AI for Students and Researchers: 2025 Trends and Statistics
Mar 10, 2025 |
20 Mins ReadWith 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 research 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 research, 51% use it for literature review and 46.3% of students and researchers are using AI in research for writing and editing, showing just how quickly these tools are being adopted.
AI in research 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 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 research. 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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Research Habits and AI 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 research 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 research 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 Research
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 research 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 Research
AI in research 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 research, 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 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 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 research, helping students and researchers work more efficiently. From automating literature reviews to improving writing and editing, it’s clear that AI in research 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 research, and discover how it’s shaping the academic world.
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How to Find Peer-Reviewed Articles for Your Research
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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Impact Factor of Journals: What You Need To Know
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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Best AI Tool for Literature Review: 5 Top Picks for 2025
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 tool for literature review in 2025. Why Use AI for Literature Reviews? Before we dig into the list of the best AI tool for literature review, 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 2025 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 tool for literature review, 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: Summarise Key Points: Summarisation by AI condenses long written materials to easily readable insights. Rewrite and Paraphrase: AI is also useful in manuscript improvements to guarantee clarity and consistency while maintaining professionalism in academia. Ensure Proper Citations: AI citation tools help with reference management and formatting. 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 BibliographyLiterature ReviewPurposeSummarises and evaluates each source individuallySynthesises and analyses sources collectivelyStructureOrganised as a list of citations with annotationsOrganised thematically or methodologicallyDepth of AnalysisFocuses on each source’s contributionIdentifies patterns, gaps, and trends in researchUse in ResearchOften used as a preparatory step for literature reviewsUsed as a foundation for research projects or thesesWriting StyleConcise, source-focusedIntegrative, argument-driven Is It Ethical to Use AI for Literature Reviews? 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 AI tools for literature review are making literature 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 looking for the Best AI tool for literature review 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. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }