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Top 46 AI Tools for Research in 2025 (Writing, Citations, Literature Review & More)

Jun 16, 2025 |

23 Mins Read

Five years ago, many believed Web 3.0 and a decentralised internet would reshape how we interact online. Instead, the real change came from artificial intelligence (AI). Quietly, it started showing up everywhere, from how we search to how we write and learn. In research, the impact of change is particularly evident. AI research tools have evolved beyond simple assistance. It's now critical to how we study, gather information, and break down complex ideas.

In our recent 2025 AI survey by Zendy shows just how common AI tools for research have become: 73.6% of students and researchers say they use AI tools, with over half of them using AI tools for literature reviews and nearly as many using them for writing and editing.

Table of contents:

AI Research Assistants for Students:
ZAIA, Elicit, Perplexity AI, Research Rabbit, Scite, ChatGPT, Connected Papers
AI-driven Literature Review Tools:
Zendy, Litmaps, ResearchPal, Sourcely, Consensus, R Discovery, Scinapse.io
AI-powered Writing Assistants:
PaperPal, Jenny.AI, Aithor, Wisio.app, Trinka AI, Grammarly
AI Tools for Data Analysis in Research:
Julius AI, Vizly, ChatGPT-4o, Polymer, Qlik
AI Paraphrasing Tools for Students:
Ref-n-write, SciSpace, MyEssayWriter.ai, Scribbr, Rewrite Guru
AI Productivity Tools for Researchers
Otter AI, Bit.ai, Todoist, Notion
AI Tools for Thesis Writing:
TheseAI, Gatsbi, Writefull, Thesify
AI Citation Management Tools:
Zotero, EndNote, Mendeley, RefWorks
AI Tools for Creating Research Presentations
Gamma, Presentations.AI, PopAI, AiPPT

AI Research Assistants for Students

Here are some of the favourite AI research assistants for students

ZAIA: Zendy's AI-powered research assistant, delivering precise, reference-backed academic insights and PDF analysis, saving time and enhancing focus
Elicit: An AI research assistant that helps with literature reviews by summarising academic papers and refining research questions, but it's limited to open-access sources and lacks full PDF upload support
Perplexity AI: Search-based chatbot offering sourced answers from web and academic content, however, it's good to keep in mind that perplexity was not designed for research support.
Research Rabbit: Visual literature mapping tool for exploring academic papers and citation networks (limited by outdated MAG database).
Scite: Citation analysis tool showing how papers reference each other, useful for evaluating credibility (paid, no full-paper summaries).
ChatGPT (with research plugins): Versatile AI assistant for summarising, brainstorming, and drafting academic content (requires fact-checking).
Connected Papers: Visual graph tool for discovering related research papers (limited journal coverage, no deep analysis).

AI-driven Literature Review Tools

Now you can save weeks, if not months, just by using one of these AI-driven literature review tools below:

Zendy: AI-powered research platform offering access to millions of peer-reviewed papers with summarisation and citation tools (some features require payment).
Litmaps: Visual citation mapping tool for tracing research connections and trends (no content analysis).
ResearchPal: AI assistant for literature reviews and reference management, integrates with Zotero/Mendeley (paid plans for full features).
Sourcely: Source-finding tool that suggests and cites relevant papers from 200M+ database (limited paywall access).
Consensus: Search engine highlighting scientific consensus on topics using peer-reviewed sources (limited free version).
R Discovery: Mobile app for personalised research paper discovery with audio/translation features (no deep analysis).
Scinapse.io: Free citation-based academic search tool with AI-generated mini-reviews (limited full-text access).

AI-powered Writing Assistants

A good research article or study is recognised by how it’s written. Below, you’ll find top AI tools for research to improve your academic writing skills.

PaperPal: AI writing assistant for academic papers with grammar/clarity checks and citation help (limited to formal writing).
Jenny.AI: Fast draft generator for academic content (requires heavy editing, better for writing than research).
Aithor: AI-assisted academic writing tool with multilingual support (mixed reviews on output quality).
Wisio.app: Writing coach for academic drafts with AI/human feedback (focused on refinement, not speed).
Trinka AI: Specialised grammar/citation checker for technical writing (English-focused).
Grammarly: Real-time grammar/spelling checker for academic writing (lacks research-specific features).

AI Tools for Data Analysis in Research

Some tools focus on cleaning and organising your data, while others assist with analysis or even visualising results.

Julius AI: Conversational data analysis tool for quick stats and forecasting (free tier has dataset limits).
Vizly: AI-powered spreadsheet visualiser for charts and trends (10 free AI interactions/month).
ChatGPT-4o: Flexible AI for dataset Q&A and brainstorming (can’t process raw files directly).
Polymer: No-code dashboard generator for interactive data visuals (limited customisation options).
Qlik: Advanced data integration and visualisation platform (steeper learning curve).

AI Paraphrasing Tools for Students

But keep in mind that paraphrasing doesn't avoid plagiarism, and you still need to cite sources. Here are some of the best AI tools for research that focus on paraphrasing:

Ref-n-write: Academic writing assistant with paraphrasing tools and phrasebank (Word/Google Docs plugin).
SciSpace: PDF-based AI tool for simplifying and rewriting academic texts (no full-document processing).
MyEssayWriter.ai: Quick essay generator/paraphraser for early drafts (multilingual but generic output).
Scribbr: Plagiarism checker and proofreading tool with synonym suggestions (125-word input limit).
Rewrite Guru: Customisable rephrasing tool with grammar/plagiarism checks (less academic-focused).

AI Productivity Tools for Researchers

True accessibility means being able to access, use, and benefit from a tool with ease. In research, that also means saving time.

Otter AI: Lecture transcription tool for real-time note-taking (accuracy depends on audio quality).
Bit.ai: Collaborative workspace for organising research with academic templates (AI features require payment).
Todoist: Task manager for breaking down academic projects (may be excessive for simple needs).
Notion: All-in-one workspace for notes, databases, and research organising (limited offline use).

AI Tools for Thesis Writing

These tools won’t write your thesis for you, but they can help you stay organised, improve your writing, and work more efficiently.

ThesisAI: AI thesis generator with citations and multi-format export (pay-per-document model).
Gatsby: AI co-scientist for technical documents with equations/citations (paid subscription required).
Writefull: Academic writing assistant for grammar, abstracts, and LaTeX (may struggle with technical terms).
Thesify: Critical thinking partner for thesis feedback (no grammar checks, focuses on structure/flow).

AI Citation Management Tools

Here are the top citation management and referencing tools in 2025 for researchers and students.

Zotero: Free, open-source reference manager with citation tools and PDF annotation (limited free storage).
EndNote: Premium reference manager for large projects with Word integration (steep learning curve).
Mendeley: Free reference manager with academic social network (occasionally clunky interface).
RefWorks: Institution-focused cloud reference manager (requires university subscription).

AI Tools for Creating Research Presentations

Presenting your research effectively is just as important as conducting it. Here are top AI tools for research presentations that can save you time while helping deliver your findings in a polished, professional format.

Gamma: AI-powered tool for fast academic slide creation from text (may need manual tweaks).
Presentations.AI: Simple research-to-slides converter with real-time collaboration (limited design flexibility).
PopAI: Interactive presentation maker with quizzes/media (steep learning curve for full feature use).
AiPPT: One-click document-to-slide converter with smart formatting (advanced customisation requires effort).

Conclusion

AI is no longer just a tool in the research process, it’s a collaborator. However, these tools aren’t perfect; they often vary in accuracy, depth, and usability. For this reason, not every tool will be a good fit for every stage of research. As a result, it’s important to explore, test, and use a multitude of tools that fit your needs. As these technologies continue to evolve, staying curious and adaptable is the best way to keep your research sharp, stay competitive, and be ready for the future.

Most importantly, always fact-check your sources, verify references, and critically review AI-generated content for clarity, accuracy, and originality. When using AI for writing or paraphrasing, ensure the final output reflects your own understanding, voice, and academic intent.
Don’t forget that ethical publication practices should always come first. Follow your institution’s policies on AI use, cite AI-generated assistance where necessary, and avoid relying on tools in ways that could be considered plagiarism or lead to misrepresentation.

From Boolean to Intelligent Search: A Librarian’s Guide to Smarter Information Retrieval

For decades, librarians have been the trusted guides in the vast world of information. But today, that world has grown into something far more complex. Databases multiply, metadata standards evolve, and users expect instant answers. Traditional search still relies on structured logic, keywords, operators, and carefully crafted queries. AI enhances this by interpreting intent rather than just words. Instead of matching text, AI tools for librarians analyse meaning. A researcher looking for “climate change effects on migration” won’t just get papers containing those words, but research exploring environmental displacement, socioeconomic factors, and regional studies. This shift from keyword to context means librarians can spend less time teaching a researcher how to “speak database” and more time helping them evaluate and use the results effectively. The Evolution of Library Search Traditional search engines focus on keywords and often return long lists of potential matches. With AI, libraries can now benefit from search engines that employ natural language processing (NLP) and machine learning (ML) to understand user queries and map them to the most relevant resources, even when key terms are missing or imprecise. Semantic search, embedding-based retrieval, and vector databases allow AI to find conceptually similar resources and suggest new directions for research. Examples of AI Tools for Librarians AI ToolMain FunctionLibrarian BenefitZendyAI-powered platform offering literature discovery, summarisation, keyphrase highlighting, and PDF analysisSupports researchers with instant insights, simplifies literature reviews, and improves discovery across 40M+ publicationsConsensusAI-powered academic search enginemanaging citation libraries, efficient literature reviewEx Libris PrimoIntegrates AI for discovery and metadata managementImproves record accuracy and user experienceMeilisearchFast, scalable vector search with NLPEnhanced search for large content databases The Ethics of Intelligent Search AI doesn’t just retrieve; it prioritises. AI tools for librarians determine which results appear first, whose research receives visibility, and what remains hidden. This creates ethical questions around transparency and bias. Librarians are uniquely positioned to question those algorithms, advocate for equitable access, and ensure users understand how results are ranked. In an AI-driven world, digital literacy extends beyond knowing how to search—it’s about learning how machines think. In conclusion AI tools for librarians are becoming more accessible. Platforms now integrate summarisation, concept mapping, and citation analysis directly into search. helping librarians and users avoid unreliable content. For libraries, experimenting with these tools can mean faster reference responses, smarter cataloguing, and better support for researchers drowning in information overload. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }

Oct 10, 202516 Mins ReadDiscover

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; }

Oct 1, 20255 Mins ReadDiscover

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; }

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