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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.

My Top AI Tools for Researchers and Academics – Tested and Reviewed!

There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.

These are my recommendations that’ll cover almost everything that you’ll want to do:

Want to find out all of the tools that you could use?

Here they are, below:

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Litmaps –  https://www.litmaps.com
• Research rabbit – https://www.researchrabbit.ai/
• Connected Papers –  https://www.connectedpapers.com/
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Laser AI –  https://laser.ai/
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Consensus –  https://consensus.app/
• Iris AI –  https://iris.ai/
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Aetherbrain – https://aetherbrain.ai
• Explain Paper – https://www.explainpaper.com
• Chat PDF – https://www.chatpdf.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/
• Open Read –  https://www.openread.academy

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
• Yomu – https://www.yomu.ai
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• PaperPal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Best free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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AI Search Engine for Research

Consensus is a search engine that uses AI to find insights in research papers

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Why Consensus?

Consensus responsibly uses AI to help you conduct effective research

Search through over 200 million scientific papers without having to keyword match

All of our results are tied to actual studies, we cite our sources and we will never show you ads

Proprietary and purpose-built features that leverage GPT4 and other LLMs to summarize results for you

Used by researchers at the world’s top institutions

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AI Literature Review Generator

Generate high-quality literature reviews fast with ai.

• Academic Research: Create a literature review for your thesis, dissertation, or research paper.
• Professional Research: Conduct a literature review for a project, report, or proposal at work.
• Content Creation: Write a literature review for a blog post, article, or book.
• Personal Research: Conduct a literature review to deepen your understanding of a topic of interest.

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Analyze research papers at superhuman speed

Search for research papers, get one sentence abstract summaries, select relevant papers and search for more like them, extract details from papers into an organized table.

Find themes and concepts across many papers

Don't just take our word for it.

Tons of features to speed up your research

Upload your own pdfs, orient with a quick summary, view sources for every answer, ask questions to papers, research for the machine intelligence age, pick a plan that's right for you, get in touch, enterprise and institutions, custom pricing, common questions. great answers., how do researchers use elicit.

Over 2 million researchers have used Elicit. Researchers commonly use Elicit to:

• Speed up literature review
• Find papers they couldn’t find elsewhere
• Automate systematic reviews and meta-analyses
• Learn about a new domain

Elicit tends to work best for empirical domains that involve experiments and concrete results. This type of research is common in biomedicine and machine learning.

What is Elicit not a good fit for?

Elicit does not currently answer questions or surface information that is not written about in an academic paper. It tends to work less well for identifying facts (e.g. “How many cars were sold in Malaysia last year?”) and theoretical or non-empirical domains.

What types of data can Elicit search over?

Elicit searches across 125 million academic papers from the Semantic Scholar corpus, which covers all academic disciplines. When you extract data from papers in Elicit, Elicit will use the full text if available or the abstract if not.

How accurate are the answers in Elicit?

A good rule of thumb is to assume that around 90% of the information you see in Elicit is accurate. While we do our best to increase accuracy without skyrocketing costs, it’s very important for you to check the work in Elicit closely. We try to make this easier for you by identifying all of the sources for information generated with language models.

What is Elicit Plus?

Elicit Plus is Elicit's subscription offering, which comes with a set of features, as well as monthly credits. On Elicit Plus, you may use up to 12,000 credits a month. Unused monthly credits do not carry forward into the next month. Plus subscriptions auto-renew every month.

What are credits?

Elicit uses a credit system to pay for the costs of running our app. When you run workflows and add columns to tables it will cost you credits. When you sign up you get 5,000 credits to use. Once those run out, you'll need to subscribe to Elicit Plus to get more. Credits are non-transferable.

How can you get in contact with the team?

Please email us at [email protected] or post in our Slack community if you have feedback or general comments! We log and incorporate all user comments. If you have a problem, please email [email protected] and we will try to help you as soon as possible.

What happens to papers uploaded to Elicit?

When you upload papers to analyze in Elicit, those papers will remain private to you and will not be shared with anyone else.

How accurate is Elicit?

Training our models on specific tasks, searching over academic papers, making it easy to double-check answers, save time, think more. try elicit for free..

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AI-Based Literature Review Tools

• Dialogues: Insightful Facts
• How to Craft Prompts
• Plugins / Extensions for AI-powered Searches
• Cite ChatGPT in APA / MLA
• AI and Plagiarism
• ChatGPT & Higher Education
• Author Profile

Selected AI-Based Literature Review Tools

Updates: See news or release of AI (Beta) across various academic research databases including Web of Science , Scopus , Ebsco , ProQues t, OVID , Dimensions , JStor , Westlaw , and LexisNexis . ********* ********** ********** ********** **********   

Disclaimer: TAMU libraries do not have subscription access to the AI-powered tools listed below the divider line. The guide serves solely as an informational resource. It is recommended that you assess these tools and their usage methodologies independently. ------------------------------------------------------------------------------------------------------------------------------------------------------------- SEMANTIC SCHOLAR

• SCIENTIFIC LITERATURE SEARCH ENGINE - finding semantically similar research papers.
• " A free, AI-powered research tool for scientific literature."  <https://www.semanticscholar.org/>. But login is required in order to use all functions.
• Over 200 millions of papers from all fields of science, the data of which has also served as a wellspring for the development of other AI-driven tools.

The 4000+ results can be sorted by Fields of Study, Date Range, Author, Journals & Conferences

Save the papers in your Library folder. The Research Feeds will recommend similar papers based on the items saved.

Example - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality Total Citations: 22,438   [Note: these numbers were gathered when this guide was created] Highly Influential Citations 2,001 Background Citations 6,109 Methods Citations 3,273  Results Citations 385

TLDRs (Too Long; Didn't Read) Try this example . Press the pen icon to reveal the highlighted key points . TLDRs "are super-short summaries of the main objective and results of a scientific paper generated using expert background knowledge and the latest GPT-3 style NLP techniques. This new feature is available in beta for nearly 60 million papers in computer science, biology, and medicine..." < https://www.semanticscholar.org/product/tldr>

• https://www.openread.academy/
• Institutionally accessed by Harvard, MIT, University of Oxford, Johns Hopkins, Standford, Beijing University. .
• AI-powered Academic Searching + Web Searching - Over 300 million papers and real-time web content.
• Every keyword search or AI quest will yield a synthesis report with citations. If you want to re-orient the search outcomes, just click on the Re-generate button and all citations will be refreshed accordingly. After that click on Follow-Up Questions to delve deeper into a particular area or subject.
• Use Paper Q&A to interact with a text directly, e.g. " What does this paper say about literature review ?"
• Click on the Translation to put a text or search results into another language.
• Upload a PDF document and let Paper Expresso to read it for you and parse the content into an academic report format for easy screening: Background and context> Research objectives and hypotheses> Methodology> Results and findings> Discussion and interpretation> Contributions to the field > Structure and flow> Achievements and significance> Limitations and future work>
• AI-POWERED RESEARCH ASSISTANT - finding papers, filtering study types, automating research flow, brainstorming, summarizing and more.
• " Elicit is a research assistant using language models like GPT-3 to automate parts of researchers’ workflows. Currently, the main workflow in Elicit is Literature Review. If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table."   <https://elicit.org/faq#what-is-elicit.>; Find answers from 175 million papers. FAQS
• Example - How do mental health interventions vary by age group?    /   Fish oil and depression Results: [Login required] (1) Summary of top 4 papers > Paper #1 - #4 with Title, abstract, citations, DOI, and pdf (2) Table view: Abstract / Interventions / Outcomes measured / Number of participants (3) Relevant studies and citations. (4) Click on Search for Paper Information to find - Metadata about Sources ( SJR etc.) >Population ( age etc.) >Intervention ( duration etc.) > Results ( outcome, limitations etc.) and > Methodology (detailed study design etc.) (5) Export as BIB or CSV
• How to Search / Extract Data / List of Concept Search -Enter a research question >Workflow: Searching > Summarizing 8 papers> A summary of 4 top papers > Final answers. Each result will show its citation counts, DOI, and a full-text link to Semantic Scholar website for more information such as background citations, methods citation, related papers and more. - List of Concepts search - e.g. adult learning motivation . The results will present a list the related concepts. - Extract data from a pdf file - Upload a paper and let Elicit extract data for you.
• Export Results - Various ways to export results.
• How to Cite - Includes the elicit.org URL in the citation, for example: Ought; Elicit: The AI Research Assistant; https://elicit.org; accessed xxxx/xx/xx

CONSENSUS.APP

ACADEMIC SEARCH ENGINE- using AI to find insights in research papers.

"We are a search engine that is designed to accept research questions, find relevant answers within research papers, and synthesize the results using the same language model technology." <https://consensus.app/home/blog/maximize-your-consensus-experience-with-these-best-practices/>

• Example - Does the death penalty reduce the crime?   /  Fish oil and depression  /    (1) Extracted & aggregated findings from relevant papers. (2) Results may include AIMS, DESIGN, PARTICIPANTS, FINDINGS or other methodological or report components. (3) Summaries and Full Text
• How to Search Direct questions - Does the death penalty reduce the crime? Relationship between two concepts - Fish oil and depression / Does X cause Y? Open-ended concepts - effects of immigration on local economics Tips and search examples from Consensus' Best Practice   
• Synthesize (beta) / Consensus Meter When the AI recognizes certain types of research questions, this functionality may be activated. It will examine a selection of some studies and provide a summary along with a Consensus Meter illustrating their collective agreement. Try this search: Is white rice linked to diabetes? The Consensus Meter reveals the following outcomes after analyzing 10 papers: 70% indicate a positive association, 20% suggest a possible connection, and 10% indicate no link.

Prompt “ write me a paragraph about the impact of climate change on GDP with citations “

CITATIONS IN CONTEXT

Integrated with Research Solutions.

Over 1.2 billion Citation Statements and metadata from over 181 million papers suggested reference.

How does it work? - "scite uses access to full-text articles and its deep learning model to tell you, for a given publication: - how many times it was cited by others - how it was cited by others by displaying the text where the citation happened from each citing paper - whether each citation offers supporting or contrasting evidence of the cited claims in the publication of interest, or simply mention it."   <https://help.scite.ai/en-us/article/what-is-scite-1widqmr/>

EXAMPLE of seeing all citations and citation statements in one place

More information: Scite: A smart citation index that displays the context of citations and classifies their intent using deep learning  

• GPT3.5 by OpenAI. Knowledge cutoff date is September 2021.
• Input/ Output length - ChatGPT-3.5 allows a maximum token limit of 4096 tokens. According to ChatGPT " On average, a token in English is roughly equivalent to 4 bytes or characters. English words are typically around 5 characters long. This means that, very roughly, you could fit around 800 to 1000 English words within 4096 tokens." 
• According to ChatGPT, the generated responses are non-deterministic by default. So if you run the searches again and get slightly or very different results, it's likely due to this factor.
• ChatGPT may find non-existent references.
• According to this study < https://arxiv.org/ftp/arxiv/papers/2304/2304.06794.pdf > "ChatGPT cites the most-cited articles and journals, relying solely on Google Scholar's citation counts" within the field of environmental science.  
• Example - "INTERVIEW WITH CHATGPT" as a Research Method & Teaching Tool Some researchers began to use this approach to obtain their research data. Try this Google Scholar search link "interview with ChatGPT"   or see two articles below: (1) Chatting about ChatGPT: how may AI and GPT impact academia and libraries? BD Lund, T Wang - Library Hi Tech News, 2023 (2) An interview with ChatGPT: discussing artificial intelligence in teaching, research, and practice , G Scaringi, M Loche - 2023  

Increased risk of sudden infant death syndrome (SIDS) Increased risk of premature birth Increased risk of low birth weight Increased risk of respiratory problems in newborns Increased risk of respiratory problems in infants exposed to secondhand smoke Increased risk of developing asthma and other respiratory illnesses later in life for infants exposed to secondhand smoke [Note : ChatGPT may generate non-existent references or false knowledge. To find out why Large Language Models hallucinate, check out this Wiki article: Hallucination (artificial intelligence) and this blog post -   A Gentle Introduction to Hallucinations in Large Language Models by Adrian Tam ]

Infant death Neonatal mortality (referring specifically to deaths within the first 28 days of life) Perinatal mortality (referring to deaths occurring during the period from 22 completed weeks of gestation to 7 completed days after birth) Early childhood mortality (referring to deaths occurring within the first five years of life) Child mortality (referring to deaths occurring before the age of 18) [Press the Regenerate button to get more.]

• Example -  RELATED WORDS What are the related words of infant mortality? Neonatal mortality, Post-neonatal mortality, Stillbirths, Low birth weight, Malnutrition, Infectious diseases, Vaccination, Maternal health,  Access to healthcare, Poverty, Social inequality, Sanitation, Hygiene, Water quality, Childbirth complications, Congenital abnormalities, Birth defects, Maternal age, Under-five mortality, Child mortality, Perinatal mortality, Preterm birth, Low birth weight, Sudden infant death syndrome (SIDS), Maternal mortality, Postnatal care, Malnutrition, Immunization, Access to healthcare, Clean water and sanitation, Congenital anomalies, Infant health disparities, Infant mortality rate (IMR), Infant survival.  

(("Infant Death"[Mesh] OR "Sudden Infant Death Syndrome"[Mesh] OR "Infant Mortality"[Mesh] OR "Perinatal Mortality"[Mesh] OR "Neonatal Mortality"[Mesh] OR "Fetal Mortality"[Mesh] OR "Infant Death" OR "Sudden Infant Death Syndrome" OR "Infant Mortality" OR "Perinatal Mortality" OR "Neonatal Mortality" OR "Fetal Mortality") AND ("Risk Factors"[Mesh] OR "Etiology"[Mesh] OR "Prevention and Control"[Mesh] OR "Interventions"[Mesh] OR "Public Health"[Mesh] OR "Health Disparities"[Mesh] OR "Healthcare Disparities"[Mesh] OR "Poverty"[Mesh] OR "Malnutrition"[Mesh] OR "Infection"[Mesh] OR "Congenital Abnormalities"[Mesh] OR "Prematurity"[Mesh] OR "Low Birth Weight"[Mesh] OR "Maternal Education"[Mesh] OR "Maternal Health"[Mesh] OR "Risk Factors" OR "Etiology" OR "Prevention and Control" OR "Interventions" OR "Public Health" OR "Health Disparities" OR "Healthcare Disparities" OR "Poverty" OR "Malnutrition" OR "Infection" OR "Congenital Abnormalities" OR "Prematurity" OR "Low Birth Weight" OR "Maternal Education" OR "Maternal Health")) Comments and Suggestions: #1 -This search string may not work on all database platforms. For example, Medline (OVID) may not be able to run it, Medline & CINAHL (EBSCO) can run it without any problems while PubMed will run it but it will indicate which MeSH terms are not indexed. See PubMed's search History and Details under Advanced . #2 - This is just one possible combination. You may need to adjust it based on your task and the databases you are using . #3 - It might be easier if you break the task into 3 parts: ask for synonyms > ask for thesauri terms > and ask it to combine them.

Examples - DATABASE RECOMMENDATIONS Heath Science - Which databases to use to find articles on the EFFECTS OF MATERNAL SMOKING ON INFANT DEATH? PubMed: This is a database of biomedical literature from the National Library of Medicine. It includes articles from medical journals and other sources on a wide range of health-related topics. Embase: This is a database of biomedical literature that covers more international and pharmaceutical content than PubMed. It includes articles from medical journals and other sources on a wide range of health-related topics. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions, including interventions related to maternal smoking and infant death. Scopus: This is a multidisciplinary database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. Web of Science: This is a database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. CINAHL: This is a database of nursing and allied health literature, including articles on maternal smoking and infant death. [Note: The response above was copied and pasted directly from ChatGPT. No editing was added.] Education -- I am doing a meta-analysis on k-12 STEM education. What databases should I use? ERIC : This is a database of education research and information, including articles from journals, reports, and conference papers. It is a commonly used database for educational research. Education Source : This is a comprehensive database of education research and information, including articles from journals, books, and conference papers. Web of Science: This is a multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. Scopus: This is another multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. PsycINFO: This is a database of psychology research, including articles from journals, books, and dissertations. It can be useful for research on STEM education that includes psychological factors. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions including interventions related to education. [Author's note: You could also ask for the GREY LITERATURE sources related to the topic above,]

Example: ACT-AS-AN-EXPERT PROMPT Here's an alternative way to consolidate all the questions into a single consultation by using this popular Act-As-An-Expert prompt, aka, system message prompt:  I am going to conduct a systematic review on games and mathematics related to K-12 education  I want you to act as a systematic review expert . I will ask you some questions related to my research topic and you advise me on how to do it. 

[You may ask one question at a time] My first question is "which databases should I use?"  My second question is "what are the relevant subject terms, descriptors and key words?" My third question is "how to do data extraction?" ..... , .... Please note that the above prompts are merely for illustrative purposes. Actual questions may contain more specific instructions. As an example, for the first question, could you provide guidance on how to identify relevant publications? Specifically, can you provide a list of the primary databases and grey literature sources related to my topic?  

Example - GREY LITERATURE Please show me all the resources that offer grey literature for conducting research in the field of education     ERIC (Education Resources Information Center): https://eric.ed.gov/     OpenGrey: http://www.opengrey.eu/     British Education Index: https://www.ebscohost.com/academic/british-education-index     WorldCat: https://www.worldcat.org/     ProQuest Dissertations & Theses Global: https://www.proquest.com/products-services/pqdtglobal.html     EThOS (Electronic Theses Online Service): https://ethos.bl.uk/     Institutional Repositories of universities and academic institutions (search for specific institution repositories)     American Educational Research Association (AERA): https://www.aera.net/     National Education Association (NEA): https://www.nea.org/     Association for Supervision and Curriculum Development (ASCD): http://www.ascd.org/     RAND Corporation: https://www.rand.org/     National Center for Education Research (NCER): https://ies.ed.gov/ncer/     National Center for Education Statistics (NCES): https://nces.ed.gov/     European Educational Research Association (EERA): https://www.eera-ecer.de/     UNESCO International Institute for Educational Planning (IIEP): http://www.iiep.unesco.org/     Organization for Economic Cooperation and Development (OECD): https://www.oecd.org/education/     Education Development Center (EDC): https://www.edc.org/     National Institute for Educational Policy Research (NIER) - Japan: https://www.nier.go.jp/     Australian Council for Educational Research (ACER): https://www.acer.org/     Canadian Education Association (CEA): https://www.cea-ace.ca/ My topic is about stem education. Which grey literature databases should I use?    STEM Education Research Centre (SERC) Grey Literature Database    National STEM Education Research Repository (NSERR)    National Science Digital Library (NSDL)    STEM Education Resource Center (SERC) GrayLit Network    STEM Education Grey Literature Library

See other Comparison Examples under Bing AI.

Examples - ZERO SHOT, ONE SHOT & FEW SHOT PROMPTING for literature review See the examples on the left panel- How to Fine Tune Prompts

Bing Chat / Copilot

• Bing Chat has become Copilot.
• CONVERSATIONAL LLM 4.0 + SEARCH ENGINE. Connects to the Internet.
• Three conversation modes: More Creative, More Balanced or More Precise.
• Comparison - Prompt: My topic is about psychology. Which grey literature databases should I use? More Precise - PsycExtra, NIH Library and links to library guides, .gov and .org websites More Balanced - PsycExtra and three main research guides that mention psychology grey literature sources. More Creative- PsycExtra, LILACS, and Worldcat + definition of grey literature + suggestions on searching for professional association websites, newsletters, magazines, standards and guidelines, and other sources that may contain relevant information for your topic.
• Some examples below were tested in Bing Chat only.  
• Example - factors of infant mortality Infant mortality is a term that refers to the death of infants before their first birthday. The leading causes of infant mortality include birth asphyxia, pneumonia, congenital malformations, term birth complications such as abnormal presentation of the fetus umbilical cord prolapse, or prolonged labor, neonatal infection, diarrhea, malaria, measles, and malnutrition1. Many situational factors lead to infant mortality such as the pregnant woman’s level of education, environmental conditions, political infrastructure, and level of medical support1. In the United States, a primary determinant of infant mortality risk is infant birth weight with lower birth weights increasing the risk of infant mortality. The determinants of low birth weight include socioeconomic, psychological, behavioral and environmental factors2.  
• Example: PEER REVIEWED SOURCES I need to find some peer reviewed articles about infant mortality? The answer includes 5 articles. The first one is “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” by Neha Bairoliya and Günther Fink1. A click on  " Tell me more about the first article " leads to this summary: " The first article is titled “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” and was published in PLOS Medicine on March 20, 2018. The authors are Neha Bairoliya and Günther Fink. The study used linked birth and death records for the period 2010–2012 to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation) in the United States. The researchers found that the full-term infant mortality rate (FTIMR) was 2.2 per 1,000 live births overall and ranged between 1.29 (Connecticut) and 3.77 (Mississippi) at the state level. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) and suffocation." More searches: Please find five empirical studies focusing on the factors influencing adult learners engaged in online education. Please locate five scholarly articles on stress management among college students site:pubmed.ncbi.nlm.nih.gov

The ERIC Thesaurus contains nearly 12,000 terms, including more than 4,500 descriptors and 7,000 synonyms1. You can use the term “Academic Achievement” or its synonyms such as “Educational Achievement”, “Student Achievement”, “Academic Performance”, “Academic Success”, “Learning Achievement” and more. [ Note : The follow-up responses include some additional sources. See two below: https://www.powerthesaurus.org/academic_achievement https://eric.ed.gov/?ti=Achievement+Gap   including https://eric.ed.gov/?ti=Academic+Achievement |

My topic is about psychology.  Which grey literature databases should I use?

ChatGPT PsycEXTRA : This database is produced by the American Psychological Association (APA) and includes grey literature such as conference papers, white papers, and government reports. OpenGrey : This database provides access to grey literature produced in Europe, including reports, theses, and conference papers Grey Literature Report : This database is produced by The New York Academy of Medicine and includes reports, conference papers, and other grey literature on health and biomedical topics, including psychology. ProQuest Dissertations and Theses Global : This database provides access to graduate-level research, including theses and dissertations related to psychology. Google Scholar: While not exclusively a grey literature database, Google Scholar can be a useful tool for finding conference papers and other grey literature related to psychology. National Technical Information Service (NTIS) : This database provides access to technical reports produced by U.S. government agencies, including those related to psychology. Bing AI   - [asked in the Precise mode] For psychology, some recommended grey literature databases include PsycEXTRA and PsyArXiv . PsycEXTRA is a database of grey literature material relating to psychology, behavioral sciences and health. PsyArXiv is a psychology archive designed to facilitate rapid dissemination of psychological research. What other models have been developed based on SERVQUAL?

ChatGPT SERVPERF: This is a modified version of SERVQUAL that measures service performance instead of service quality. It was developed by Cronin and Taylor (1992) and is based on the same five dimensions as SERVQUAL. SERVPERF-GAP: This model, developed by Caruana and Ewing (2006), extends the SERVQUAL model to include three additional gaps that can occur when customers evaluate service quality. E-SERVQUAL: This model, developed by Parasuraman et al. (2005), adapts the SERVQUAL scale to measure the quality of online services. SERVCO: This is a modified version of SERVQUAL that includes a sixth dimension, "cost," in addition to the five original dimensions. It was developed by Sureshchandar et al. (2002) to address the importance of cost in service quality evaluation. SERVQ-HC: This is a modified version of SERVQUAL that is specifically designed for evaluating the quality of health care services. It was developed by Cronin et al. (2000) and includes 20 items across five dimensions. Bing AI The Nordic model of service quality2. The hierarchical model of service quality3. The American Customer Satisfaction Index (ACSI)4.

     Bing AI Sidebar - CHAT, THINK, & WRITE

• Examples of Using AI sidebar to Recap Google Scholar findings

Chat - If you highlight a study in the search results of Google Scholar, you will be prompted with a question by AI sidebar asking if you want to send the selected text to it. If you choose to do so, you can then select either  EXPLAIN or EXPAND to learn more about the chosen article. It's worth noting that EXPAND provides more detailed information. You can then click on any of the follow-up links to delve further into the selected article or topic. If the text of the chosen study is written in a foreign language, the AI sidebar can also provide an explanation of the text in English. Another option for extracting the key points from a selected article is to submit the URL of the PDF. Insights - Use the insights button to get more ideas about the topic. Compose - Tell the Edge sidebar your topic and let its Compose tool to generate a draft for you. Google Scholar Button- See also the Plugins and Extension page for how to perform a combined search using Google, Google Scholar, and ChatGPT .

Example: FINDING SIMILAR ARTICLES Find similar articles like this one - [then paste in the abstract ] Note - Use this prompt with Bing chat since real-time web info is required.

Examples: SUMMARIZE + REFERENCE Use a basic prompt like " Please summarize . ..." or add a REFERENCE TEXT that provides the necessary information about what you want it to focus or how the summary or resolution should look like. The reference could be a quote, an excerpt, a partial sentence, a conversation, an abstract, a code snippet and so on. Formula : Specify a role > assign a task > provide a reference text. Example#1 - Scholarly Journal Quote

STEP 1 Prompt: As a research assistant specializing in renewable energy, your task is to locate comparable research studies based on the provided abstract from an article: "This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy."

Bing Chat then found 3 documents: the ScienceDirect paper where the abstract came from and two other documents from un.org and frontiersin.org respectively.

STEP 2 Prompt: Please synthesize the findings of the three studies provided above.

Bing Chat: "Sure! Here is a synthesis of the findings from the three studies:

All three studies emphasize the importance of renewable energy in achieving sustainable development and mitigating climate change. They all discuss various strategies and actions that can be taken to increase the use of renewable energy sources and reduce reliance on fossil fuels.

Henrik Lund’s paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and .....

The United Nations outlines five critical actions the world needs to prioritize now to transform our energy systems and speed up the shift to renewable energy: Make renewable energy technology a global public good, Improve global access to components and raw materials......

“Sustainable Energy Transition for Renewable and Low Carbon Grid” discusses that a sustainable global electricity transition will entail increased use of renewable energy sources particularly wind and solar, nuclear energy as a low carbon energy source, electrification of transport and.......

In summary , these studies suggest that transitioning to renewable energy sources is crucial for sustainable development and mitigating climate change. This can be achieved through various strategies such as increasing energy efficiency, improving access to renewable technology and materials, leveling the playing field for renewables, ending fossil fuel subsidies, and redirecting investments towards renewables." Example #2 Y ou are an expert researcher, given the following article, please discuss its key findings. Reference: How Large Language Models are Transforming Machine-Paraphrased Plagiarism at https://arxiv.org/abs/2210.03568 This is another way to put REFERENCE TEXT. As a side not, role specifying or the use of persona is important as it can impact the level of responses obtained. Just imagine if the designated role were " You are a high school student... ." Example #3 Please read this article and summarize it f or me - "Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making" and then find 5 similar studies and please also summarize each for me.

CONNECTED PAPERS

• RELATED STUDIES
• Uses visual graphs or other ways to show relevant studies. The database is connected to the Semantic Scholar Paper Corpus which has compiled hundreds of millions of published papers across many science and social science fields.
• See more details about how it works .  
• Example - SERVQUAL and then click on SELECT A PAPER TO BUILD THE GRAPH > The first paper was selected. Results: (1) Origin paper - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality + Connected papers with links to Connected Papers / PDF / DOI or Publisher's site / Semantic Scholar / Google Scholar. (2) Graph showing the origin paper + connected papers with links to the major sources . See above. (3) Links to Prior Works and Derivative Works See the detailed citations by Semantic Scholar on the origin SERVQUAL paper on the top of this page within Semantic Scholars.
• How to Search Search by work title. Enter some keywords about a topic.
• Download / Save Download your saved Items in Bib format.

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• SUMMARY & SYNTHESIS
• " Knowledge graph & natural language processing platform tailored for technology domain . <"https://www.paperdigest.org/> Areas covered: technology, biology/health, all sciences areas, business, humanities/ social sciences, patents and grants ...

• LITERATURE REVIEW - https://www.paperdigest.org/review/ Systematic Review - https://www.paperdigest.org/literature-review/
• SEARCH CONSOLE - https://www.paperdigest.org/search/ Conference Digest - NIPS conference papers ... Tech AI Tools: Literature Review  | Literature Search | Question Answering | Text Summarization Expert AI Tools: Org AI | Expert search | Executive Search, Reviewer Search, Patent Lawyer Search...

Daily paper digest / Conference papers digest / Best paper digest / Topic tracking. In Account enter the subject areas interested. Daily Digest will upload studies based on your interests.

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• CITATION-BASED MAPPING: SIMILAR / EARLY / LATER WORKS
• " 100s of millions of academic articles and covers more than 90%+ of materials that can be found in major databases used by academic institutions (such as Scopus, Web of Science, and others) ." See its FAQs page. Search algorithms were borrowed from NIH and Semantic Scholar.

The default “Untitled Collection” will collect your search histories, based on which Research Rabbit will send you recommendations for three types of related results: Similar Works / Earlier Works / Later Works, viewable in graph such as Network, Timeline, First Authors etc.

Zotero integration: importing and exporting between these two apps.

• Example - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality [Login required] Try it to see its Similar Works, Earlier Works and Later Works or other documents.
• Export Results - Findings can be exported in BibTxt, RIS or CSV format.

CITING GENERATIVE AI

• How to cite ChatGPT  [APA] - https://apastyle. apa.org/blog /how-to-cite-chatgpt  
• How to Cite Generative AI  [MLA]  https://style. mla.org /citing-generative-ai/
• Citation Guide - Citing ChatGPT and Other Generative AI (University of Queensland, Australia)
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• 10 September 2018
• Correction 05 October 2018

How AI technology can tame the scientific literature

• Andy Extance 0

Andy Extance is a freelance writer based in Exeter, UK.

You can also search for this author in PubMed   Google Scholar

Illustration by The Project Twins

When computer scientist Christian Berger’s team sought to get its project about self-driving vehicle algorithms on the road, it faced a daunting obstacle. The scientists, at the University of Gothenburg in Sweden, found an overwhelming number of papers on the topic — more than 10,000 — in a systematic literature review. Investigating them properly would have taken a year, Berger says.

Luckily, they had help: a literature-exploration tool powered by artificial intelligence (AI), called Iris.ai. Using a 300-to-500-word description of a researcher’s problem, or the URL of an existing paper, the Berlin-based service returns a map of thousands of matching documents, visually grouped by topic. The results, Berger says, provide “a quick and nevertheless precise overview of what should be relevant to a certain research question”.

Iris.ai is among a bevy of new AI-based search tools offering targeted navigation of the knowledge landscape. Such tools include the popular Semantic Scholar, developed by the Allen Institute for Artificial Intelligence in Seattle, Washington, and Microsoft Academic. Although each tool serves a specific niche, they all provide scientists with a different look at the scientific literature than do conventional tools such as PubMed and Google Scholar. Many are helping researchers to validate existing scientific hypotheses. And some, by revealing hidden connections between findings, can even suggest new hypotheses for guiding experiments.

Such tools provide “state-of-the-art information retrieval”, says Giovanni Colavizza, a research data scientist at the Alan Turing Institute in London, who studies full-text analysis of scholarly publications. Whereas conventional tools act largely as citation indices, AI-based ones can offer a more penetrating view of the literature, Colavizza says.

That said, these tools are often expensive, and limited by the fraction of the scientific literature they search. “They are not meant to give you an exhaustive search,” says Suzanne Fricke, an animal-health librarian at Washington State University in Pullman, who has written a resource review on Semantic Scholar ( S. Fricke J. Med. Lib. Assoc. 106 , 145–147; 2018 ). Some, for example, “are meant to get you quickly caught up on a topic, which is why they should be used in conjunction with other tools”. Berger echoes this sentiment: “Blindly using any research engine doesn’t answer every question automatically.”

Teaching science to machines

AI-based ‘speed-readers’ are useful because the scientific literature is so vast. By one estimate, new papers are published worldwide at a rate of 1 million each year — that’s one every 30 seconds. It is practically impossible for researchers to keep up, even in their own narrow disciplines. So, some seek to computationally tame the flood.

The algorithms powering such tools typically perform two functions — they extract scientific content and provide advanced services, such as filtering, ranking and grouping search results. Algorithms extracting scientific content often exploit natural language processing (NLP) techniques, which seek to interpret language as humans use it, Colavizza explains. Developers can use supervised machine learning, for example — which involves ‘tagging’ entities, such as a paper’s authors and references, in training sets to teach algorithms to identify and extract them.

To provide more-advanced services, algorithms often construct ‘knowledge graphs’ that detail relationships between the extracted entities and show them to users. For example, the AI could suggest that a drug and a protein are related if they’re mentioned in the same sentence. “The knowledge graph encodes this as an explicit relationship in a database, and not just in a sentence on a document, essentially making it machine readable,” Colavizza says.

Iris.ai takes a different approach, Colavizza notes, grouping documents into topics defined by the words they use. Iris.ai trawls the CORE collection, a searchable database of more than 134 million open-access papers, as well as journals to which the user’s library provides access. The tool blends three algorithms to create ‘document fingerprints’ that reflect word-usage frequencies, which are then used to rank papers according to relevance, says Iris.ai chief technology officer Viktor Botev.

The result is a map of related papers, but eventually the company plans to supplement those results by identifying hypotheses explored in each paper as well. It is also developing a parallel, blockchain-based effort called Project Aiur, which seeks to use AI to check every aspect of a research paper against other scientific documents, thus validating hypotheses.

Colavizza says that tools such as Iris.ai — free for basic queries, but costing upwards of €20,000 (US$23,000) a year for premium access, which allows more-nuanced searches — can accelerate researchers’ entry into new fields. “It facilitates initial exploration of the literature in a domain in which I’m marginally familiar,” he says.

Experts seeking deeper insights into their own specialities might consider free AI-powered tools such as Microsoft Academic or Semantic Scholar, Colavizza suggests. Another similar option is Dimensions, a tool whose basic use is free but which costs to search and analyse grant and patent data, as well as to access data using the programmable Dimensions Search Language. (Dimensions is created by technology firm Digital Science, operated by the Holtzbrinck Publishing Group, which also has a majority share in Nature ’s publisher.)

Semantic Scholar has a browser-based search bar that closely mimics engines such as Google. But it gives more information than Google Scholar to help experts to prioritize results, Colavizza says. That includes popularity metrics, topics such as data sets and methods, and the exact excerpt in which text is cited. “I was very surprised to find that they also capture indirect citations,” Colavizza adds — such as when a method or idea is so well established that researchers don’t refer to its origin.

Doug Raymond, Semantic Scholar’s general manager, says that one million people use the service each month. Semantic Scholar uses NLP to extract information while simultaneously building connections to determine whether information is relevant and reputable, Raymond says. It can identify non-obvious connections, such as methodologies in computer science that are relevant to computational biology, he adds, and it can help to identify unsolved problems or important hypotheses to validate or disprove. Currently, Semantic Scholar incorporates more than 40 million documents from computer and biomedical science, and its corpus is growing, says Raymond. “Ultimately, we’d like to incorporate all academic knowledge.”

For other tools, such as SourceData from the European Molecular Biology Organization (EMBO) in Heidelberg, Germany, experimental data are a more central concern. As chief editor of Molecular Systems Biology , an EMBO publication, Thomas Lemberger wants to make the data underlying figures easier to find and interrogate. SourceData therefore delves into figures and their captions to list biological objects involved in an experiment, such as small molecules, genes or organisms. It then allows researchers to query those relationships, identifying papers that address the question. For instance, searching, ‘Does insulin affect glucose?’ retrieves ten papers in which the “influence of insulin (molecule) on glucose (molecule) is measured”.

SourceData is at an early stage, Lemberger says, having generated a knowledge graph comprising 20,000 experiments that were manually curated during the editing process for roughly 1,000 articles. The online tool is currently limited to querying this data set, but Lemberger and his colleagues are training machine-learning algorithms on it. The SourceData team is also working on a modified neuroscience-focused version of the tool with an interdisciplinary neuroscience consortium led by neurobiologist Matthew Larkum at Humboldt University in Berlin. Elsewhere, IBM Watson Health in Cambridge, Massachusetts, announced in August that it will combine its AI with genomics data from Springer Nature to help oncologists to define treatments. ( Nature ’s news team is editorially independent of its publisher.)

Hypothetically useful

Among those embarking on hypothesis generation are the roughly 20 customers of Euretos, based in Utrecht, the Netherlands. Arie Baak, who co-founded Euretos, explains that the company sells tools to industry and academia, mainly for biomarker and drug-target discovery and validation, for prices he did not disclose.

Euretos uses NLP to interpret research papers, but this is secondary to the 200-plus biomedical-data repositories it integrates. To understand them, the tool relies on the many ‘ontologies’ — that is, structured keyword lists — that life scientists have created to define and connect concepts in their subject areas.

Baak demonstrates by searching for a signalling protein called CXCL13. Above the resulting publication list are categories such as ‘metabolites’ or ‘diseases’. The screen looks much like Google Scholar or PubMed at this stage, with an ordered list of results. But clicking on a category reveals extra dimensions. Selecting ‘genes’, for instance, pulls up a list of the genes associated with CXCL13, ranked by how many publications mention them; another click brings up diagrams illustrating connections between CXCL13 and other genes.

Researchers at the Leiden University Medical Centre (LUMC) in the Netherlands have shown that this approach can yield new hypotheses, identifying candidate diseases that existing drugs might treat. The team presented its results at the Semantic Web Applications and Tools for Health Care and Life Sciences meeting in Rome in December 2017. They have also used Euretos to identify gene-expression changes in a neurological disorder called spinocerebellar ataxia type 3 ( L. Toonen et al. Mol. Neurodegener. 13 , 31; 2018 ).

So, should researchers worry that AI-based hypothesis generation could put them out of a job? Not according to Colavizza. Hypothesis generation is a “very challenging ambition”, he says, and improvements initially will be incremental. The hypotheses suggested so far are therefore “mostly in the realm of the relatively unsurprising ones”, Colavizza says.

That will probably change, of course. But surprising or not, computer-generated hypotheses must still be tested. And that requires human researchers. “One should never believe an auto-generated hypothesis first-hand without investigating the underlying evidence,” warns LUMC researcher Kristina Hettne. “Even though these tools can assist in collecting the known evidence, experimental validation is a must.”

Nature 561 , 273-274 (2018)

doi: https://doi.org/10.1038/d41586-018-06617-5

Updates & Corrections

Correction 05 October 2018 : An earlier version of this Toolbox referred to the CORE repository by its old name, Connecting Repositories.

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• Who can use it? Our service is perfect for students, researchers, writers, and anyone else who needs to find reliable sources to support their work. It's a great tool for anyone looking to improve the credibility and quality of their writing.
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Welcome to Sourcely! Our AI-powered source finding tool is built by students for students, and this approach allows us to create a tool that truly understands the needs of the academic community. Our student perspective also enables us to stay up-to-date with the latest research and trends, and our collaborative approach ensures that our tool is continually improving and evolving.

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Using Litmaps for my research papers has significantly improved my workflow. Typically, I start with a single paper related to my topic. Whenever I find an interesting work, I add it to my search. From there, I can quickly cover my entire Related Work section.

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As a person who is an early researcher and identifies as dyslexic, I can say that having research articles laid out in the date vs cite graph format is much more approachable than looking at a standard database interface. I feel that the maps Litmaps offers lower the barrier of entry for researchers by giving them the connections between articles spaced out visually. This helps me orientate where a paper is in the history of a field. Thus, new researchers can look at one of Litmap's "seed maps" and have the same information as hours of digging through a database.

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Aaron Tay maintains a large, comprehensive list of literature mapping tools on his blog. Here are a few popular free, or freemium, ones:

• Research Rabbit Always free, non-profit citation chasing literature mapping and visualization tool. Searches a very large literature corpus covering >90% of academic databases, plus grey literature. Start with uploaded citations, or link to Zotero citation manager. Clear FAQ and help documentation .
• Connected Papers Searches the Semantic Scholar database. Allows you to create and explore a visual map of connected papers based on any single search result. Free tier allows limited number of maps per month.
• Inciteful Free literature mapper based on a seed paper, or pair of papers to explore connecting literature.
• Iris.ai Explore literature from open access research papers (from CORE UK and PubMed, >70 million items) on an AI generated map. Free tier requires you to supply an article URL, maps connections to it, and allows you to filter the map with concepts you supply.
• Litmaps Literature navigator and recommender with a free tier. Allows you to search and generates a map of relevant articles based on any one paper. Search results are drawn from CrossRef, OpenAlex, and Semantic Scholar (>200 million items). Allows both citation-based search and semantic similarity searching.
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• 5 Best AI Research Paper Summarizers (May 2024)

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Table Of Contents

In the fast-paced world of academic research, keeping up with the ever-growing body of literature can be a daunting task. Researchers and students often find themselves inundated with lengthy research papers, making it challenging to quickly grasp the core ideas and insights. AI-powered research paper summarizers have emerged as powerful tools, leveraging advanced algorithms to condense lengthy documents into concise and readable summaries.

In this article, we will explore the top AI research paper summarizers, each designed to streamline the process of understanding and synthesizing academic literature:

1. Tenorshare AI PDF Tool

Tenorshare AI PDF Tool is a cutting-edge solution that harnesses the power of artificial intelligence to simplify the process of summarizing research papers. With its user-friendly interface and advanced AI algorithms, this tool quickly analyzes and condenses lengthy papers into concise, readable summaries, allowing researchers to grasp the core ideas without having to read the entire document.

One of the standout features of Tenorshare AI PDF Tool is its interactive chat interface, powered by ChatGPT. This innovative functionality enables users to ask questions and retrieve specific information from the PDF document, making it easier to navigate and understand complex research papers. The tool also efficiently extracts critical sections and information, such as the abstract, methodology, results, and conclusions, streamlining the reading process and helping users focus on the most relevant parts of the document.

Key features of Tenorshare AI PDF Tool:

• AI-driven summarization that quickly condenses lengthy research papers
• Interactive chat interface powered by ChatGPT for retrieving specific information
• Automatic extraction of critical sections and information from the paper
• Batch processing capabilities for handling multiple PDF files simultaneously
• Secure and private, with SSL encryption and the option to delete uploaded files

Elicit is an AI-powered research assistant that improves the way users find and summarize academic papers. With its intelligent search capabilities and advanced natural language processing, Elicit helps researchers quickly identify the most relevant papers and understand their core ideas through automatically generated summaries.

By simply entering keywords, phrases, or questions, users can leverage Elicit's AI algorithms to search through its extensive database and retrieve the most pertinent papers. The tool offers various filters and sorting options, such as publication date, study types, and citation count, enabling users to refine their search results and find exactly what they need. One of Elicit's most impressive features is its ability to generate concise summaries of the top papers related to the search query, capturing the key findings and conclusions and saving researchers valuable time.

Key features of Elicit:

• Intelligent search that understands the context and meaning of search queries
• Filters and sorting options for refining search results
• Automatic summarization of the top papers related to the search query
• Detailed paper insights, including tested outcomes, participant information, and trustworthiness assessment
• Inline referencing for transparency and accuracy verification

3. QuillBot

QuillBot is an AI-powered writing platform that offers a comprehensive suite of tools to enhance and streamline the writing process, including a powerful Summarizer tool that is particularly useful for condensing research papers. By leveraging advanced natural language processing and machine learning algorithms, QuillBot's Summarizer quickly analyzes lengthy articles, research papers, or documents and generates concise summaries that capture the core ideas and key points.

One of the key advantages of QuillBot's Summarizer is its ability to perform extractive summarization, which involves identifying and extracting the most critical sentences and information from the research paper while maintaining the original context. Users can customize the summary length to be either short (key sentences) or long (paragraph format) based on their needs, and the output can be generated in either a bullet point list format or as a coherent paragraph. This flexibility allows researchers to tailor the summary to their specific requirements and preferences.

Key features of QuillBot's Summarizer:

• AI-powered extractive summarization that identifies and extracts key information
• Customizable summary length (short or long) to suit different needs
• Bullet point or paragraph output for flexible formatting
• Improved reading comprehension by condensing the paper into its core concepts
• Integration with other QuillBot tools, such as Paraphraser and Grammar Checker, for further enhancement

4. Semantic Scholar

Semantic Scholar is a free, AI-powered research tool developed by the Allen Institute for AI that improves the way researchers search for and discover scientific literature. By employing advanced natural language processing, machine learning, and machine vision techniques, Semantic Scholar provides a smarter and more efficient way to navigate the vast landscape of academic publications.

One of the standout features of Semantic Scholar is its ability to generate concise, one-sentence summaries of research papers, capturing the essence of the content and allowing researchers to quickly grasp the main ideas without reading lengthy abstracts. This feature is particularly useful when browsing on mobile devices or when time is limited. Additionally, Semantic Scholar highlights the most important and influential citations within a paper, helping researchers focus on the most relevant information and understand the impact of the research.

Key features of Semantic Scholar:

• Concise one-sentence summaries of research papers for quick comprehension
• Identification of the most influential citations within a paper
• Personalized paper recommendations through the “Research Feed” feature
• Semantic Reader for in-line citation cards with summaries and “skimming highlights”
• Personal library management with the ability to save and organize papers

5. IBM Watson Discovery

IBM Watson Discovery is a powerful AI-driven tool designed to analyze and summarize large volumes of unstructured data, including research papers, articles, and scientific publications. By harnessing the power of cognitive computing, natural language processing, and machine learning, Watson Discovery enables researchers to quickly find relevant information and gain valuable insights from complex documents.

One of the key strengths of IBM Watson Discovery is its ability to understand the context, concepts, and relationships within the text, allowing it to identify patterns, trends, and connections that may be overlooked by human readers. This makes it easier to navigate and summarize complex research papers, as the tool can highlight important entities, relationships, and topics within the document. Users can create customizable queries, filter, and categorize data to generate summaries of the most relevant research findings, and the tool's advanced search capabilities enable precise searches and retrieval of specific information from large document libraries.

Key features of IBM Watson Discovery:

• Cognitive capabilities that understand context, concepts, and relationships within the text
• Customizable queries and filtering for generating summaries of relevant research findings
• Relationship identification to highlight important entities, relationships, and topics
• Significant time-saving by automating the discovery of information and insight

Empowering Researchers with AI-Driven Summarization Tools

The emergence of AI-powered research summarizers has transformed the way researchers and academics approach scientific literature. By leveraging advanced natural language processing, machine learning, and cognitive computing, these innovative tools enable users to quickly find, understand, and summarize complex research papers, saving valuable time and effort.

Each of these AI research summarizers offers unique features and benefits that cater to researchers' diverse needs. As these tools continue to evolve and improve, they will undoubtedly play an increasingly crucial role in empowering researchers to navigate the ever-expanding universe of scientific knowledge more efficiently and effectively.

5 Best AI SOP (Standard Operating Procedures) Generators in 2024

Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.

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AI-assisted writing is quietly booming in academic journals—here's why that's OK

by Julian Koplin, The Conversation

If you search Google Scholar for the phrase " as an AI language model ," you'll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says,

"As an AI language model, I don't have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …"

Obvious gaffes like this aren't the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as "commendable," "meticulously" and "intricate"), and found they became far more common after the launch of ChatGPT—so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it's because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as " contaminating " scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between "plagiarizing" text authored by humans and text authored by AI. Those who plagiarize humans' work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone's autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a "mushroom" for my own purposes.

These kinds of overconfident mistakes have been referred to as "AI hallucinations" and " AI bullshit ." While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science—one of the world's most influential academic journals—disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT's own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words "commendable," "meticulously" and "intricate").

The second problem is that banning generative AI outright prevents us from realizing these technologies' benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

Provided by The Conversation

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Computer Science > Cryptography and Security

Title: large language models for cyber security: a systematic literature review.

Abstract: The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.

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This workshop serves as an introduction to artificial intelligence (AI) technology for writing high-quality articles. It will guide faculty members to write high-quality articles by integrating AI technology and thus gain the ability to prepare and perfect academic manuscripts using AI from the first draft stage to final publication.

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Some scientists can't stop using AI to write research papers

If you read about 'meticulous commendable intricacy' there's a chance a boffin had help.

Linguistic and statistical analyses of scientific articles suggest that generative AI may have been used to write an increasing amount of scientific literature.

Two academic papers assert that analyzing word choice in the corpus of science publications reveals an increasing usage of AI for writing research papers. One study , published in March by Andrew Gray of University College London in the UK, suggests at least one percent – 60,000 or more – of all papers published in 2023 were written at least partially by AI.

A second paper published in April by a Stanford University team in the US claims this figure might range between 6.3 and 17.5 percent, depending on the topic.

Both papers looked for certain words that large language models (LLMs) use habitually, such as “intricate,” “pivotal,” and “meticulously." By tracking the use of those words across scientific literature, and comparing this to words that aren't particularly favored by AI, the two studies say they can detect an increasing reliance on machine learning within the scientific publishing community.

In Gray's paper, the use of control words like "red," "conclusion," and "after" changed by a few percent from 2019 to 2023. The same was true of other certain adjectives and adverbs until 2023 (termed the post-LLM year by Gray).

In that year use of the words "meticulous," "commendable," and "intricate," rose by 59, 83, and 117 percent respectively, while their prevalence in scientific literature hardly changed between 2019 and 2022. The word with the single biggest increase in prevalence post-2022 was “meticulously”, up 137 percent.

The Stanford paper found similar phenomena, demonstrating a sudden increase for the words "realm," "showcasing," "intricate," and "pivotal." The former two were used about 80 percent more often than in 2021 and 2022, while the latter two were used around 120 and almost 160 percent more frequently respectively.

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The researchers also considered word usage statistics in various scientific disciplines. Computer science and electrical engineering were ahead of the pack when it came to using AI-preferred language, while mathematics, physics, and papers published by the journal Nature, only saw increases of between five and 7.5 percent.

The Stanford bods also noted that authors posting more preprints, working in more crowded fields, and writing shorter papers seem to use AI more frequently. Their paper suggests that a general lack of time and a need to write as much as possible encourages the use of LLMs, which can help increase output.

Potentially the next big controversy in the scientific community

Using AI to help in the research process isn't anything new, and lots of boffins are open about utilizing AI to tweak experiments to achieve better results. However, using AI to actually write abstracts and other chunks of papers is very different, because the general expectation is that scientific articles are written by actual humans, not robots, and at least a couple of publishers consider using LLMs to write papers to be scientific misconduct.

Using AI models can be very risky as they often produce inaccurate text, the very thing scientific literature is not supposed to do. AI models can even fabricate quotations and citations, an occurrence that infamously got two New York attorneys in trouble for citing cases ChatGPT had dreamed up.

"Authors who are using LLM-generated text must be pressured to disclose this or to think twice about whether doing so is appropriate in the first place, as a matter of basic research integrity," University College London’s Gray opined.

The Stanford researchers also raised similar concerns, writing that use of generative AI in scientific literature could create "risks to the security and independence of scientific practice." ®

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The four best AI writing apps worth a try

Suswati Basu is a multilingual, award-winning editor and the founder of the intersectional literature channel, How To Be Books. She was shortlisted for the Guardian…

AI tools like ChatGPT are revolutionizing writing, aiding in document creation and streamlining work processes.

Human oversight remains crucial to ensure accuracy and prevent AI-generated content from being too generic or erroneous.

Various AI writing apps, such as Copy.ai, Anyword, Writesonic, and Rytr, offer different features and pricing plans to cater to diverse needs.

Whatever the industry, more and more people are using artificial intelligence to assist with their work, especially since the launch of ChatGPT . Many workers and students now use the tool to write documents, essays, and more. It often speeds up the process of creating content and managing the mundane aspects of work. While they aren’t taking over the world yet, they are quickly evolving and have reached a stage where they can be useful, at least in the right circumstances.

Of course, all AI writing software requires some level of human oversight to achieve the best results. Without it, the software can generate content that is rather generic or even hallucinate facts , even though it may appear to be written by a human. As AI tools gain popularity, people are becoming more adept at recognizing the often bland nature of AI-generated content and are likely to either identify it or at least be suspicious of content that seems to lack a human touch.

How do AI writing tools work?

Predictive text on a smartphone or keyboard works in a similar way to AI. It essentially provides an “educated” guess as to what a user might say next based on their history of typing in a specific app. The difference is that it will rely on what other people have said as well, and find some level of commonality.

For example, if typing the sentence “The cat sat on the,” the model predicts the word “mat” based on the input sequence. It’s a simplistic illustration, but in reality, the model considers a vast number of possible continuations based on its training data and selects the most likely one.

Some large language models (LLMs) now use internet searches to gather information relevant to their writing prompts, which they then integrate into their responses. While earlier versions tended to produce stiff and robotic output, developments in algorithms and programming have allowed AI writers to produce more human-like responses. These days, most are using OpenAI’s ChatGPT models to power their tools.

Which is the best free AI app for writing?

ReadWrite decided to ask AI itself for a text prompt to assess how well it would fare:

Write a 250-word short story about a character who discovers a hidden world within their favorite childhood book. The story should include:

• A vivid description of the hidden world, including its unique features and inhabitants.
• The character’s emotional journey as they explore this new world and confront their own nostalgia.
• A surprising twist or revelation that changes their perspective on the book and their childhood.

Copy.ai ’s free trial uses ChatGPT-3.5, hence it is limited in terms of vocabulary and sophistication. Using the given prompt, it managed to create a story about a woman named Evelyn who discovered a magical library hidden in a garden.

While the story was quite pleasant to read, several noticeable AI traits appeared. For one, it used the main character’s name in every paragraph. Several words seem to be commonly used by ChatGPT, such as “nostalgia,” “abundance,” and “vibrant.” Combined with the repetitiveness, it seemed rather obvious that AI was used.

The pro version of Copy.ai uses ChatGPT-4, so it may produce better results. It’s unclear whether the app will incorporate new versions of the LLM, as OpenAI’s CEO Sam Altman revealed that ChatGPT-5 is on its way.

Copy.ai is also compatible with a variety of other AI models including Azure and Anthropic , which makes it more flexible for delivering results. Another piece of good news is that the company has a zero-retention data policy.

Like OpenAI, Copy.ai has a free version with limited words, while the Pro version starts from $36 per month for five users, and has unlimited capacity.

Anyword is a little bit more complicated to use as it doesn’t bring the user directly to the writing app. It offers a variety of writing options, including 1,000-word SEO-friendly blog articles, social media posts, and ad campaigns. If you just need a simple text prompt box, you can find it under “general prompt” on the dashboard.

However, it allows you to change the tone of voice from neutral to rebellious, or even to a multi-tonal style incorporating three dimensions. There is also the capability to target specific audiences, whether they are nostalgic readers or creative writers. When kept neutral with a broad audience, it produced a similar copy, characterized by the repetition of names and overly flowery language. Nevertheless, the story was creative but seemed to heavily favor the fantasy genre.

Using other filters, it generated a tale almost identical to the first, including the same phrases such as “The Enchanted Forest,” “newfound,” “intertwining,” and “grandmother.” It’s quite apparent that it has a defined notion of what constitutes a childhood book.

The Starter plan with one seat costs $39 per month paid annually. The Data-Driven plan costs $79/month, and the Business plan is $349 per month.

Writesonic is another free AI writing tool, with an interface comparable to ChatGPT as it has a dark left-hand side panel. Like Copy.ai, the unpaid version is powered by ChatGPT-3.5 among others, but has better billing options than OpenAI. The company states that it is “agnostic” about using a variety of LLMs.

“Our proprietary AI Model Gateway, called GPT Router dynamically routes between multiple AI models, thus speeding up responses and ensuring the best quality output as well as non-stop reliability,” it says on its website. These include Meta’s LLama and Google’s Gemini models.

The AI article writer can create long-form content pieces and includes a chatbot assistant named Chatsonic. When tested with a text prompt, it produced a slightly more imaginative story, making it more reliable than other tools.

Although there is a free version, Chatsonic costs $12 per month when paid annually and claims to offer output quality comparable to GPT-4 and Claude 3 Opus . The individual plan costs $16 and includes additional features such as an SEO optimizer, plagiarism checker, and a tool for facts and citations.

Rytr , with a name that phonetically resembles “writer,” adopts a no-nonsense approach by featuring a straightforward interface like that of a writing pad. That being said, it still allows users to select the tone and use case of the output. Using the “story plot” function, it produced a tale almost identical to one by Anyword.

Not only did “Amelia” have a copy of “The Enchanted Forest,” but she also showed a penchant for streams and creeks. Hence, it is obvious that Rytr also uses ChatGPT-3 for its content writing.

Rytr is free to use, with unlimited queries available for $7.50 per month. In comparison, ChatGPT-4 costs $20 per month currently. The difference is that Rytr offers other features such as a plagiarism checker and SEO toolkit.

Featured image: Canva

About ReadWrite’s Editorial Process

The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Editors assign relevant stories to staff writers or freelance contributors with expertise in each particular topic area. Before publication, articles go through a rigorous round of editing for accuracy, clarity, and to ensure adherence to ReadWrite's style guidelines.

Suswati Basu Tech journalist

Suswati Basu is a multilingual, award-winning editor and the founder of the intersectional literature channel, How To Be Books. She was shortlisted for the Guardian Mary Stott Prize and longlisted for the Guardian International Development Journalism Award. With 18 years of experience in the media industry, Suswati has held significant roles such as head of audience and deputy editor for NationalWorld news, digital editor for Channel 4 News and ITV News. She has also contributed to the Guardian and received training at the BBC As an audience, trends, and SEO specialist, she has participated in panel events alongside Google. Her…

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