Research Better: 6 Best AI Tools for Research Efficiency
Jan 25, 2024 |
9 Mins Read
The increased usage of AI tools has sparked many conversations in the world of academia, which inspired the innovation of AI tools for research. The integration of technology in academia has brought innovation that introduced digital libraries, plagiarism trackers and grammar-checking softwares, this changed the way researchers write and discover academic literature. In recent years, the academic sphere has witnessed the rapid growth and implementation of AI, which we believe can be leveraged to create efficiency in research.
As an AI-powered research library, Zendy provides key tools like summarisation and keyphrase highlighting to streamline the process of literature review, we also developed and launched ZAIA, the AI research assistant. These tools have been designed to create efficiency in research, which allows researchers to invest significant time in data analysis and their primary research.
With the growing integration of AI products, we believe in the ethical use of AI in the world of research while also leveraging this technology to streamline research processes for students and researchers alike. Ethical AI practices are essential for building tools that are both trustworthy and effective.
On the other hand, according to our latest survey, AI in Education for Students and Researchers: 2025 Trends and Statistics, 73.6% use AI in research, 51% use it for literature review and 46.3% of students and researchers are using AI tools for writing and editing, showing just how quickly these tools are being adopted.
However, some of the AI ethical issues remind us that without proper guidelines, these tools risk compromising the integrity of research. Notably, slightly less than a third of students still do not use AI tools for research at all.
In this blog, we recommend the 6 best AI-driven tools to assist you with annotating, citing and more!
1. ZAIA - AI Assistant for Research
ZAIA is a domain-specific LLM designed to assist researchers in understanding essential research concepts and finding relevant papers. This tool enhances the efficiency of the literature review process, providing answers backed by millions of academic research papers.
Housing over 200 million papers from all fields of science, this tool generates a comprehensive summary of an academic paper, including the area of study the research addressed and its overall impact on the discipline.
3. Paperguide
Paperguide is an all-in-one AI workspace for researchers to conduct literature reviews, understand and extract data from scientific papers, collaborate, manage and write research.
4. Tableau
This tool generates data visualisation and analytical tools for businesses and researchers. The platform is equipped with Einstein AI which is driven by machine learning and delivers predictions and recommendations within Tableau workflows to assist with efficient decision-making.
5. Scite.ai
This is a well-rounded citation tool that provides context to citations by clearly stating whether an academic paper supports or contrasts the cited claim. This helps researchers save time having to read lengthy papers and quickly determine whether the citation is relevant to their research.
6. Consensus AI
This tool annotates insights about research papers using AI. It produces “study snapshots” to condense lengthy research papers by mentioning study aims, variables, and findings. The platform also provides credible responses backed by academic papers to queries, by presenting research papers that both support and contrast the query, allowing users to browse through objective responses.
Limitations of AI Tools
While AI tools are effective in streamlining research processes, they raise ethical concerns and can impact research integrity if misused. AI raises ethical issues including data privacy, algorithm bias and misuse of AI. However, due to the increasing usage of AI tools, policies are developing quickly to ensure the technology is adequately regulated.
Tips for Using AI Tools in Research
Follow these recommendations to ensure ethical AI usage:
- Always fact-check content generated by AI tools
- Do not write academic articles using AI tools. Instead, use these tools to edit and structure original research content.
- Do not use AI tools to generate references, instead use it to manage and store the references.
- Use AI tools that produce accurate results.
- Incorporate your own voice and style of written content as much as possible.
In conclusion, AI is a resourceful innovation in academic research when used ethically. The correct and responsible usage of AI can create immeasurable efficiency in research processes, and aid in citation management, resource annotation, data visualization, literature discoverability and summarisation.
Discover a comprehensive suite of AI-driven tools like summarisation, keyphrase highlighting and ZAIA - AI assistant for research on Zendy now.
Key Considerations for Training Library Teams on New Research Technologies
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. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }
Digital Information Literacy Guidelines for Academic Libraries
Information literacy is the skill of finding, evaluating, and using information effectively. Data literacy is the skill of understanding numbers and datasets, reading charts, checking how data was collected, and spotting mistakes. Critical thinking is the skill of analysing information, questioning assumptions, and making sound judgments. With so many digital tools today, students and researchers need all three skills, not just to find information, but also to make sense of it and communicate it clearly. Why Academic Libraries Should Offer Literacy Programs Let’s face it: research can be overwhelming. Over 5 million research papers are published every year. This information overload means researchers spend 25-30%1 of their time finding and reviewing academic literature, according to the International Study: Perceptions and Behavior of Researchers. Predatory journals, low-quality datasets, and confusing search results can make learning stressful. Libraries are more than book storage, they’re a place to build practical skills. Programs that teach information and data literacy help students think critically, save time, and feel more confident with research. Key Skills Students, Researchers, and Librarians Need Finding and Using Scholarly Content Knowing how to search a database efficiently is a big deal. Students should learn how to use filters, Boolean logic, subject headings and, of course, intelligent search. They should also know the difference between journal articles, conference papers, and open-access resources. Evaluating Sources and Data Not all information is equal. Programs should teach students how to check if sources are reliable, understand peer review, and spot bias in datasets. A few practical techniques, like cross-checking sources or looking for data provenance, can make research much stronger. Managing Information Ethically Citing sources properly, avoiding plagiarism, and respecting copyright are essentials. Tools like Zotero or Mendeley help keep references organised, so students spend less time managing files and more time on research. Sharing Findings Clearly Communicating is sharing, and sharing is caring. It’s one thing to collect information; it’s another to communicate it. Using infographics, slides, or storytelling techniques to make research more memorable. Ultimately, clear communication ensures that the work they’ve done can be understood, used, and appreciated by others. Frameworks That Guide Literacy Programs ACRL Framework: Provides six key concepts for teaching information literacy. EU DigComp / DigCompEdu: Covers digital skills for students and educators. Data Literacy Project: Helps students understand how to work with datasets, complementing traditional research skills. These frameworks help librarians structure programs so students get consistent, practical guidance. Steps to Build a Digital Literacy Program Audit Campus Needs: Talk to students and faculty, see what resources exist, and find gaps. Set Learning Goals: Decide what students should be able to do at the end, and make goals measurable. Select Content and Tools: Choose databases, software, and datasets that fit the library’s budget and tech setup. Create Short, Modular Lessons: Break skills into manageable pieces that build on each other. Launch and Improve: Introduce the program, gather feedback, and adjust lessons based on what works and what doesn’t. Teaching Strategies and Online Tools Flipped and Embedded Instruction Students watch a short video about search techniques at home, then practice in class. A librarian might join a research methods class, helping students build search strings live. Pre-class quizzes on topics like peer review versus predatory journals prepare students for hands-on exercises. Short Videos and Tutorials Quick videos (2–5 minutes) can teach one skill at a time, like citation management, evaluating sources, or basic data visualisation. Include captions, transcripts, and small practice exercises to reinforce learning. AI Summaries and Chatbots AI tools can summarise articles, suggest keywords, highlight main points, and even draft bibliographies. But they aren’t perfect, they can make mistakes, miss nuances, or misread complex tables. Human oversight is still important. Free Resources and Open Datasets Students can practice with free databases and datasets like DOAJ, arXiv, Kaggle, or Zenodo. Using one of the open-access resources keeps programs affordable while providing real-world examples. Checking if Students Are Learning Before and After Assessments: Simple quizzes or tasks to see how skills improve. Performance Rubrics: Compare beginner, developing, and advanced levels in searching, evaluating, and presenting data. Analytics: Track which videos or tools students use most to improve future lessons. Working With Faculty Embedded Workshops: Librarians teach skills directly tied to assignments. Joint Assignments: Faculty design research projects that naturally teach literacy skills. Faculty Training: Show instructors how to integrate digital literacy into their courses. Tackling Challenges Staff Training: Librarians may need extra help with data tools. Peer mentoring and workshops work well. Limited Budgets: Open access tools, collaborative licensing, and free platforms help make programs feasible. Distance Learners: Make videos and tutorials accessible anytime, account for different time zones and internet access. Looking Ahead AI, open science, and global collaboration are changing research. AI can personalise learning, but it still needs oversight. Open science and FAIR data principles (set of guidelines for making research dataFindable,Accessible,Interoperable, andReusable to both humans and machines) encourage transparency and reproducibility. Libraries can also connect with international partners to share resources and best practices. FAQs How long does a program take to launch?Basic services can start in six months; full programs usually take 1–2 years. Do humanities students need data skills?Yes, focus is more on qualitative analysis and digital humanities tools. Where can libraries find free datasets?Government repositories, Kaggle, Zenodo, and university archives. Can small libraries succeed without data specialists?Yes, faculty collaboration and online resources can cover most needs. .wp-block-image img { max-width: 75% !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; }
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