Tag: #Discover

Many researchers work outside of universities or formal institutions. These independent researchers often rely on public access to scholarly research to study, write, or contribute to their fields.

However, access to scholarly research is not equal. Most academic journals are behind paywalls, which means users must pay to read them unless they are affiliated with an institution that pays for access.

In this blog, we’ll explore the structure of academic publishing and how it affects independent researchers. We’ll break down the current challenges, the systems in place, and recent developments designed to improve research accessibility.

Why Research Accessibility Matters

Research accessibility refers to how easily someone can read, use, and build upon academic studies. For independent researchers, access is often limited because they lack university or library credentials required to unlock paywalled content.

A large portion of scholarly research remains behind subscription paywalls. Many journal articles cost between $30 and $50 each, and full journal subscriptions can reach thousands of dollars per year.

These costs create a divide between researchers affiliated with institutions and those working independently. Independent researchers may be excluded from current findings, which restricts their ability to contribute to academic conversations.

Without equal access, knowledge development becomes uneven. Some communities and individuals are left out, creating a gap in who can participate in scientific and scholarly work.

Understanding Open Access Models

Open access (OA) refers to academic research that anyone can read online without paying. There are different types of open access, and each works in a specific way.

1. Gold Open Access to Scholarly Research

Gold open access means that the final version of a research article is freely available on the publisher’s website. The author or their funder usually pays a fee to make the article open.

Researchers can find gold open access content in fully open access journals listed in the Directory of Open Access Journals (DOAJ). These journals allow anyone to read and download the scholarly research directly from the publisher.

• Reader benefit: Immediate access to the final, formatted version of articles
• Limitation: Authors often pay fees ranging from $500-$3000 to publish

2. Green Open Access

Green open access is when authors share a version of their article in a free online repository. This version may be a preprint (before peer review) or a postprint (after peer review but before journal formatting).

Repositories like arXiv.org specialise in many disciplines, and bioRxiv.org for biology, host these papers. These platforms do not require any affiliation to access the content.

• Reader benefit: Free access to research content, often before formal publication
• Limitation: The version available might not be the final published version

3. Diamond Open Access

Diamond open access journals make articles freely available to read and do not charge authors any fees to publish. Neither readers nor authors pay. One good example of diamond open access is KnE Publishing, an open access publishing service by Knowledge E, provides high-quality publishing services to support the development and advancement of diamond open access journals, with a particular focus on increasing the visibility and accessibility of scholarly research.

This model is often supported by academic institutions or non-profit organisations. The Free Journal Network lists many of these journals.

• Reader benefit: Completely free access with no barriers
• Author benefit: No publication fees to share research

Open Access Model	Who Pays	Where to Find	Version Available
Gold	Authors/funders	Publisher websites	Final published version
Green	No one (usually)	Repositories	Preprint or postprint
Diamond	Institutions/grants	Publisher websites	Final published version

Practical Tools For Independent Researchers

Independent researchers need affordable ways to find and use scholarly research. Several tools make this process easier.

AI Summarisers

AI summarisers extract the main points from academic papers. These AI tools help researchers quickly understand if a paper is relevant to their work without reading the entire document.

Zendy’s AI summarisation tool identifies key findings, methods, and conclusions from scholarly research papers. This saves time when reviewing large amounts of literature.

• Time-saving: Condenses hours of reading into minutes
• Comprehension aid: Helps readers understand complex academic language

Literature Discovery Tools

Discovery tools help researchers find academic papers and locate free versions when available.

Google Scholar indexes scholarly research and sometimes links to free versions.

Zendy uses AI to recommend relevant papers based on your interests.

Browser extensions like Unpaywall and Open Access Button automatically find legal, free versions of paywalled articles.

• Broader search: Searches across multiple journals and repositories at once
• Free alternatives: Identifies open access versions of paywalled content

Scholarly Research Reference Manager Tools

Reference manager tools help organise research papers and create citations. These tools are essential for independent researchers writing their own papers.

Zotero is a free, open-source reference manager that saves papers, creates citations, and integrates with word processors. Mendeley offers similar features with some social networking elements.

• Organisation: Keeps research papers in one searchable library
• Citation help: Automatically formats citations in different styles

Policy Shifts Empowering Independent Scholars

Recent policy changes are increasing the amount of research that is freely available to everyone. These changes help independent researchers access more content without institutional subscriptions.

Plan S requires that research funded by certain organisations be published with open access. This means more high-quality scholarly research is becoming freely available to read.

Many funding agencies now require researchers to share their findings openly. The National Institutes of Health in the US and UK Research and Innovation have policies requiring funded research to be publicly accessible.

Authors are also finding ways to keep their rights to share their work. Rights retention strategies allow researchers to post copies of their articles in public repositories even when publishing in traditional journals.

The trend toward open science continues to grow. More institutions are adopting policies that make research outputs—including data, software, and educational materials—freely available by default.

Ensuring Accessibility For All Researchers

Accessibility in scholarly research goes beyond open access. It also means making content usable for people with disabilities and those using different devices or internet connections.

Universal Design Principles

Universal design makes scholarly research usable by as many people as possible. This includes clear structure, readable text, and compatibility with assistive tools.

Well-designed articles use proper headings, include descriptions for images, and create documents that work with screen readers. These features help all users navigate and understand the content more easily.

Examples of accessible design in scholarly research:

• Structured headings that create a logical outline
• Alternative text for images and diagrams
• Tables with proper headers and simple layouts
• PDF files with proper tagging for screen readers

Assistive Technology Compatibility

Assistive technologies help people with disabilities access digital content. Researchers need to work well with these tools.

Screen readers convert text to speech for people who are blind or have low vision. Text enlargement tools and colour contrast adjusters help people with different visual needs.

When looking for accessible research content:

• PDF accessibility: Look for tagged PDFs that work with screen readers
• HTML versions: Often more accessible than PDFs for assistive technologies
• Plain text options: Simple format that works with most assistive tools

If you need a more accessible version of any scholarly research, you can contact the publisher directly. Many journals now provide alternative formats upon request.

New Innovations in Research Access

The landscape of scholarly access continues to evolve with new models and technologies making research more available to independent scholars.

AI-powered research assistants are changing how people interact with academic literature. These tools can summarise articles, extract key information, and help researchers find connections between papers.

Digital libraries like Zendy are creating alternatives to traditional subscription models. With AI assistants like ZAIA (Zendy’s AI assistant for researchers), these platforms not only partner with publishers to offer access to both open and paywalled content at affordable rates for individual researchers, but also enhance the research experience through AI support.

The future of scholarly research access looks increasingly open and innovative. New technologies and business models continue to break down barriers between knowledge and those who seek it.

FAQs about Accessing Scholarly Research

How can independent researchers find free academic articles legally?

Independent researchers can use open access repositories like PubMed Central and preprint servers like arXiv. Public libraries sometimes offer access to academic databases, and contacting authors directly often results in them sharing their papers.

What makes scholarly research accessible to people with disabilities?

Accessible scholarly research uses proper document structure with headings, provides alternative text for images, creates tables that screen readers can navigate, and offers formats compatible with assistive technologies. Articles in HTML format are typically more accessible than PDFs, and properly tagged PDFs are more accessible than untagged ones.

How do researchers evaluate the quality of open access journals?

Researchers can check if an open access journal is listed in the Directory of Open Access Journals (DOAJ), look for clear peer review policies, verify the journal’s impact factor, and research the editorial board members. Quality open access journals maintain the same rigorous standards as traditional subscription journals.

What AI tool helps independent researchers conduct a literature review?

ZAIA, Zendy’s AI research assistant, helps independent researchers conduct efficient literature reviews by automatically summarising academic papers, extracting key findings, and identifying connections between related studies. Researchers can also use reference managers like Zotero or Mendeley to organise papers and create citations. Literature mapping tools like VOSviewer help visualise research networks and identify influential papers. For comprehensive literature reviews, ZAIA can recommend relevant papers based on your research interests, saving hours of manual searching across multiple databases.

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 latest AI survey, conducted by Zendy team, 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 2026 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.

In a previous blog, we highlighted top 5 best AI tools for literature review that offer a large collection of research materials to support your research. Now, in this blog, we will go through the process and the 3 pillars of writing lit review while providing a real-life literature review example to help you grasp the concept with ease.

What’s a Literature Review?

A literature review is a summary of what other researchers have already discovered about your topic. It helps you figure out what’s already known, what questions still need answers, and how your own research fits into the bigger picture.

What Are the 3 Pillars of Literature Review?

Introduction

The introduction of a literature review should:

• Provide context for the topic
• Define the scope and purpose of the review
• Outline the organisational framework
• Present a clear thesis statement

Body

The body of the literature review is where you analyse and synthesise existing research. Consider organising your review by:

• Themes or concepts
• Chronological order
• Methodological approaches

Conclusion

A strong conclusion should:

• Summarise key findings
• Highlight contributions to the field
• Discuss implications and applications
• Suggest directions for future research

Tips on How to Write a Great Literature Review

• Read widely to get a full picture of your topic.
• Don’t just summarise – analyse and compare different studies.
• Look for patterns and gaps in the research.
• Keep your writing clear and logical.
• Use transition words to help your ideas flow smoothly.

How Zendy Can Help with Your Literature Review

In order to write a strong literature review you need to find the right sources, and of course a tools to help accelerate the process.

Zendy.io isn’t just a digital library that offers access to relevant studies across disciplines, but it also helps you write a solid literature review, Zendy AI tools include features designed to help you with literature reviews. You can:

• Summarise lengthy research articles instantly
• Highlight key phrases and findings
• Ask ZAIA, your personal AI research assistant, to explain complex papers
• Compare multiple sources to find common themes or gaps

It’s a simple way to stay focused, save time, and actually enjoy the process.

Real-Life Literature Review Example

This is my favourite example of a literature review! Assume you’re reviewing literature on climate change and agriculture:

“Researchers have been studying how climate change affects farming for decades. Early studies in the 1990s focused mainly on crop yields (Stephen, 1992; Iana, 1995). As time went on, scientists started looking at more complex issues. For example, Sleman (2005) examined how changing rainfall patterns affect soil quality, while Monica (2010) investigated the spread of new crop diseases in warmer temperatures.

Recent research has taken a broader view. Emad (2018) looked at how climate change impacts entire food systems, from farm to table. Meanwhile, Rodrigo (2020) explored how small-scale farmers in developing countries are adapting to new weather patterns.

Despite all this research, we still don’t know enough about how climate change will affect food security in specific regions. Future studies could focus on creating detailed, local predictions to help farmers prepare for what’s ahead.”

See how this example of literature review flows naturally from one idea to the next? It gives you a clear picture of how research in this area has evolved over time. 

Other Examples of Literature Review

• Chronological literature review example: Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review 
• Methodological literature review example: Literature review as a research methodology: An overview and guidelines
• Theoretical literature review example: Why Do People Migrate? A Review of the Theoretical Literature
• Thematic literature review example: The Use of Technology in English Language Learning: A Literature Review 
• Other literature review example:
  • The Economics of Global Climate Change: A Historical Literature Review
  •  University of West Florida: Literature Review: Conducting & Writing

By following these tips and studying literature review examples, you’ll be ready to start writing a solid literature review.

Remember, the key is to organise your sources, explain how they connect, and point out what still needs to be studied. Happy researching!

A top peer-reviewed journal since 1974 published by Elsevier, founded by Michael C. Jensen, Eugene Fama, and Robert C. Merton. The Journal of Financial Economics (JEF) serves as a dedicated platform for publishing high-quality studies on financial markets, corporate finance, financial intermediation, entrepreneurial finance, corporate governance, organisational economics, macro-finance, behavioural finance, and household finance. 

Focused on both theoretical and empirical research in financial economics. The Journal of Financial Economics prioritises rigorous empirical, theoretical, and experimental contributions, particularly those related to the theory of the firm and financial economics.

Journal of Financial Economics Impact Factor and Rankings

According to the latest updates from VU Journal Publishing Guide, The Journal of Financial Economics has made a big impact, with an impressive 10.4 impact factor as of 2024. That puts it among the top economics and finance journals globally. It ranks 5th out of 600 in the Economics category and takes the top spot—1st out of 233—in Business, Finance. These rankings highlight just how influential the journal is in its field.

Abbreviation	J. FINANC. ECON
ISSN	0304-405X (1879-2774)
eISSN:	N/A
Category	Accounting (Q1); Economics and Econometrics (Q1); Finance (Q1); Strategy and Management (Q1)
WoS Core Citation Indexes	SSCI – Social Sciences Citation Index
Journal Impact Factor (JIF)	10.4
5-year Impact Factor	11.4
Best ranking:	ECONOMICS (Q1) Percentage rank: 99.2%
Country	SWITZERLAND
Publisher	Elsevier

Source: WoS

Journal of Financial Economics Scopus Metrics

According to Scopus data for 2023, the journal’s metrics are equally impressive:

• SCImago Journal Rank (SJR): 13.655
• Source Normalized Impact per Paper (SNIP): 5.048
• CiteScore: 15.8
  

These metrics reinforce the journal’s reputation as a leader in its field, with high rankings in many categories including Accounting, Finance, Economics and Econometrics, and Strategy and Management.

Journal of Financial Economics H-index and Citations

The Journal of Financial Economics has an exceptional h-index of 311, indicating that at least 311 articles published in the journal have been cited at least 311 times each. This high h-index reflects the journal’s strong impact and the enduring relevance of its published research.

What is The Scope of The Journal of Financial Economics?

The journal covers a wide range of topics within finance and economics, including:

• Accounting
• Economics and Econometrics
• Finance
• Strategy and Management

It’s particularly known for its applied papers and case studies section, which provides a platform for scholarly studies of actual cases, events, or practices in the financial world.

Conclusion

The Journal of Financial Economics stands out as a top-tier publication in its field, consistently ranking highly across various metrics. Its high impact factor, impressive SJR, and strong h-index all point to its significant influence in the world of financial economics. For researchers and professionals in finance and economics, publishing or referencing articles from this journal can lend considerable weight to their work.

If you’re doing research, you’ll want to use reliable sources. Peer-reviewed articles are among the best because experts review them before they’re published, ensuring quality and credibility. The benefits of expert peer review in research are significant—it helps maintain high standards, validates findings, and improves the overall reliability of academic work. But how do you find these peer-reviewed journal articles, and how can you tell if a journal is peer-reviewed? Let’s break it down.

What Are Peer-Reviewed Articles?

A peer-reviewed article is one that has been checked by other experts in the field before publication. This process helps make sure the research is solid and trustworthy.

Where Can You Find Peer-Reviewed Journal Articles?

You can find peer-reviewed articles in a few different places:

• University Libraries – If you’re a student or faculty member, your university library probably gives you access to research databases.
• Academic Databases – Websites like PubMed, JSTOR, ScienceDirect, and Web of Science have collections of scholarly peer-reviewed articles.
• Zendy – Affordable and accessible peer-reviewed scholarly content, in partnerships with leading publishers e.g., IntechOpen, IEEE, IT Governance Publishing, and IGI Global, powered by AI. 
• Google Scholar – Some journal articles here are peer-reviewed, but not all. You’ll need to check the source.

See The Growth of Digital Libraries: Benefits, Challenges & Trajectory

How to Recognise Peer-Reviewed Journal Articles

There are 4 ways to check if a journal is peer-reviewed or not:

• Visit the Journal’s Website – Look for a section about their review process.
• Use Library Databases – Many academic databases label peer-reviewed journals.
• Check the Editorial Board – A peer-reviewed journal usually lists experts who review submissions.
• Look It Up on Ulrichsweb – This directory can tell you if a journal is peer-reviewed.

Are Google Scholar Articles Peer-Reviewed?

Not necessarily. Google Scholar collects all sorts of academic work, including conference papers and preprints, which may not have gone through peer review. To check, see if the article was published in a reputable journal.

What Is a Peer Review Example?

Let’s say a scientist submits a research paper to a journal. The editor sends it to other experts, who review it for accuracy and clarity. And then they might suggest changes or reject the paper if it doesn’t meet the journal’s standards. So if the paper gets approved, it’s published as a peer-reviewed article.

What’s the Difference Between Peer Review and an Original Article?

• Peer Review – A process where experts evaluate a research paper before it’s published.
• Original Article – A research paper that presents new findings. Some original articles are peer-reviewed, while others aren’t.

How To Select Peer-Reviewed Journal Articles?

When looking for peer-reviewed articles:

• Check the Journal – Make sure it’s known for publishing peer-reviewed articles.
• Look at the Author’s Background – Are they an expert in the field?
• Review the References – Good research builds on other credible studies.
• Use Trusted Databases – Databases like Zendy, PubMed, and Scopus focus on peer-reviewed work.

How Do You Know If a Peer-Reviewed Article Is Credible?

Even among scholarly peer-reviewed journals, some are more reliable than others. Here’s what to look for:

• Reputation of the Journal – Some journals have stricter standards than others.
• Possible Bias – If a study is funded by a company with an interest in the results, for example, a pharmaceutical company funding a study on its own medication might have an interest in positive findings, be cautious. 
• Strong Research Methods – A reliable, peer-reviewed article clearly explains how the research was conducted and how conclusions were reached.
• Retraction History – Some papers are later retracted due to mistakes or misconduct. Check if the article has been retracted.

Final Thoughts

It’s not hard to find peer-reviewed journal articles; it just takes a trusted source and a clear understanding of what you’re looking for. Digital libraries like Zendy give you access to everything you need in one place, including both free and paywalled peer-reviewed articles, with over 40 million articles across disciplines like engineering, medicine, economics, and more.

The impact factor of journals is a crucial academic publishing metric, serving as a measure of a journal’s influence and importance within its field. For you, as a researcher, and institutions alike, understanding this journal classification is essential for making informed decisions about where to publish and which journals to follow.

What is Impact Factor?

The Impact Factor was created by Eugene Garfield, the founder of the Institute for Scientific Information (ISI). Garfield first mentioned the idea of an impact factor in Science magazine in 1955.

In the early 1960s, Eugene Garfield with the help of Irving H. Sher created the Journal Impact Factor (JIF) to help select journals for the Science Citation Index (SCI). They developed this metric by re-sorting the researcher citation index into a journal citation index.

Initially, the Impact Factor was used internally by ISI to compile the Science Citation Index. In 1975, ISI began publishing the Journal Citation Reports (JCR), which included the Impact Factor calculations for journals.

How is the Impact Factor of Journals Calculated?

By calculating the average number of citations received by articles published in those journals over a set period of time, typically two years.

For example, the 2022 impact factor of journals is calculated as follows:

Journal Impact Factor (JIF) = A / B

Where:

• A = Total number of citations in a given year (e.g., 2023) to articles published in the previous two years (e.g., 2021 and 2022).
• B = Total number of citable items (articles, reviews, etc.) published in those same two years (2021 and 2022).

What Does the Impact Factor of Journals Tell a Researcher?

The impact factor of journals provides you with valuable insights into a journal’s influence and importance within its field. Here’s what the impact factor tells you:

1. Journal Quality: A higher impact factor generally indicates a more prestigious and influential journal in its discipline. This can help you identify high-quality publications for your work.

2. Citation Frequency: The impact factor reflects the average number of citations received by articles published in the journal over a specific period. This indicates how frequently the journal’s content is cited by other researchers.

3. Visibility and Reach: Journals with higher impact factors tend to have broader readership and greater visibility in the academic community. Publishing in these journals can increase the exposure of your research.

4. Research Influence: The impact factor of journals can serve as a proxy for the potential influence of research published in a particular journal. It suggests how impactful the average article in that journal might be.

5. Career Advancement: Publishing in high impact factor journals can be crucial for academic and professional advancement, often considered in tenure decisions, grant applications, and professional evaluations.

6. Comparative Tool: Researchers can use the impact factor to compare journals within the same field, helping them make informed decisions about where to submit their work.

However, it’s important to note that the impact factor has limitations. It doesn’t measure the quality of individual articles, and it can be influenced by factors such as the number of review articles a journal publishes. You should consider the impact factor alongside other metrics (e.g., SJR scores), and qualitative assessments when evaluating journals for your research.

What is a good impact factor?

The impact factor (IF) is a metric used to evaluate the influence and quality of academic journals by measuring the frequency with which their articles are cited. Generally, a higher impact factor indicates a more influential journal within its field. However, “good” impact factors vary significantly across different disciplines. For instance, in biochemistry, impact factors are often categorized as follows:

• Good: 2–4
• Great: 5–8
• Awesome: 9–14
• Excellent: Above 14

It’s important to note that these ranges are approximate and can vary based on specific research areas. Additionally, while impact factors provide insight into a journal’s citation frequency, they do not necessarily reflect the methodological quality or societal impact of individual articles. Therefore, when assessing research quality, it’s advisable to consider multiple metrics alongside the impact factor.

What are the Highest Impact Factor Journals

Some of the top impact factor journals include:

• Medical and Life Sciences
  – CA-A Cancer Journal for Clinicians (254.7)
  – The New England Journal of Medicine (91.245)
  – The Lancet (79.321)
  – Nature Reviews Molecular Cell Biology (94.444)

• Multidisciplinary Sciences
  – Nature (50.5)
  – Science (47.728)

• Physical Sciences
  – Chemical Reviews (60.622)
  – Nature Materials (43.841)
  – Nature Nanotechnology (39.213)

• Environmental Sciences
  – Energy & Environmental Science (38.532)
  – Nature Geoscience (16.908)
• Computer Science and Engineering
  – IEEE Transactions: Systems, Man, and Cybernetics (13.451)

These top-tier journals represent the pinnacle of academic publishing, often featuring groundbreaking research and influential studies.

Academic Journal Impact: Beyond the Numbers

While the impact factor of journals is a valuable journal ranking, it’s important to consider other factors when evaluating academic influence:

• Field-specific considerations: Impact factors can vary significantly between different academic disciplines
• Citation patterns: Some fields have faster citation cycles than others, affecting impact factor calculations.
• Journal scope: Specialised journals may have lower impact factors but still be highly influential in their niche.

Conclusion

Understanding the impact factor of journals is crucial for researchers navigating the academic publishing landscape. While it’s a valuable metric, it should be considered alongside other factors when evaluating journal quality and influence. By staying informed about impact factors and their implications, researchers can make more strategic decisions about where to publish their work and maximise the visibility and impact of their research.

Conducting and writing a literature review has always been the most time-consuming task of any academic research. Weeks of reading countless scientific papers (if not months), summarising key points, and identifying gaps in existing research. Fortunately, AI is making this process a lot easier, faster and more efficient. In this blog, we’ll go through the best AI literature review tools in 2026

Why Use AI Literature Review Tools?

Before we dig into the list of the best AI literature review tools, let’s ask ourselves, why use AI in the first place? The answer is very simple:

• Saving Time: AI literature review tools can quickly scan thousands of research papers and extract relevant information in seconds.
• Improving Accuracy: AI tools in research can help you identify key themes, citations, and trends, reducing the chances of missing important studies.
• Enhancing Organisation: Many AI tools for literature review offer smart categorisation, tagging, and citation management, ensuring a well-structured literature review.

Best 5 AI Tools for Literature Review in 2026

Here are the top AI tools that can help you conduct a literature review:

1. ZAIA by Zendy

ZAIA is not just the best AI literature review tools, it’s also one of the best personal AI research Q&A assistants that will help you effectively explore a large amount of academic research. Keyphrase highlighting, summarisation, PDF analysis, and AI insights make it a great AI tool for the literature review process. 

2. Elicit

Elicit uses AI to automate the research process, allowing you to generate structured summaries, find relevant papers, and extract key insights without manual searching.

3. Research Rabbit

This tool is known for its unique visualisation of research connections. It helps users discover related papers and track the evolution of ideas across different studies.

4. Scite

Scite provides citation analysis with AI-powered insights, allowing researchers to evaluate how a study has been cited in different contexts, supportive, contrasting, or neutral.

5. Semantic Scholar

Powered by AI, Semantic Scholar enhances literature discovery by providing smart recommendations, citation tracking, and insights into academic papers.

How to Write a Literature Review Using AI

It can be quite difficult to write a literature review, but AI can help in several ways:

1. Summarise Key Points: Summarisation by AI condenses long written materials to easily readable insights.
2. Rewrite and Paraphrase: AI is also useful in manuscript improvements to guarantee clarity and consistency while maintaining professionalism in academia.
3. Ensure Proper Citations: AI citation tools help with reference management and formatting.
4. Refine and Edit: Make your literature review more polished and professional by using writing tools to improve readability and flow.

What Is the Difference Between an Annotated Bibliography and a Literature Review?

	Annotated Bibliography	Literature Review
Purpose	Summarises and evaluates each source individually	Synthesises and analyses sources collectively
Structure	Organised as a list of citations with annotations	Organised thematically or methodologically
Depth of Analysis	Focuses on each source’s contribution	Identifies patterns, gaps, and trends in research
Use in Research	Often used as a preparatory step for literature reviews	Used as a foundation for research projects or theses
Writing Style	Concise, source-focused	Integrative, argument-driven

Is It Ethical to Use AI Literature Review Tools?

When using AI-powered literature review tools, keep these principles in mind in order to not compromise your research integrity:

• Use AI responsibly and ethically to avoid plagiarism or creation of misleading content
• Remember that AI is a tool, not a replacement for human expertise
• Critically evaluate the information provided by AI tools
• Exercise judgment when incorporating AI-generated insights into your research

By following these guidelines and leveraging AI tools effectively, you can conduct a more efficient and insightful literature review while maintaining the integrity of your research process.

Disclaimer: AI-generated content should always be reviewed and verified by researchers to ensure accuracy and ethical compliance in academic work.

Conclusion

Literature review tools are making lit reviews easier, faster, and more organised. Whether you’re a student or a researcher, the right tool can help you sort through academic papers, find key insights, and manage citations without getting overwhelmed.

Each tool on this list has something useful to offer. ZAIA is a great choice if you’re willing to use AI literature review tools that highlights key points, summarises research, and helps you navigate academic papers more efficiently. Elicit and Research Rabbit are helpful for finding related studies, while Scite and Semantic Scholar can guide you through citations and academic trends.

AI won’t do all the work for you, but it can take some of the pressure off. If you haven’t tried using AI for your literature review yet, now might be a good time to start.

With millions of scholarly content published every year, in addition to the integration of Artificial Intelligence (AI)  into various fields in the past few years, including the educational sector, AI in education has had, and still, a major impact on simplifying research projects, accelerating discoveries, and optimising learning experiences. Giving students and researchers the chance to work efficiently and effectively more than ever. So, how much time do researchers spend on repetitive tasks that AI can simplify?

On the other side of the story, The global market for AI in education was estimated to be worth $2.5 billion in 2022 and is expected to more than double by 2025, according to the most recent data from AIPRM. However, how exactly are researchers using AI, and what are the challenges they face?

This means that Artificial Intelligence (AI) is changing every aspect of modern life, including education and research. It’s reshaping how students learn, how researchers solve problems, and how educators teach. According to our latest survey, 73.6% use AI in education, 51% use it for literature review and 46.3% of students and researchers are using AI in education for writing and editing, showing just how quickly these tools are being adopted.

AI in education helps by sorting through the tremendous amount of scientific information, analysing large datasets of structured or unstructured data, and spotting connections that might take months to find manually. It also takes care of time-consuming tasks like summarising studies and formatting citations, so researchers can focus on bigger questions. With so much information and so little time, AI isn’t just helpful, it’s becoming a necessity.

Zendy surveyed more than 1,500 students and researchers to understand how they use AI tools. The study shows how people incorporate AI into their academic work, the benefits they find most useful, and the challenges they face. The findings give a clearer picture of AI’s role in academic work and its impact on productivity.

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Demographics of AI Users in Research

Zendy’s study provides insights into who is using AI in education. Most respondents are young learners, early in their academic journey, which gives us a sense of tools and support they’re looking for.

• 60.1% of respondents are female, 36% are male while the rest prefer not to disclose.
• 67.6% are between 18-24 years old, reflecting early-career researchers and students.
• 45% are undergraduate students, 37.2% are high school students, exploring AI tools for learning and research

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Habits and AI in Education Adoption

Students and researchers are highly engaged in academic literature and are shifting toward AI-driven tools for efficiency. Zendy’s survey reveals how dedicated students and researchers are to expanding their knowledge and staying current in their fields. Regularly engaging with academic literature is a key part of their studies and professional growth, reflecting the effort they invest in learning.

The survey also shows a clear preference for online databases, highlighting a growing reliance on digital tools for easy access to research materials. This shift points to a broader move toward more convenient and centralised platforms, supported by the use of responsible AI and other technologies in academic work. These findings underline the importance of user-friendly, well-resourced tools that meet the changing needs of today’s learners and professionals.

• 71.5% read research papers daily or several times a week, indicating high engagement
• 49.3%  of respondents spend an average of 4.5 hours each day engaging with research papers
• 50% prefer online databases for accessing research articles, reflecting the growing digitisation of academic research.

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How is AI Being Used in Research?

The study highlights how AI in education is transforming scholarly content practices, with more researchers using it in their daily routines. One of the most common uses is for literature reviews, traditionally a time-consuming task that AI is helping to make faster and more manageable. The findings show a willingness to embrace AI and point to key areas where it can have an even greater impact, especially in literature reviews, writing, and editing.

• 73.6% have used or are exploring AI tools for research.
• 51% use AI for literature reviews.
• 46.3% for writing and editing, highlighting key areas for AI development

These findings indicate a widespread acceptance of AI in education and a growing demand for AI-powered tools.

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Accessibility and Device Preferences

With research becoming increasingly digital, the choice of devices used for academic work is evolving, most users still rely on desktops for research, and more researchers are turning to mobile devices. This shift highlights the need to focus on making mobile access smoother and more user-friendly, all while ensuring that the desktop experience remains just as reliable and effective.  

• 57.9% prefer desktops valuing stability and a larger screen.
• 34.8% prefer mobile devices for reading research, emphasising convenience and portability.
• 7.3% prefer tablets.

This trend highlights the need for mobile-friendly AI-powered research platforms while maintaining robust desktop experiences.

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Impact of AI in Education

Finally, researchers were asked about their perception of AI’s effectiveness in academic work. Over half of the respondents shared they consider AI tools to be highly effective, particularly for simplifying complex tasks. Many highlighted how impactful these tools are in saving hours and making the research journey more efficient, showing just how valuable AI has become in the academic sector.

• 39.6% find it very effective
• 33.4% find it effective
• 21.8% are neutral
• 3.7% think it’s ineffective
• 1.5% think it’s very ineffective 

The overwhelming majority see AI as a valuable tool, streamlining research and saving time.

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Ethical Concerns & AI Limitations

AI offers many benefits, but there are still some ethical issues to work through:

• Bias and Accuracy – AI in education can reflect biases in the data it’s trained on, which can lead to misleading results.
• Ethical Concerns – Researchers need to make sure AI-generated content meets academic integrity standards.
• Cost and Access – Some AI tools are expensive, making them harder to access for students and researchers with limited resources.

To address these AI ethical issues, educators, researchers, and technology providers need to work together to ensure AI is used responsibly in academia.

The Future of AI in Education

AI in education is evolving rapidly, and the trends from Zendy’s survey suggest where it’s headed next.

With 73.6% of respondents already using AI in education, its role will only expand. One of the biggest areas of growth is predictive analysis, where AI is expected to help researchers spot patterns in massive datasets—an extension of how AI is already streamlining literature reviews and data organisation today.

Collaboration is another key area. As AI in education tools become more sophisticated, they will help researchers across different disciplines and countries work together more efficiently, reducing language barriers and improving access to global knowledge.

As AI technology advances, its impact on academic research will deepen, offering both opportunities and challenges. The focus now is on ensuring these tools remain accessible, ethical, and aligned with researchers’ real needs.

Finally, AI in education is set to transform experimentation and simulations. Innovations in AI-driven modelling, combined with augmented and virtual reality, could make complex experiments more interactive, accurate, and scalable.

Conclusion

The survey offers a closer look at how AI is undeniably shaping the future of education, specially scientific research, helping students and researchers work more efficiently. From automating literature reviews to improving writing and editing, it’s clear that AI in education is becoming an indispensable part of academic workflows. However, challenges like affordability and accessibility remain key areas to address, ensuring that AI-powered research tools remain accessible and fair for everyone. 

At Zendy, we are committed to developing AI-driven tools that cater to the real needs of students, researchers, and professionals.

Download the full report to learn about the methodology behind our findings, explore deeper insights into AI in education, and discover how it’s shaping the academic world.

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Going through mountains of papers, studies, and data is a common task when working on a research project. Finding a logical approach to combine all of this information is the hard part. This process is called synthesis, a skill that can help make your research be clearer, more insightful, and more impactful. In simple terms, synthesis in research means combining ideas from different sources to create a new understanding. 

But here’s the thing: synthesis is often confused with summarising or analysing, which can lead to unclear or incomplete results. In this blog, we’ll break down what synthesis really means, how it’s different from other processes, and how you can do it well. Let’s get started.

What is Synthesis in Research?

It’s about connecting ideas, data, and findings from multiple sources to generate a new perspective. It’s not just repeating what others have said—it’s about connecting the dots to see the bigger picture and how sources relate to your main idea

For instance, suppose you are researching the effects of exercise on mental health. One study may indicate that yoga enhances mood, another suggests jogging lowers anxiety and a third that emphasises the advantages of team sports for social well-being. In order to conclude that different kinds of exercise have different positive effects on mental health, synthesis in research involves connecting these studies together.

How is Synthesis in Research Different from Summarising and Analysing? 

A lot of researchers mix up synthesis with summarising or analysing. Here’s how they’re different:

Synthesis vs. Summarising 

• Summarising Summarising means condensing the key points of a source or multiple sources without adding new insights.
  • Example: If three studies say that exercise improves mental health, a summary would list those findings without connecting them.
• Synthesis goes a step further. It combines ideas from multiple sources to create a new understanding.
  • Example: Connecting findings about running, yoga, and team sports to show how different types of exercise benefit mental health in unique ways.

Synthesis vs. Analysis

• Analysis involves breaking down a single source or idea to examine its parts, strengths, or weaknesses. It’s about looking closely at one piece of information.
  • Example: Analysing a study on exercise and mental health might involve critiquing its methods or interpreting its results in detail.
• Synthesis does not just connect ideas but constructs a new argument or framework from them. It’s about seeing how different pieces of information relate to each other.
  • Example: Synthesising multiple studies on exercise and mental health might reveal patterns, like how different types of exercise affect different aspects of mental health.

Why Is Synthesis in Research Important?

Because it helps you:

• See Connections: By combining insights, you can understand a topic more fully.
• Find Gaps: It helps you spot what’s missing in the existing research.
• Build Stronger Arguments: When you connect ideas from multiple sources, your conclusions become more convincing.
• Make Research Useful: In fields like policy or healthcare, synthesis helps turn research into practical solutions.

Without synthesis, research can feel scattered or repetitive. It’s what ties everything together.

How to Do Synthesis in Research: A Step-by-Step Guide

Here’s a simple way to approach synthesis in research:

1. Start with a Clear Question

Before diving into your sources, ask yourself: What am I trying to learn or answer? A clear question will help you stay focused and avoid getting overwhelmed.

2. Gather Your Sources

Look for reliable, relevant studies, articles, and data. Use tools like Zendy to find the latest articles in one place and EndNote to keep everything organised.

3. Look for Patterns and Connections

As you read, take notes on common themes, differences, or trends. For example, do multiple studies point to the same conclusion? Are there conflicting results? A table or chart can help you visualise these patterns.

4. Bring It All Together

Combine the insights you’ve gathered into a cohesive narrative. Explain how the ideas relate to each other and to your research question.

5. Draw Your Conclusions

Summarise what you’ve learned and explain why it matters. Be honest about any limitations and suggest areas for further study.

How can you improve your synthesis skills? 

• Read your sources multiple times.
• Make well-organised notes on each one. 
• Find relevant ideas and evidence for them.
• Rearrange your notes based on concepts.
• Put ideas into an outline.

Conclusion

Synthesis in research is more than just combining information—it’s about creating new knowledge and understanding. By following the steps above, you can effectively bring together ideas, spot patterns, and produce work that’s clear, insightful, and useful. Whether you’re a student, academic, or professional, synthesis can help you take your research to the next level.

Struggling to make sense of your research? AI tools can assist with synthesis by identifying key themes and connections across multiple papers, check out our AI tools to simplify the process and get better results. Let’s make your research work for you!

AI tools have made handling complex tasks easier, but not all of them are created equal. While DeepSeek is widely known and accessible, it is a general-purpose AI tool, available to the public and easily understood by many. In contrast, ZAIA, an AI assistant for researchers developed by Zendy, takes a more specialised approach. Designed specifically for researchers and academics, ZAIA easily integrates into academic workflows with customised tools. Let’s take a closer look at how these two AI tools compare.

What is DeepSeek

DeepSeek AI is a Chinese conversational AI chat created by Liang Wenfeng, mostly used as a general-purpose AI tool that can assist with writing, brainstorming, coding, and more. It’s a Swiss Army knife, good for a lot of things but not customised to a specific need. This makes it a great option for users who need an all-in-one AI assistant rather than a highly specialised tool.

What is ZAIA

ZAIA, Zendy’s AI assistant, is built with researchers in mind. You can ask any research-related question, and ZAIA will analyse millions of academic papers to provide credible, reference-backed answers. In addition, It’s integrated into Zendy to give you access to over 40 million academic research papers and offers AI tools that make chatting, summarising, analysing, and extracting insights from research papers much simpler. 

ZAIA and DeepSeek Data & Knowledge Sources

One of the key differences between ZAIA and DeepSeek is how they access and process information.

ZAIA is designed by researchers for researchers, providing direct access to a vast collection of 40 million scholarly publications through Zendy’s massive digital library. This means researchers retrieve precise insights for their research, summarise it, and chat with it, all from credible, peer-reviewed sources. 

DeepSeek, on the other hand, is trained on a broad dataset covering various topics but lacks integration with academic databases like Zendy, PubMed or even SpringerLink,  and cannot verify sources with citations. While it can generate general information, it lacks the ability to pull insights directly from academic literature, with a high possibility of predatory journals in the outputs. 

So, what does DeepSeek do better? As we mentioned above, it helps you in answering questions, brainstorming ideas, helping with writing, summarising information, and providing explanations on complex topics in a clear, understandable way.

A Smarter, Lower Energy Consumption Choice

Since AI became available to the public, concerns about energy consumption have begun to rise. For instance, DeepSeek-V3 required 2,788,000 GPU hours for training, equivalent to 836,400 kWh of energy. According to NDTV News, this consumes less energy compared to the current leading U.S. AI model, ChatGPT.

According to RW Digital, the annual energy consumption for ChatGPT is expected to hit 226.8 GWh. To give you an idea of how much energy that is:

• It could fully charge 3.13 million electric vehicles, which is nearly 95% of all electric vehicles in the United States.
• It could power around 21,602 U.S. homes for a whole year.
• It would be enough to run the entire countries of Finland or Belgium for one day.

On the other hand, ZAIA was developed to minimise energy usage even more, consuming as little as several kilowatts (kW) per server in data centres

ZAIA’s focus on academic tasks means it requires fewer resources. This makes it an environmentally friendly choice for researchers who value sustainability.

Who Should Use Which?

If you’re looking for a general-purpose assistant to handle a variety of tasks, DeepSeek is a good option.

But if you’re a student, researcher, or academic professional who works with research papers regularly, ZAIA is a better fit. It’s built to simplify the research process and provide you with the necessary AI tools and access to your academic needs, integrating AI in scientific research for enhanced productivity and quicker discoveries.

Conclusion

DeepSeek is a great all-around tool, but ZAIA’s focus on the academic field makes it the smarter choice for researchers. It’s affordable, consumes less energy and time, and is designed to make your academic journey smoother. That’s why, when it comes to AI tools, personalisation is the key.