy controls to protect sensitive recordings. Researchers need to verify compliance with institutional data policies and obtain participant consent when managing such data. Can Otter AI transcribe interviews without internet connection? Otter.AI requires internet connection for real-time transcription. Some features work offline with limited functionality, but full transcription capabilities need online access for processing. Which productivity tool works best with Zotero and Mendeley? Notion provides flexible integration through its API, allowing various connections with citation management software. Bit.ai offers direct export features for popular reference managers like Zotero and Mendeley. Do these AI tools support research content in languages other than English? Language support varies by tool. Otter AI includes multiple language transcription capabilities, while Notion AI processes text in various languages for research content management. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
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