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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Research Rabbit: Visual literature mapping tool for exploring academic papers and citation networks (limited by outdated MAG database).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 Curator to Digital Navigator: Evolving Roles for Modern Librarians

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: AI-Enhanced DiscoveryUsing tools like ZAIA to help researchers refine queries and find relevant studies faster. Research Data Management Organising, preserving, and curating datasets for long-term academic use. Ethical AI and Digital Literacy Training Teaching researchers how to verify AI outputs, evaluate bias, and maintain academic integrity. 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. .wp-block-image img { max-width: 65% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202512 Mins ReadDiscover

Strategic AI Skills Every Librarian Must Develop

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. .wp-block-image img { max-width: 75% !important; margin-left: auto !important; margin-right: auto !important; }

Nov 25, 202511 Mins ReadDiscover

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; }

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