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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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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 in 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.

How can you get in contact with the team?

You can email us at [email protected] or post in our Slack community ! We log and incorporate all user comments, and will do our best to reply to every inquiry 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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Top AI Tools for Literature Review 

As researchers, one of the most important steps in conducting a comprehensive study is to perform a literature review. Some of the common challenges researchers face include the overwhelming volume of literature, difficulty in identifying relevant studies, synthesizing findings, and keeping up with the latest research. Fortunately, AI tools for literature reviews are helping researchers overcome these challenges by providing efficient and effective ways to search, filter, and analyze literature.   

In this blog post, we’ll explore some of the top AI tools for literature review and how they can help streamline the research process.   

Table of Contents

Top five AI tools for literature review  

Conducting literature review is a long and arduous task where researchers have to closely go through vast information sources. However, with these five AI literature review tools, sifting large volumes of information easy.   

R Discovery  

R Discovery is one of the largest scholarly content repositories allowing you access to 250M+ research papers. R Discovery accelerates your research discovery journey, with latest and relevant content in your area of interest. Below are a few benefits of R Discovery tailored to students and researchers to enhance their research finding and reading experience:   

• Personalized Research Reading Feeds – R Discovery curates an academic reading library based on the user’s interests. It provides personalized reading recommendations with daily alerts on top papers, ensuring that users are always updated with the latest and most relevant research in their fields.
• Multiple reading lists – You can create and manage separate reading lists for different literature reviews you’re conducting, keeping your research organized.
• Multilingual & Full-Text Audio Features – R Discovery offers audio versions of research abstracts or full-text articles in more than 30+ languages. This feature is particularly beneficial for non-native English speakers, allowing them to listen to or read research papers in their preferred language, thus enhancing accessibility and comprehension  
• Smart Research Alerts – The app sends targeted research alerts and notifications to users based on their reading habits and preferences. Users receive no more than three research paper notifications per day to avoid information overload. The notifications include curated lists of top research papers, updates from preferred journals, and alerts on full-text articles available for reading.   
• Integration with Reference Managers – The platform offers auto-sync capabilities with reference managers like Zotero and Mendeley, ensuring that users’ libraries are up to date with the papers they save or remove  

Mendeley is a comprehensive reference management software that serves as an essential tool for researchers, academics, and students engaged in literature review and research management. Founded in 2007 by PhD students and acquired by Elsevier in 2013, Mendeley has evolved into a multifaceted platform that facilitates the organization, sharing, and discovery of research papers.      

Below are the detailed features of this AI tool for literature reviews:  

• Reference Management – Mendeley allows users to store, organize, and search all their references from a single library. This feature simplifies the process of managing a vast amount of literature, making it easier to access and cite these references in future research.
• Viewing and Annotation – Users can open PDFs directly within Mendeley’s PDF viewer, where they can add highlights and notes to the document. These annotations are stored in the user’s Mendeley account, and a new PDF file containing all annotations can be exported, facilitating collaborative review and personal notetaking.
• Collaboration and Sharing – Mendeley supports collaboration through the creation of private groups, allowing users to share references and annotated documents with co-workers or research team members. This feature enhances the collaborative aspect of literature review by enabling shared access to key resources.
• Literature Search and Discovery – While a dedicated literature search feature was removed from the latest version of Mendeley, the platform still offers capabilities for discovering relevant research. Users can import references from other sources and utilize Mendeley’s academic social network to find and share literature.
• Citation and Bibliography Generation – Mendeley simplifies the citation process with Mendeley Cite, an add-in for Microsoft Word that automates the generation of citations and bibliographies in various citation styles. This feature significantly reduces the time and effort required to accurately cite sources during a literature review.  

Zotero is an open-access, easy-to-use reference management tool designed to assist with the collection, organization, citation, and sharing of research sources. It serves as a personal research assistant for students, researchers, and academics, helping them manage their literature review process efficiently.    

• Reference Management – Zotero allows users to collect citations from various sources, including books, articles, media, webpages, and more. It provides a centralized library where users can organize these references into collections, tag them with keywords, and create saved searches that automatically populate with relevant materials.   
• PDF Viewing and Annotation – Zotero has a built-in PDF viewer that enables users to highlight text, add sticky notes, and take snapshots of images or charts within PDFs. Annotations made in the PDF viewer are saved within Zotero and can be extracted as notes, making them fully searchable and easier to organize.  
• Collaboration and Sharing – Zotero’s Groups feature allows users to collaborate with other Zotero users through shared libraries. Users can create private, public closed, or public open groups to work on collaborative research projects, distribute course materials, or build collaborative bibliographies  
• Citation and Bibliography Generation – Zotero integrates with word processors like Microsoft Word, LibreOffice, and Google Docs to insert citations and bibliographies directly into documents. It supports over 10,000 citation styles, allowing users to format their work to match any style guide or publication  

Scholarcy  

Scholarcy is an innovative tool designed to assist researchers, students, and academics in managing the often-overwhelming task of conducting literature reviews. It leverages artificial intelligence to automate the extraction of key information from academic papers, creating structured summaries that make it easier to evaluate and understand research articles. Below are the detailed features of Scholarcy that are particularly useful for conducting literature reviews:    

• Flashcard Summaries – Scholarcy generates interactive summary flashcards from research papers, book chapters, and other documents. These flashcards highlight key information, providing a compact and easy-to-read overview of the text. This feature is perfect for skim reading or getting to the key points of an article before reading it in full.   
• Smart Highlighting and Analysis – The tool guides users to important sections of text and helps interpret them through smart highlighting and analysis. Scholarcy identifies factual statements and findings, highlighting them in different colors (blue for factual statements and orange for findings) to facilitate quick understanding of the content.   
• Access to Full Text and Cited Papers – Scholarcy provides convenient access to the full text of articles and cited papers. It uses icons to link directly to the full text and to services like Libkey, which provides access via institutional subscriptions. Additionally, clicking on the Scholarcy icon shows the key findings from cited articles, offering a quick overview of the citation context.
• Literature Discovery and Screening – Scholarcy aids in the discovery and screening of new literature. It can source, screen, and store academic literature more efficiently than traditional methods. The Scholarcy browser extension provides a detailed Synopsis and Highlights for a more comprehensive screening than what an abstract alone can offer. This process can take as little as five minutes, allowing readers to absorb the underlying points of the literature quickly  
• Reference Management Integration – Scholarcy allows for the export of flashcards to reference management software like Zotero. This integration enables users to see not only the reference of the article in their Zotero library but also the key highlights and structured summaries generated by Scholarcy.   

Unpaywall  

Offering open access to scholarly articles, Unpaywall is a free database built from over 50,000 publishers and repositories globally.  They also leverage open data sources like PubMed Central, DOAJ, Crossref, and DataCite. Major databases including Dimensions, Scopus, and Web of Science have incorporated Unpaywall. For users with Digital Object Identifiers (DOIs), Unpaywall provides various tools to find open access and full-text articles. Data access is available through REST API, R API Wrapper, the Simple Query Tool, or by downloading the entire dataset. Below are the features of this AI tool for literature review  

• Simple Query – The Simple Query Tool offered by Unpaywall helps users to determine if there is an open access (OA) version of a list of articles they are interested in. It is particularly useful for users with a list of Digital Object Identifiers (DOIs) for articles and are seeking free, full-text versions of these articles are available through Unpaywall’s extensive database.
• Browser Extension – The Unpaywall browser extension automatically searches for legally available, free versions of scholarly articles as you browse. When you come across a research article online, the extension checks Unpaywall’s database for an open access version. If such a version is available, a green tab appears on the side of your browser, which you can click to access the full text of the article directly. This feature is powered by an index of over 20 million free, legal full-text PDFs, making it a powerful ally in the quest for open access literature.  

There are many AI tools for literature review available for your assistance. However, the best tool for you depends on your specific needs. Do you prioritize comprehensive search and full-text access? Easy organization and citation management? Or perhaps AI-powered summaries to grasp key findings quickly?  

Evaluate the functionalities of each tool and choose the one that best complements your research workflow. With the right AI tool, your literature review can become a breeze.   

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

Related Posts

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What is Convenience Sampling: Definition, Method, and Examples 

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LitLLM: A Toolkit for Scientific Literature Review

Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work. It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate—generate non-factual information—and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM and Huggingface space ( https://huggingface.co/spaces/shubhamagarwal92/LitLLM ) with the video demo at https://youtu.be/E2ggOZBAFw0

Shubham Agarwal 1,2,3 , Issam H. Laradji 1,4 , Laurent Charlin 2,3,5 , Christopher Pal 1,2,5 1 ServiceNow Research, 2 Mila - Quebec AI Institute, 3 HEC Montreal, Canada 4 UBC, Vancouver, Canada, 5 Canada CIFAR AI Chair Correspondence: [email protected]

1 Introduction

Scientists have long used NLP systems like search engines to find and retrieve relevant papers. Scholarly engines, including Google Scholar, Microsoft Academic Graph, and Semantic Scholar, provide additional tools and structure to help researchers further. Following recent advances in large language models (LLMs), a new set of systems provides even more advanced features. For example, Explainpaper 1 1 1 https://www.explainpaper.com/ helps explain the contents of papers, and Writefull 2 2 2 https://x.writefull.com/ helps with several writing tasks, including abstract and title generation. There are, of course, many other tasks where similar technologies could be helpful.

Systems that help researchers with literature reviews hold promising prospects. The literature review is a difficult task that can be decomposed into several sub-tasks, including retrieving relevant papers and generating a related works section that contextualizes the proposed work compared to the existing literature. It is also a task where factual correctness is essential. In that sense, it is a challenging task for current LLMs, which are known to hallucinate. Overall, creating tools to help researchers more rapidly identify, summarize and contextualize relevant prior work could significantly help the research community.

Recent works explore the task of literature review in parts or in full. For example, Lu et al. ( 2020 ) proposes generating the related works section of a paper using its abstract and a list of (relevant) references. Researchers also look at the whole task and build systems using LLMs like ChatGPT for literature review  Haman and Školník ( 2023 ); Huang and Tan ( 2023 ) . While these LLMs tend to generate high-quality text, they are prone to hallucinations  Athaluri et al. ( 2023 ) . For example, the Galactica system was developed to reason about scientific knowledge (Taylor et al., 2022 ) . While it outperforms contemporary models on various scientific tasks, it generates made-up content like inaccurate citations and imaginary papers. 3 3 3 see e.g., What Meta Learned from Galactica

As a step forward, we explore retrieval-augmented-generation (RAG) to improve factual correctness  Lewis et al. ( 2020 ) . The idea is to use the retrieval mechanism to obtain a relevant list of existing papers to be cited which provides relevant contextual knowledge for LLM based generation.

LitLLM is an interactive tool to help scientists write the literature review or related work section of a scientific paper starting from a user-provided abstract (see Figure  1 ). The specific objectives of this work are to create a system to help users navigate through research papers and write a literature review for a given paper or project. Our main contributions are:

We provide a system based on a modular pipeline that conducts a literature review based on a user-proposed abstract.

We use Retrieval Augmented Generation (RAG) techniques to condition the generated related work on factual content and avoid hallucinations using multiple search techniques.

We incorporate sentence-based planning to promote controllable generation.

2 Related Work

LLMs have demonstrated significant capabilities in storing factual knowledge and achieving state-of-the-art results when fine-tuned on downstream Natural Language Processing (NLP) tasks   Lewis et al. ( 2020 ) .

However, they also face challenges such as hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes   Huang et al. ( 2023 ); Gao et al. ( 2023 ); Li et al. ( 2024 ) . These limitations have motivated the development of RAG (Retrieval Augmented Generation), which incorporates knowledge from external databases to enhance the accuracy and credibility of the models, particularly for knowledge-intensive tasks   Gao et al. ( 2023 ) . RAG has emerged as a promising solution to the challenges faced by LLMs. It synergistically merges LLMs’ intrinsic knowledge with the vast, dynamic repositories of external databases   Gao et al. ( 2023 ) . This approach allows for continuous knowledge updates and integration of domain-specific information in an attempt to limit the effect of outdated knowledge. The proposed work builds upon the advancements around RAG to provide a more efficient solution for academic writing.

On the other hand, there has been a notable emphasis on utilizing Large Language Models (LLMs) for tasks related to information retrieval and ranking   Zhu et al. ( 2023 ) . The work by  Sun et al. ( 2023 ) leverages generative LLMs such as ChatGPT and GPT-4 for relevance ranking in information retrieval, demonstrating that these models can deliver competitive results to state-of-the-art supervised methods. Pradeep et al. ( 2023b , a ) introduce different open-source LLM for listwise zero-shot reranking, further motivating the proposed approach of using LLMs for reranking in our work.

The exploration of large language models (LLMs) and their zero-shot abilities has been a significant focus in recent research. For instance, one study investigated using LLMs in recommender systems, demonstrating their promising zero-shot ranking abilities, although they struggled with the order of historical interactions and position bias   Hou et al. ( 2023 ) . Another study improved the zero-shot learning abilities of LLMs through instruction tuning, which led to substantial improvements in performance on unseen tasks   Wei et al. ( 2021 ) . A similar approach was taken to enhance the zero-shot reasoning abilities of LLMs, with the introduction of an autonomous agent to instruct the reasoning process, resulting in significant performance boosts   Crispino et al. ( 2023 ) . The application of LLMs has also been explored in the context of natural language generation (NLG) assessment, with comparative assessment found to be superior to prompt scoring   Liusie et al. ( 2023 ) . In the domain of Open-Domain Question Answering (ODQA), a Self-Prompting framework was proposed to utilize the massive knowledge stored in LLMs, leading to significant improvements over previous methods   Li et al. ( 2022 ) . Prompt engineering has been identified as a key technique for enhancing the abilities of LLMs, with various strategies being explored   Shi et al. ( 2023 ) . 4 4 4 This paragraph was generated using our platform with some minor modifications based on a slightly different version of our abstract.

Figure 2 provides an overview of the pipeline. The user provides a draft of the abstract or a research idea. We use LLM to first summarize the abstract in keywords that can be used as a query for search engines. Optionally, the users could provide relevant keywords to improve search results. This query is passed to the search engine, which retrieves relevant papers with the corresponding information, such as abstracts and open-access PDF URLs. These retrieved abstracts with the original query abstract are used as input to the other LLM Re-ranker, which provides a listwise ranking of the papers based on the relevance to the query abstract. These re-ranked abstracts with the original query are finally passed to the LLM generator, which generates the related work section of the paper. Recently, Agarwal et al. ( 2024 ) showed that prompting the LLMs with the sentence plans results in reduced hallucinations in the generation outputs. These plans contain information about the number of sentences and the citation description on each line, providing control to meet author preferences. We include this sentence-based planning in the LLM generator as part of this system. In the following, we provide more details about each of the modules.

3.1 Paper Retrieval Module

In our toolkit, we retrieve relevant papers using the Semantic Scholar API. Other platforms could be used, but the S2 Platform is well-adapted to this use case. It is a large-scale academic corpus comprising 200M+ metadata records across multiple research areas, providing information about papers’ metadata, authors, paper embedding, etc. The Recommendations API also provides relevant papers similar to any seed paper. Figure 3 shows our system’s different strategies. We describe these three settings that we use to search for references:

User provides an abstract or a research idea (roughly the length of the abstract). We prompt an LLM (see Figure 4 ) to summarize this abstract in keywords which can be used as a search query with most APIs.

Users can optionally also provide keywords that can improve search results. This is similar (in spirit) to how researchers search for related work with a search engine. This is particularly useful in interdisciplinary research, and authors would like to include the latest research from a particular domain, which could not be captured much in the abstract.

Lastly, any seed paper the user finds relevant enough to their idea could be used with the Recommendations API from search engines to provide other closely related papers.

3.2 Paper Re-Ranking Module

Recent efforts have explored the application of proprietary LLMs for ranking   Sun et al. ( 2023 ); Ma et al. ( 2023 ) as well as open-source models like   Pradeep et al. ( 2023a , b ) . These approaches provide a combined list of passages directly as input to the model and retrieve the re-ordered ranking list   Zhang et al. ( 2023 ) . Typically, a retriever first filters top-k potential candidates, which are then re-ranked by an LLM to provide the final output list. In our work, we use the instructional permutation generation approach  Sun et al. ( 2023 ) where the model is prompted to generate a permutation of the different papers in descending order based on the relevance to the user-provided abstract, thus producing an ordered list of preferences against providing intermediate scores. Figure 5 showcases the prompt we used for LLM-based re-ranking.

3.3 Summary Generation Module

We explore two strategies for generation: (1) Zero-shot generation and (2) Plan-based generation, which relies on sentence plans for controllable generation, described in the following

3.3.1 Zero-shot generation

While LLMs can potentially search and generate relevant papers from their parametric memory and trained data, they are prone to hallucinating and generating non-factual content. Retrieval augmented generation, first introduced in   Parvez et al. ( 2021 ) for knowledge tasks, addresses this by augmenting the generation model with an information retrieval module. The RAG principles have been subsequently used for dialogue generation in task-oriented settings  Thulke et al. ( 2021 ) , code generation  Liu et al. ( 2020 ); Parvez et al. ( 2021 ) and product review generation  Kim et al. ( 2020 ) . RAG drastically reduces hallucinations in the generated output  Gao et al. ( 2023 ); Tonmoy et al. ( 2024 ) .

Our work builds upon the principles of RAG, where we retrieve the relevant papers based on the query and augment them as context for generating the literature review. This also allows the system to be grounded in the retrieved information and be updated with the latest research where the training data limits the parametric knowledge of the LLM. Figure 6 shows our system’s prompt for effective Retrieval Augmented Generation (RAG).

3.3.2 Plan based generation

To get the best results from LLM, recent research shifts focus on designing better prompts (Prompt Engineering) including 0-shot chain-of-thought prompting   Kojima et al. ( 2022 ); Zhou et al. ( 2022 ) , few-shot prompting   Brown et al. ( 2020 ) techniques, few-shot Chain-of-thought prompting   Wei et al. ( 2022 ) and in-context prompting   Li and Liang ( 2021 ); Qin and Eisner ( 2021 ) . However, the longer context of our problem statement (query paper and multiple relevant papers) hinders the application of these techniques for response generation.

We utilized sentence plan-based prompting techniques drawing upon insights from the literature of traditional modular Natural Language Generation (NLG) pipelines with intermediary steps of sentence planning and surface realization  Reiter and Dale ( 1997 ); Stent et al. ( 2004 ) . These plans provide a sentence structure of the expected output, which efficiently guides the LLM in generating the literature review in a controllable fashion as demonstrated in concurrent work (Agarwal et al., 2024 ) . Figure  7 (in Appendix) shows the prompt for plan-based generation with an example template as:

Please generate {num_sentences} sentences in {num_words} words. Cite {cite_x} at line {line_x}. Cite {cite_y} at line {line_y}.

4 Implementation Details

We build our system using Gradio  Abid et al. ( 2019 ) , which provides a nice interface to quickly and efficiently build system demos. Our user interface is also available at HuggingFace Space 5 5 5 https://huggingface.co/spaces/shubhamagarwal92/LitLLM . We query the Semantic Scholar API available through the Semantic Scholar Open Data Platform (Lo et al., 2020 ; Kinney et al., 2023 ) to search for the relevant papers. Specifically, we use the Academic Graph 6 6 6 https://api.semanticscholar.org/api-docs/graph and Recommendations 7 7 7 https://api.semanticscholar.org/api-docs/recommendations API endpoint. In this work, we use OpenAI API 8 8 8 https://platform.openai.com/docs/guides/gpt to generate results for LLM using GPT-3.5-turbo and GPT-4 model. At the same time, our modular pipeline allows using any LLM (proprietary or open-sourced) for different components. We also allow the end-user to sort the retrieved papers by relevance (default S2 results), citation count, or year.

5 User Experience

As a preliminary study, we provided access to our user interface to 5 different researchers who worked through the demo to write literature reviews and validate the system’s efficacy. We also provide an example in the demo with an abstract for a quick start. Particularly, the users found the 0-shot generation to be more informative about the literature in general while the plan-based generation to be more accessible and tailored for their research paper, as also evident in our demo video. 9 9 9 https://youtu.be/E2ggOZBAFw0 . Table 1 (in Appendix) shows the output-related work for a recent paper  Li et al. ( 2023 ) that was randomly chosen with a number of cited papers as 4. Our system generated an informative query Multimodal Research: Image-Text Model Interaction and retrieved relevant papers where the top recommended paper was also cited in the original paper. While zero-shot generation provides valuable insights into existing literature, plan-based generation produces a more succinct and readily usable literature review.

6 Conclusion and Future Work

In this work, we introduce and describe LitLLM, a system which can generate literature reviews in a few clicks from an abstract using off-the-shelf LLMs. This LLM-powered toolkit relies on the RAG with a re-ranking strategy to generate a literature review with attribution. Our auxiliary tool allows researchers to actively search for related work based on a preliminary research idea, research proposal or even a full abstract. We present a modular pipeline that can be easily adapted to include the next generation of LLMs and other domains, such as news, by changing the source of retrieval information.

Given the growing impact of different LLM-based writing assistants, we are optimistic that our system may aid researchers in searching relevant papers and improve the quality of automatically generated related work sections of a paper. While our system shows promise as a helpful research assistant, we believe that their usage should be disclosed to the readers, and authors should also observe caution in eliminating any possible hallucinations.

In the future, we would also like to explore academic search through multiple APIs, such as Google Scholar. This work only considered abstracts of the query paper and the retrieved papers, which creates a bottleneck in effective literature review generation. With the advent of longer context LLMs, we envision our system ingesting the whole paper (potentially leveraging an efficient LLM-based PDF parser) to provide a more relevant background of the related research. We consider our approach as an initial step for building intelligent research assistants which could help academicians through an interactive setting (Dwivedi-Yu et al., 2022 ) .

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In the following, we provide snippets of code to retrieve results from the Semantic Scholar API for both recommendation and query-based search:

Literature Review Generator

Ai tool for generating academic literature review.

• Conduct a literature review for a dissertation or thesis: Save time and ensure a comprehensive understanding of your research topic.
• Prepare for a research proposal: Demonstrate a thorough understanding of the existing literature in your field.
• Write a research paper or article: Use the tool to generate a literature review section for your academic paper or article.
• Develop course materials: As an educator, you can use the tool to prepare literature reviews for course materials or to provide examples to students.

New & Trending Tools

Text rewriter ai, combine two paragraphs, history fact finder.

• Research Guides
• University Libraries

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

Disclaimer:

• The guide is intended for informational purposes. It is advisable for you to independently evaluate these tools and their methods of use.
• Research AI Assistant is available in Dimensions Analytics (TAMU) and Statista (TAMU).
• See news about their AI Assistant (Beta): Web of Science , Scopus , Ebsco , ProQues t, OVID , Dimensions , JStor , Westlaw , and LexisNexis .       

Suggestions:

• Please keep these differences in mind when exploring AI-powered academic search engines.

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• https://www.openread.academy/
• Accessed institutionally by Harvard, MIT, University of Oxford, Johns Hopkins, Stanford, and more. ..
• AI-powered Academic Searching + Web Searching - Over 300 million papers and real-time web content.
• Trending and Topics - Browse them to find the latest hot papers. Use Topic to select specific fields and then see their trending.
• Each keyword search or AI query generates a synthesis report with citations. To adjust the search results, simply click on the Re-Generate button to refresh the report and the accompanied citations. After that click on Follow-Up Questions to go deeper into a specific area or subject.
• Use Paper Q&A to interact with a text directly. Examples: " What does this paper say about machine translation ?" ;  "What is C-1 in Fig.1?"
• When you read a paper, under Basic Information select any of the following tools to get more information: Basic Information > Related Paper Graph> Paper Espresso > Paper Q&A , and > Notes. The Related Paper Graph will present the related studies in a visual map with relevancy indication by percentage.
• Click on Translation to put a text or search results into another language.
• Read or upload a document and let Paper Espresso analyze it for you. It will organize the content into a standard academic report format for easy reference: Background and Context > Research Objectives and Hypotheses > Methodology > Results and Findings > Discussion and Interpretation > Contributions to the field > Structure and Flow > Achievements and Significance , and > Limitations and Future Work.  

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

Semantic Reader "Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual." It "uses artificial intelligence to understand a document’s structure and merge it with the Semantic Scholar’s academic corpus, providing detailed information in context via tooltips and other overlays ." <https://www.semanticscholar.org/product/semantic-reader>.

Skim Papers Faster "Find key points of this paper using automatically highlighted overlays. Available in beta on limited papers for desktop devices only."  <https://www.semanticscholar.org/product/semantic-reader>. Press on the pen icon to activate the highlights.

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>  

• 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  

Scholar GPT - By awesomegpts.ai

• " Enhance research with 200M+ resources and built-in critical reading skills. Access Google Scholar, PubMed, JSTOR, Arxiv, and more, effortlessly ."
• Dialogue prompts suggested on the page: - Find the latest research about AI. - I'll provide a research paper link; Please analyze it. - I will upload a PDF paper; Use critical skills to read it. - Type "LS" to list my built-in critical reading list.
• To access it, in your ChatGPT account > Select " Explore GPTs > Scholar GPT
• 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.
• See a case of using ChatGPT40 to extract a PDF file below.  
• 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

Example- using ChatGPT4o to Extract sections from PDF

I am going to upload a PDF file. Please extract its Research Objectives, Hypotheses,Methodology and findings for me, and then save it into a text file. ChatGPT40 : Yes, you can upload the PDF..... The uploaded document appears to be a paper titled "......" I'll begin by searching for and extracting the relevant sections. Extracting Sections: Research Objectives and Hypotheses, Methodology, Results and Findings Let me start with the extraction. The information has been saved into a text file. You can download it using the link below:Download Extracted_Info.txt

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.  

PAPER DIGEST

• 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.

RESEARCH RABBIT

• 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)
• Next: Dialogues: Insightful Facts >>
• Last Updated: Aug 18, 2024 9:08 PM
• URL: https://tamu.libguides.com/c.php?g=1289555

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10 Powerful AI Tools for Academic Research

• Serra Ardem

AI is no longer science fiction, but a powerful ally in the academic realm. With AI by their side, researchers can free themselves from the burden of tedious tasks, and push the boundaries of knowledge. However, they must use AI carefully and ethically, as these practices introduce new considerations regarding data integrity, bias mitigation, and the preservation of academic rigor.

In this blog, we will:

• Highlight the increasing role of AI in academic research
• List 10 best AI tools for academic research, with a focus on each one’s strengths
• Share 5 best practices on how to use AI tools for academic research

Let’s dig in…

The Role of AI in Academic Research

AI tools for academic research hold immense potential, as they can analyze massive datasets and identify complex patterns. These tools can assist in generating new research questions and hypotheses, navigate mountains of academic literature to find relevant information, and automate tedious tasks like data entry.

Let’s take a look at the benefits AI tools offer for academic research:

• Supercharged literature reviews: AI can sift through vast amounts of academic literature, and pinpoint relevant studies with far greater speed and accuracy than manual searches.
• Accelerated data analysis: AI tools can rapidly analyze large datasets and uncover intricate insights that might otherwise be overlooked, or time-consuming to identify manually.
• Enhanced research quality: Helping with grammar checking, citation formatting, and data visualization, AI tools can lead to a more polished and impactful final product.
• Automation of repetitive tasks: By automating routine tasks, AI can save researchers time and effort, allowing them to focus on more intellectually demanding tasks of their research.
• Predictive modeling and forecasting: AI algorithms can develop predictive models and forecasts, aiding researchers in making informed decisions and projections in various fields.
• Cross-disciplinary collaboration: AI fosters collaboration between researchers from different disciplines by facilitating communication through shared data analysis and interpretation.

Now let’s move on to our list of 10 powerful AI tools for academic research, which you can refer to for a streamlined, refined workflow. From formulating research questions to organizing findings, these tools can offer solutions for every step of your research.

1. HyperWrite

For: hypothesis generation

HyperWrite’s Research Hypothesis Generator is perfect for students and academic researchers who want to formulate clear and concise hypotheses. All you have to do is enter your research topic and objectives into the provided fields, and then the tool will let its AI generate a testable hypothesis. You can review the generated hypothesis, make any necessary edits, and use it to guide your research process.

Pricing: You can have a limited free trial, but need to choose at least the Premium Plan for additional access. See more on pricing here .

2. Semantic Scholar

For: literature review and management

With over 200 million academic papers sourced, Semantic Scholar is one of the best AI tools for literature review. Mainly, it helps researchers to understand a paper at a glance. You can scan papers faster with the TLDRs (Too Long; Didn’t Read), or generate your own questions about the paper for the AI to answer. You can also organize papers in your own library, and get AI-powered paper recommendations for further research.

Pricing: free

For: summarizing papers

Apparently, Elicit is a huge booster as its users save up to 5 hours per week. With a database of 125 million papers, the tool will enable you to get one-sentence, abstract AI summaries, and extract details from a paper into an organized table. You can also find common themes and concepts across many papers. Keep in mind that Elicit works best with empirical domains that involve experiments and concrete results, like biomedicine and machine learning.

Pricing: Free plan offers 5,000 credits one time. See more on pricing here .

For: transcribing interviews

Supporting 125+ languages, Maestra’s interview transcription software will save you from the tedious task of manual transcription so you can dedicate more time to analyzing and interpreting your research data. Just upload your audio or video file to the tool, select the audio language, and click “Submit”. Maestra will convert your interview into text instantly, and with very high accuracy. You can always use the tool’s built-in text editor to make changes, and Maestra Teams to collaborate with fellow researchers on the transcript.

Pricing: With the “Pay As You Go” plan, you can pay for the amount of work done. See more on pricing here .

5. ATLAS.ti

For: qualitative data analysis

Whether you’re working with interview transcripts, focus group discussions, or open-ended surveys, ATLAS.ti provides a set of tools to help you extract meaningful insights from your data. You can analyze texts to uncover hidden patterns embedded in responses, or create a visualization of terms that appear most often in your research. Plus, features like sentiment analysis can identify emotional undercurrents within your data.

Pricing: Offers a variety of licenses for different purposes. See more on pricing here .

6. Power BI

For: quantitative data analysis

Microsoft’s Power BI offers AI Insights to consolidate data from various sources, analyze trends, and create interactive dashboards. One feature is “Natural Language Query”, where you can directly type your question and get quick insights about your data. Two other important features are “Anomaly Detection”, which can detect unexpected patterns, and “Decomposition Tree”, which can be utilized for root cause analysis.

Pricing: Included in a free account for Microsoft Fabric Preview. See more on pricing here .

7. Paperpal

For: writing research papers

As a popular AI writing assistant for academic papers, Paperpal is trained and built on 20+ years of scholarly knowledge. You can generate outlines, titles, abstracts, and keywords to kickstart your writing and structure your research effectively. With its ability to understand academic context, the tool can also come up with subject-specific language suggestions, and trim your paper to meet journal limits.

Pricing: Free plan offers 5 uses of AI features per day. See more on pricing here .

For: proofreading

With Scribbr’s AI Proofreader by your side, you can make your academic writing more clear and easy to read. The tool will first scan your document to catch mistakes. Then it will fix grammatical, spelling and punctuation errors while also suggesting fluency corrections. It is really easy to use (you can apply or reject corrections with 1-click), and works directly in a DOCX file.

Pricing: The free version gives a report of your issues but does not correct them. See more on pricing here .

9. Quillbot

For: detecting AI-generated content

Want to make sure your research paper does not include AI-generated content? Quillbot’s AI Detector can identify certain indicators like repetitive words, awkward phrases, and an unnatural flow. It’ll then show a percentage representing the amount of AI-generated content within your text. The tool has a very user-friendly interface, and you can have an unlimited number of checks.

10. Lateral

For: organizing documents

Lateral will help you keep everything in one place and easily find what you’re looking for. 

With auto-generated tables, you can keep track of all your findings and never lose a reference. Plus, Lateral uses its own machine learning technology (LIP API) to make content suggestions. With its “AI-Powered Concepts” feature, you can name a Concept, and the tool will recommend relevant text across all your papers.

Pricing: Free version offers 500 Page Credits one-time. See more on pricing here .

How to Use AI Tools for Research: 5 Best Practices

Before we conclude our blog, we want to list 5 best practices to adopt when using AI tools for academic research. They will ensure you’re getting the most out of AI technology in your academic pursuits while maintaining ethical standards in your work.

• Always remember that AI is an enhancer, not a replacement. While it can excel at tasks like literature review and data analysis, it cannot replicate the critical thinking and creativity that define strong research. Researchers should leverage AI for repetitive tasks, but dedicate their own expertise to interpret results and draw conclusions.
• Verify results. Don’t take AI for granted. Yes, it can be incredibly efficient, but results still require validation to prevent misleading or inaccurate results. Review them thoroughly to ensure they align with your research goals and existing knowledge in the field.
• Guard yourself against bias. AI tools for academic research are trained on existing data, which can contain social biases. You must critically evaluate the underlying assumptions used by the AI model, and ask if they are valid or relevant to your research question. You can also minimize bias by incorporating data from various sources that represent diverse perspectives and demographics.
• Embrace open science. Sharing your AI workflow and findings can inspire others, leading to innovative applications of AI tools. Open science also promotes responsible AI development in research, as it fosters transparency and collaboration among scholars.
• Stay informed about the developments in the field. AI tools for academic research are constantly evolving, and your work can benefit from the recent advancements. You can follow numerous blogs and newsletters in the area ( The Rundown AI is a great one) , join online communities, or participate in workshops and training programs. Moreover, you can connect with AI researchers whose work aligns with your research interests.

Frequently Asked Questions

Is chatgpt good for academic research.

ChatGPT can be a valuable tool for supporting your academic research, but it has limitations. You can use it for brainstorming and idea generation, identifying relevant resources, or drafting text. However, ChatGPT can’t guarantee the information it provides is entirely accurate or unbiased. In short, you can use it as a starting point, but never rely solely on its output.

Can I use AI for my thesis?

Yes, but it shouldn’t replace your own work. It can help you identify research gaps, formulate a strong thesis statement, and synthesize existing knowledge to support your argument. You can always reach out to your advisor and discuss how you plan to use AI tools for academic research .

Can AI write review articles?

AI can analyze vast amounts of information and summarize research papers much faster than humans, which can be a big time-saver in the literature review stage. Yet it can struggle with critical thinking and adding its own analysis to the review. Plus, AI-generated text can lack the originality and unique voice that a human writer brings to a review.

Can professors detect AI writing?

Yes, they can detect AI writing in several ways. Software programs like Turnitin’s AI Writing Detection can analyze text for signs of AI generation. Furthermore, experienced professors who have read many student papers can often develop a gut feeling about whether a paper was written by a human or machine. However, highly sophisticated AI may be harder to detect than more basic versions.

Can I do a PhD in artificial intelligence?

Yes, you can pursue a PhD in artificial intelligence or a related field such as computer science, machine learning, or data science. Many universities worldwide offer programs where you can delve deep into specific areas like natural language processing, computer vision, and AI ethics. Overall, pursuing a PhD in AI can lead to exciting opportunities in academia, industry research labs, and tech companies.

This blog shared 10 powerful AI tools for academic research, and highlighted each tool’s specific function and strengths. It also explained the increasing role of AI in academia, and listed 5 best practices on how to adopt AI research tools ethically.

AI tools hold potential for even greater integration and impact on research. They are likely to become more interconnected, which can lead to groundbreaking discoveries at the intersection of seemingly disparate fields. Yet, as AI becomes more powerful, ethical concerns like bias and fairness will need to be addressed. In short, AI tools for academic research should be utilized carefully, with a keen awareness of their capabilities and limitations.

About Serra Ardem

Serra Ardem is a freelance writer and editor based in Istanbul. For the last 8 years, she has been collaborating with brands and businesses to tell their unique story and develop their verbal identity.

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Feature Featire

COMPETITORS

Advanced AI-Powered Assistance

Uses state-of-the-art AI technology to provide relevant literature suggestions and structural guidance.

May rely on simpler algorithms, resulting in less dynamic or comprehensive support.

User-Friendly Interface

Designed for ease of use, making it accessible for users with varying levels of tech proficiency.

Interfaces can be complex or less intuitive, posing a challenge for some users.

Transparent and Flexible Pricing

Offers a free trial and clear, flexible pricing plans suitable for different needs.

Pricing structures can be opaque or inflexible, with fewer user options.

Unparalleled Customization

Offers highly personalized suggestions and adapts to your specific research needs over time.

Often provide generic suggestions that may not align closely with individual research topics.

Comprehensive Literature Access

Provides access to a vast and up-to-date range of academic literature, ensuring comprehensive research coverage.

Some may have limited access to current or diverse research materials, restricting the scope of literature reviews.

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This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates prior research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research. We highlight three primary research challenges: integrating advanced AI solutions, such as large language models and knowledge graphs, improving usability, and developing a standardised evaluation framework. We also propose best practices to ensure more robust evaluations in terms of performance, usability, and transparency. Overall, this review offers a detailed overview of AI-enhanced SLR tools for researchers and practitioners, providing a foundation for the development of next-generation AI solutions in this field.

Introduction

In this page, we report the tables we have developed when conducting our analysis on the Systematic Literature Review Tools.

Systematic Literature Review Tools analysed through AI and Generic Features

Screening phase of systematic literature review tools analysed through ai features.

• Extraction Phase of Systematic Literature Review Tools analysed through AI Features
• Systematic Literature Review Tools analysed based on AI Features

Figures of the paper

Prisma checklist.

• Codebase Snowballing

How to cite

In this section, we report three tables that describe the 21 systematic literature review tools examined according to both generic and AI-based features. In Section 1.1 and Section 1.2 , we present the analysis of the AI features for the screening and the extraction phases, respectively. In Section 1.3 , we report the analysis of the tools according to the generic features. A copy of these tables is persistently available on ORKG.

-: No information available

Extraction phase of Systematic Literature Review Tools analysed through AI Features

Systematic Literature Review Tools analysed based on General Features

-: No information available NA: Not applicable because the tools are specifically for extraction

In this section we attach all the figures of the mauscript in high defininition (300DPI).

In the following table we report our PRISMA checklist, using the model from "Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71".

Here is the codebase we developed for the snowballing search on Semantic Scholar.

F. Bolaños Burgos, A. Salatino, F. Osborne, and E. Motta. Artificial intelligence for systematic literature reviews: Opportunities and challenges. Submitted to Artificial Intelligence Review, 2024.

This work is licensed under CC BY 4.0

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SciSpace: An all-in-one AI tool for literature reviews

• by Julianne Burns
• February 11, 2024 February 17, 2024
• AI , literature review , tool-review

Literature Review is a complex and tedious process. Luckily, modern tools make it much more manageable. Just like the internet search was a paradigm shift from a library, AI is a paradigm shift from a simple search. In this deep dive, we are looking at one of the most versatile tools that combines many features and saves you a lot of time: SciSpace .

Join the Effortless Newsletter. Receive free tips on note-taking, literature review, AI tools, and other productivity topics specifically tailored for academics and students.

The primary aim of SciSpace is to use AI to understand and draw connections between research papers, which is essential for anyone undertaking a literature review. Scispace can help you during every step, from finding papers relevant to your research question to helping you understand critical findings. With SciSpace, you can see the source of all information: all answers are cited with a quick link. Here are six use-cases of how this single tool can enhance your literature review:

• Searching literature from a question rather than keywords
• Scanning your existing collection of PDFs for specific criteria using AI
• Interacting with a single PDF to find relevant literature and explain concepts
• Using SciSpace as a browser extension to interact with websites
• Scientific Writing, paraphrasing and AI detection
• Conversation on SciSpace using ChatGPT Plus

Let’s get started:

1. Semantic Search for Literature: Starting From Scratch

SciSpace is an effective tool to search for literature. With SciSpace, you don’t need to rely on keyword searches , which can potentially cause you to miss out on finding related literature. Instead, you can search your papers using a question or phrase. This is called a semantic search. Internally, SciSpace utilizes AI first to understand what you are looking for and then cross-reference it with the content of the papers in its database (282M at the time of writing) .

Your search result will be a paragraph of text answering your question. It will generally contain 5-10 citations and use academic jargon. Below, you will find the papers listed individually and can add columns that summarize a specific aspect of the study. In the example above, we are looking to summarize the model organism and location of the study for a set of ecological papers.

To refine your search you can then select the papers that seem relevant and click the “Show more like selected” button. SciSpace will learn a little bit better what you are looking for and add similar papers to the list of selected papers.

Save relevant papers to “Collections” for later analysis. A collection is essentially a folder containing a number of papers.

Can I use AI search for a systematic literature review?

Generally, no. A systematic literature review systematically reviews all literature that fulfills specific criteria (e.g., employs a particular methodology). This is done so others can reproduce your search (and findings). Using an AI algorithm, however, often leads to (1) slightly deviating results every time you run it and (2) may be incomplete. It is not reproducible, which makes your review not systematic . Therefore, do not use AI to search in the context of a systematic literature review.

But once you have found the papers using a reproducible keyword search in a database like Pubmed, you can use SciSpace to analyze these for criteria. This will save you a lot of time.

2. Analyzing your existing PDF collection using AI

When you already have a collection of PDFs, you can use SciSpace to search through the content of these PDFs. This is useful if you are dealing with more niche literature that might not be in their database or when you are working on a systematic literature review and selecting papers with a reproducible keyword search. The first step is to upload your papers:

After uploading the papers, they will be displayed as a table in your library. You can add columns to this table. Each column answers a question or summarizes an aspect of each paper, respectively. The most powerful feature is that you can create custom columns. For example, I have a giant collection on range shifts in climate change. It is generally assumed that the changing climate makes plants shift their locations/ranges. However, a growing body of literature suggests that competition between plant species also plays a significant role. To find all studies that deal with these so-called “biotic interactions,” I made a column titled “Relevance to biotic interactions.” Note how, in the screenshot below, some studies have an N/A in this column. This means the paper is irrelevant to this particular topic and we didn’t even have to open the PDF to find this out!

3. SciSpace CoPilot: Detailed insights for a single paper

CoPilot is a PDF reader feature of SciSpace. With Copilot, you can have an AI conversation with a paper. Upload a paper or select one from your library and start asking questions. You can even tell CoPilot to recommend questions, helping you think more like a scientist. Additionally, CoPilot speaks many languages, so while the paper may be in English, you can converse in any language you choose.

Some of the best use cases for using CoPilot are less summarizing the content and more utilization of it as an AI-powered search engine within the paper. For example, when you are writing a paper, you will make statements that need citations. To double-check your cite correctly, you can upload the cited PDF to SciSpace and ask, “Does this paper support the statement…”. You can find more information on this in the guide to academic writing with AI .

Conversational AI agents work best if you give them a single task at a time. To solve more complex tasks, SciSpace offers the possibility to ask follow-up questions. For example, ask it to identify the core problem in a paper and then the proposed solution.

4. CoPilot: as a browser extension

CoPilot also works as a Chrome extension , too. When browsing for papers, use this tool to quickly skim papers to decide whether the paper is worth correctly reading. Click on the CoPilot Chrome extension while you have a paper open and start asking the paper questions. Try questions like “Does this paper cover X?” or “Does this paper use X method?”. For a quicker understanding, try to “summarise the results.”

Another use case for the CoPilot extension is to translate and summarize insights. Some domains have a large body of work not translated into English. Some Wikipedia pages also vary significantly in detail depending on their language. The extension allows you to extract information from and to any language.

5. SciSpace for Academic Writing

The paraphraser tool, as the name suggests, can paraphrase text. The tone can be changed, which is helpful when converting highly technical information into more casual language or vice versa. Your text can then be shorter or longer while adjusting the magnitude of these changes.

Lastly, the paraphraser tool can also translate your texts into any language. This is useful if you write a “dirty” paragraph in your language and use AI to translate it to academic English.

SciSpace has an AI detector for academic writing as well. It is currently debated whether AI-generated texts can be detected. Still, if you have worked with ChatGPT for a while, you will notice that it tends to recreate predictable structures across its replies (e.g., “In summary…”). Here is an example of a text I wrote with a lot of assistance from AI ( see this workflow ), but never by just copying and pasting sentences. Instead, AI was used to inspire the writing structure and paraphrase some stubborn sentence parts. The result is pretty on point:

6. Using SciSpace within ChatGPT

ChatGPT is a potent and versatile tool that lacks precise understanding of scientific topics. Connecting SciSpace’s giant publication database with the conversational abilities of ChatGPT makes it a pretty powerful tool. This works through a custom GPT assistant. You can read what GPT assistants are and how to build your own in this tutorial .

The result is a table with citations to your question. Each entry in the table is a link to SciSpace, where you can use the CoPilot to read this article in-depth (See Section 3.)

There are currently 100s of GPTs available in ChatGPT; here are my 5 top AI assistant picks for academics .

7. Saving Notes to Notebooks

SciSpace has a promising new feature called notebooks it allows you to combine highlights in papers, conversations with the Copilot (step 3) and your own writing into a single document. Think of a notebook just as a single document where you can add content in a semi-automatic way. In itself, this is not remarkable, but SciSpace allows you to treat these notebooks just like uploaded PDFs. This means you can analyze notebooks (whether you uploaded them manually or collected in SciSpace) and bulk analyze them with AI (see step 2). This can be really handy if your notes form a big part of your knowledge base.

Pricing and 20-40% discount codes

You will get an incredible amount of work done for free with SciSpace. However, just like with ChatGPT, you will need a premium subscriptions to leverage AI fully. AI models are usually differentiated on their size. Larger models are exponentially more complex but allow for larger inputs and more sophisticated outputs.

A notable feature missing in the free version is the export to Zotero (via CSV or BibTeX). While you can import papers from your Zotero library, exporting is only available to premium users.

If you are considering getting SciSpace premium, you can use the codes Effortless20 and Effortless40 to get 20% off the monthly subscription or 40% off the yearly subscription, respectively.

Learn a complete AI literature Review workflow

SciSpace is a perfect first step in an in-depth literature review process. If you want to learn more about what comes next, check out this course:

Leverage AI to find the most impactful literature quickly, cut your reading volume by 75%, and uncover hidden reference gaps combining various tools into a tested workflow.

SciSpace offers features that assist in all stages of the literature review process:

When looking for papers: use the literature review feature to find papers that match your question and add them to your library.

Once you’ve created your library: Use the AI columns to compare and contrast papers, while narrowing in on the key literature.

When the Key Literature is found: Use CoPilot to ask questions alongside your thorough reading and note-taking process.

Keep in mind that a semantic search is not a great idea for a systematic literature review as the search results will not always be reproducible.

SciSpace is entirely free. However, they do offer a premium subscription that includes a more advanced and unlimited use of the copilot feature. SciSpace is a versatile tool you don’t want to miss in your literature review tool belt.

Something went wrong when searching for seed articles. Please try again soon.

No articles were found for that search term.

Author, year The title of the article goes here

LITERATURE REVIEW SOFTWARE FOR BETTER RESEARCH

“Litmaps is a game changer for finding novel literature... it has been invaluable for my productivity.... I also got my PhD student to use it and they also found it invaluable, finding several gaps they missed”

Varun Venkatesh

Austin Health, Australia

As a full-time researcher, Litmaps has become an indispensable tool in my arsenal. The Seed Maps and Discover features of Litmaps have transformed my literature review process, streamlining the identification of key citations while revealing previously overlooked relevant literature, ensuring no crucial connection goes unnoticed. A true game-changer indeed!

Ritwik Pandey

Doctoral Research Scholar – Sri Sathya Sai Institute of Higher Learning

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.

David Fischer

Research Associate – University of Applied Sciences Kempten

“It's nice to get a quick overview of related literature. Really easy to use, and it helps getting on top of the often complicated structures of referencing”

Christoph Ludwig

Technische Universität Dresden, Germany

“This has helped me so much in researching the literature. Currently, I am beginning to investigate new fields and this has helped me hugely”

Aran Warren

Canterbury University, NZ

“I can’t live without you anymore! I also recommend you to my students.”

Professor at The Chinese University of Hong Kong

“Seeing my literature list as a network enhances my thinking process!”

Katholieke Universiteit Leuven, Belgium

“Incredibly useful tool to get to know more literature, and to gain insight in existing research”

KU Leuven, Belgium

“As a student just venturing into the world of lit reviews, this is a tool that is outstanding and helping me find deeper results for my work.”

Franklin Jeffers

South Oregon University, USA

“Any researcher could use it! The paper recommendations are great for anyone and everyone”

Swansea University, Wales

“This tool really helped me to create good bibtex references for my research papers”

Ali Mohammed-Djafari

Director of Research at LSS-CNRS, France

“Litmaps is extremely helpful with my research. It helps me organize each one of my projects and see how they relate to each other, as well as to keep up to date on publications done in my field”

Daniel Fuller

Clarkson University, USA

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.

Baylor Fain

Postdoctoral Associate – University of Florida

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Introduction

Dissertation writing is part of being a graduate student. There are many different ways to organise your research, and several steps to this process . Typically, the literature review is an early chapter in the dissertation, providing an overview of the field of study. It should summarise relevant research papers and other materials in your field, with specific references. To understand how to write a good literature review, we must first understand its purpose. The goals of a literature review are to place your dissertation topic in the context of existing work (this also allows you to acknowledge prior contributions, and avoid accusations of plagiarism), and to set you up to show you are making a new contribution to the field. Since literature review is repetitive, many students find it tedious. While there are some traditional tools and techniques to help, covered below, they tend to be cumbersome and keyword-based. For this reason, we built a better tool for research and literature review, which I describe in the last section. You can see the Lateral tool in action , and how it makes the literature review a lot easier. To sign up to the tool, click here.

1. Different kinds of reading

We can divide the activity of reading for research into three different kinds: 

• Exploratory reading, mostly done in the initial phase;
• Deep reading of highly informative sources; and 
• Broad, targeted skim reading of large collections of books and articles, in order to find specific kinds of information you already know exist.

1.1. Exploratory reading

Initially, a research student will need to read widely in a new field to gain fundamental understanding. In this early stage, the goal is to explore and digest the main ideas in existing research. Traditionally, this phase has been a manual process, but there is a new generation of digital tools to aid in getting a quick overview of your field, and more generally to organise your research . This stage can happen both before and after the research topic or question has been formulated. It is often unstructured and full of serendipitous (“happy accidental”) discovery  — the student’s job is to absorb what they find, rather than to conduct a targeted search for particular information. ‍

Put another way: You don’t know what you’re looking for ahead of time. By the end of this phase, you should be able to sketch a rough map of your field of study.

1.2. Narrow, deep reading

After the exploratory reading phase, you will be able to prioritise the information you read. Now comes the second phase: Deep, reflective reading. In this phase, your focus will narrow to a small number of highly relevant sources — perhaps one or two books, or a handful of articles — which you will read carefully, with the goal of fully understanding important concepts. This is a deliberative style of reading, often accompanied by reflective pauses and significant note taking. If the goal in the first phase was sketching a map of the globe, the goal in this second phase is to decide which cities interest you most, and map them out in colour and detail.

1.3. Broad, targeted reading

You have now sketched a map of your field of study (exploratory reading), and filled in some parts of this map in more detail (narrow, deep reading). I will assume that by this point, you have found a thesis question or research topic, either on your own, or with the help of an advisor. This is often where the literature review begins in earnest. In order to coherently summarise the state of your field, you must review the literature once again, but this time in a more targeted way: You are searching for particular pieces of information that either illustrate existing work, or demonstrate a need for the new approach you will take in your dissertation. For example, 

• You want to find all “methodology” sections in a group of academic articles, and filter for those that have certain key concepts;
• You want to find all paragraphs that discuss product-market fit, inside a group of academic articles.

To return to the map analogy: This is like sketching in the important roads between your favourite cities — you are showing connections between the most important concepts in your field, through targeted information search.

2. Drawbacks of broad targeted reading

The third phase — broad, targeted reading, where you know what kind of information you’re looking for and simply wish to scan a collection of articles or books to find it — is often the most mechanical and time consuming one. Since human brains tend to lose focus in the face of dull repetition, this is also a tedious and error-prone phase for many people. What if you miss something important because you’re on autopilot? Often, students end up speed- or skim reading through large volumes of information to complete the literature review as quickly as possible. With focus and training, this manual approach can be efficient and effective, but it can also mean reduced attention to detail and missed opportunities to discover relevant information. Only half paying attention during this phase can also lead to accidental plagiarism, otherwise known as cryptomnesia: Your brain subconsciously stores a distinctive idea or quote from the existing literature without consciously attributing it to its source reference. Afterwards, you end up falsely, but sincerely believing you created the idea independently, exposing yourself to plagiarism accusations.

3. Existing solutions to speed up literature reviews

Given the drawbacks of manual speed- or skim-reading in the broad reading phase, it’s natural to turn to computer-driven solutions. One popular option is to systematically create a list of search term keywords or key phrases, which can then be combined using boolean operators to broaden results. For example, in researching a study about teenage obesity, one might use the query:

• “BMI” or “obesity” and “adolescents” and not “geriatric”,

to filter for obesity-related articles that do mention adolescents, but don’t mention older adults.

Constructing such lists can help surface many relevant articles, but there are some disadvantages to this strategy:

• These keyword queries are themselves fiddly and time-consuming to create.
• Often what you want to find is whole “chunks” of text — paragraphs or sections, for example — not just keywords.
• Even once you have finished creating your boolean keyword query list, how do you know you haven’t forgotten to include an important search query?

This last point reflects the fact that keyword searching is “fragile” and error-prone: You can miss results that would be relevant — this is known as getting “false negatives” — because your query uses words that are similar, but not identical to words appearing in one or more articles in the library database. For example, the query “sporting excellence” would not match with an article that mentioned only “high performance athletics”.

4. Lateral — a new solution

To make the process of finding specific information in big collections of documents quicker and easier — for example, in a literature review — search, we created the Lateral app , a new kind of AI-driven interface to help you organise, search through and save supporting quotes and information from collections of articles. Using techniques from natural language processing, it understands, out-of-the-box, not only that “sporting excellence” and “high-performance” athletics are very similar phrases, but also that two paragraphs discussing these topics in slightly different language are likely related. Moreover, it also learns to find specific blocks of information, given only a few examples. Want to find all “methodology” sections in a group of articles? Check. How about all paragraphs that mention pharmaceutical applications? We have you covered. If you’re interested, you can sign up today .

5. Final note — novel research alongside the literature review

Some students, to be more efficient, use the literature review process to collect data not just to summarise existing work, but also to support one or more novel theses contained in their research topic. After all, you are reading the literature anyway, so why not take the opportunity to note, for example, relevant facts, quotes and supporting evidence for your thesis? Because Lateral is designed to learn from whatever kind of information you’re seeking, this process also fits naturally into the software’s workflow.

References:

• Is your brain asleep on the job?: https://www.psychologytoday.com/us/blog/prime-your-gray-cells/201107/is-your-brain-asleep-the-job
• Tim Feriss speed reading: https://www.youtube.com/watch?v=ZwEquW_Yij0
• Five biggest reading mistakes: https://www.timeshighereducation.com/blog/five-biggest-reading-mistakes-and-how-avoid-them
• Skim reading can be bad: https://www.inc.com/jeff-steen/why-summaries-skim-reading-might-be-hurting-your-bottom-line.html
• Cryptomnesia: https://en.wikipedia.org/wiki/Cryptomnesia
• Systematic literature review with boolean keywords: https://libguides.library.cqu.edu.au/c.php?g=842872&p=6024187

Lit review youtube intro: https://www.youtube.com/watch?v=bNIG4qLuhJA

Spread the word

There is a better way than Dropbox and Google Drive to do collaborative research

In this blog, I describe the limitations of Dropbox and Google in the space of research, and propose Lateral as the much needed alternative.

Remote group work and the best student collaboration tools

In this blog, I outline some organisational techniques and the best digital collaborative tools for successful student group work.

6 things to consider and organise before writing your dissertation (and how Lateral can help)

I hope the following six things to consider and organise will make the complex dissertation writing more manageable.

Get into flow.

Literature Reviews

• Artificial intelligence (AI) tools
• Getting started
• Types of reviews
• 1. Define your research question
• 2. Plan your search
• 3. Search the literature
• 4. Organize your results
• 5. Synthesize your findings
• 6. Write the review

Introduction to AI

Research rabbit, copilot (powered by gpt4).

• Thompson Writing Studio This link opens in a new window
• Need to write a systematic review? This link opens in a new window

Contact a Librarian

Ask a Librarian

Generative AI tools have been receiving a lot of attention lately because they can create content like text, images, and music. These tools employ machine learning algorithms that can produce unique and sometimes unexpected results. Generative AI has opened up exciting possibilities in different fields, such as language models like GPT and image generators.

However, students need to approach these tools with awareness and responsibility. Here are some key points to consider:

Information Hallucinations and Summarization Accuracy : Generative AI tools can produce inaccurate citations and summaries. Some tools use trusted sources, but users must understand the scope of these sources and verify all information independently.

Novelty and Creativity : Generative AI tools can produce content that is both innovative and unexpected. They allow users to explore new ideas, generate unique artworks, and even compose original music. This novelty is one of their most exciting aspects. It also means that generative AI content is not reproducible .

Ethical Considerations : While generative AI offers creative potential, it also raises ethical questions. Students should be aware of potential biases, unintended consequences, and the impact of their generated content. Responsible use involves considering the broader implications.

Academic Integrity : When using generative AI tools for academic purposes, students should consult their instructors. Policies regarding the use of AI-generated content may vary across institutions. Always seek guidance to ensure compliance with academic integrity standards.

In summary, generative AI tools are powerful and fascinating, but students should approach them thoughtfully, seek guidance, and adhere to institutional policies. Please refer to the Duke Community Standard  for questions related to ethical AI use.

Looking for a tool that isn't listed here? Let us know about it!

Research Rabbit is a literature mapping tool that takes one paper and performs backward- and forward citation searching in addition to recommending "similar work." It scans the Web for publicly available content to build its "database" of work.

Best suited for...

Disciplines whose literature is primarily published in academic journals.

Considerations

• Integrates with Zotero
• Works mostly with just journal articles
• Potential for bias in citation searching/mapping

»   researchrabbit.ai   «

What is it?

Elicit is a tool that semi-automates time-intensive research processes, such as summarizing papers , extracting data , and synthesizing information . Elicit pulls academic literature from Semantic Scholar , an academic search engine that also uses machine learning to summarize information.

Empirical research (i.g., the sciences, especially biomedicine).

• Both free and paid versions
• Doesn't work well in identifying facts or in theoretical/non-empirical research (e.g., the humanities)
• Potential biases in the natural language processing (NLP) algorithms
• Summarized information and extracted data will still need to be critically analyzed and verified for accuracy by the user

»   elicit.com   «

Think of Consensus as ChatGPT for research! Consensus is "an AI-powered search engine designed to take in research questions, find relevant insights within research papers, and synthesize the results using the power of large language models" ( Consensus.app ).  Consensus runs its language model over its entire body of scientific literature (which is sourced from Semantic Scholar ) and extracts the “key takeaway” from every paper.

The social sciences and sciences (non-theoretical disciplines).

• Free and paid versions
• Similar to Elicit, Consensus should not be used to ask questions about basic facts
• Consensus recommends that you ask questions related to research that has already been conducted by scientists
• Potential for biases in the input data from participants

»   consensus.app   «

Dubbed the "AI-powered Swiss Army Knife for information discovery," Perplexity is used for answering questions (including basic facts, a function that many other AI tools are not adept at doing), exploring topics in depth utilizing Microsoft's Copilot, organizing your research into a library, and interacting with your data (including asking questions about your files).

Perplexity has wide-reaching applications and could be useful across disciplines.

• Free and paid pro versions (the pro version utilizes Microsoft's Copilot AI tool)
• Available in desktop, iOS, and Android apps
• See  Perplexity's blog for more info
• Your personal information and data on how you use the tool are stored for analytical purposes (however, this feature can be turned off in settings)
• Features a browser plug-in, Perplexity Companion , that is essentially a blend of Google and ChatGPT

»   perplexity.ai   «

Did you know that as Duke faculty, staff, and students, we have free access to GPT4 via Microsoft Copilot ?

Log in with your Duke credentials to start using it today.

Microsoft Copilot harnesses the power of GPT-4, one of the most robust large language models (LLMs), in the form of a chatbot, answering questions and generating text that sounds like it was written by a human. While not a replacement for conducting research, it can be helpful when it comes to brainstorming topics or research questions and also as a writing tool (rewriting or paraphrasing content, assessing tone, etc.).

All users across all disciplines.

• Free way to use the GPT-4 model since ChatGPT-4 requires a subscription
• Since it can't always verify its sources, be wary of hallucinations (or made-up citations) that can look very real.
• It is not 100% accurate ! While GPT-4 is touted as being 40% more accurate than its predecessor, users are still expected to verify the information generated by it.
• There is always the potential for bias since the GPT model was trained on a massive dataset of websites, articles, books, etc. (much of which is inherently biased since it was created by humans).

Microsoft Copilot (GPT-4) »   copilot.microsoft.com   «

For ChatGPT-3.5 (free) »   chat.openai.com   «

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• Published: 24 February 2023

Artificial intelligence in academic writing: a paradigm-shifting technological advance

• Roei Golan   ORCID: orcid.org/0000-0002-7214-3073 1   na1 ,
• Rohit Reddy 2   na1 ,
• Akhil Muthigi 2 &
• Ranjith Ramasamy 2  

Nature Reviews Urology volume  20 ,  pages 327–328 ( 2023 ) Cite this article

4175 Accesses

31 Citations

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• Preclinical research
• Translational research

Artificial intelligence (AI) has rapidly become one of the most important and transformative technologies of our time, with applications in virtually every field and industry. Among these applications, academic writing is one of the areas that has experienced perhaps the most rapid development and uptake of AI-based tools and methodologies. We argue that use of AI-based tools for scientific writing should widely be adopted.

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Acknowledgements

The manuscript was edited for grammar and structure using the advanced language model ChatGPT. The authors thank S. Verma for addressing inquiries related to artificial intelligence.

Author information

These authors contributed equally: Roei Golan, Rohit Reddy.

Authors and Affiliations

Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA

Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA

Rohit Reddy, Akhil Muthigi & Ranjith Ramasamy

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Corresponding author

Correspondence to Ranjith Ramasamy .

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Competing interests.

R.R. is funded by the National Institutes of Health Grant R01 DK130991 and the Clinician Scientist Development Grant from the American Cancer Society. The other authors declare no competing interests.

Additional information

Related links.

ChatGPT: https://chat.openai.com/

Cohere: https://cohere.ai/

CoSchedule Headline Analyzer: https://coschedule.com/headline-analyzer

DALL-E 2: https://openai.com/dall-e-2/

Elicit: https://elicit.org/

Penelope.ai: https://www.penelope.ai/

Quillbot: https://quillbot.com/

Semantic Scholar: https://www.semanticscholar.org/

Wordtune by AI21 Labs: https://www.wordtune.com/

Writefull: https://www.writefull.com/

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Golan, R., Reddy, R., Muthigi, A. et al. Artificial intelligence in academic writing: a paradigm-shifting technological advance. Nat Rev Urol 20 , 327–328 (2023). https://doi.org/10.1038/s41585-023-00746-x

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AI tools for literature searching

Overview of ai tools for literature searching, selected tools, how to use these tools safely.

There are many new AI tools available to researchers and students which focus on academic sources (rather than general AI tools such as Copilot or ChatGPT). These tools offer a powerful new method of discovering academic literature and can be a great complement to traditional methods of searching the literature in your field.

For thorough or comprehensive searching such as for a research paper or thesis, you should still search the major academic databases in your field . These AI tools use freely available or negotiated underlying datasets. These datasets do not necessarily contain the important journals in any field, however academic databases are produced specifically to provide this coverage. You can find the databases in your field in our Subject Guides .

Some of the more popular tools you might like to explore are below. Note that there are many more, and new developments are happening all the time. You can search the web to investigate what is available.

• Use these tools as an extra technique  for research and not to replace a thorough search.
• Verify any information provided by generative AI tools with credible sources and check for missing information.
• Note that most of these tools do not have access to the full text of papers - they base their answers on titles and abstracts only. You should always access, read and cite the full papers that you use in your work.
• Don't upload materials from Library resources such as databases. This is in breach of copyright, and license agreements
• Don't upload your own sensitive or private documents. It's possible another user of the tool might retrieve your information from the underlying software
• You should acknowledge your use of AI tools to complete your work. See our guide on using Generative AI tools for assignments for more information.      
• Last Updated: Aug 26, 2024 11:33 AM
• URL: https://guides.library.uq.edu.au/research-techniques/ai-tools-for-literature-searching

• INNOVATION FESTIVAL
• Capital One

08-20-2024 TECH

How AI tools help students—and their professors—in academic research

New systems can help surface relevant research papers and quickly understand what they have to say.

[Photo: Nikish Hiraman/peopleimages.com/Adobe Stock]

BY  Steven Melendez 7 minute read

For students and professional scholars alike, starting a new research project typically means digging through academic literature to understand what others have already written.

That can take a considerable amount of time, with researchers tracking down and combing through journal articles to begin their research and contextualize their own findings. But a growing collection of AI-powered tools aims to make that process easier. These new tools can help researchers more quickly find relevant papers, pull out relevant information from them, or both.

“It can be a really helpful way to get started with research, especially for students who aren’t familiar with the research process,” says Breanne Kirsch, director of the library at Illinois College. “As long as they’re taught how to use it in an ethical way, and that they can then expand beyond what it does.”

A tool called Elicit can help researchers conduct what are called systematic reviews , which involve going through copious amounts of published research to find an answer to a question, like how a particular drug affects a medical condition. “It’s all very, very manual,” says James Brady, head of engineering at Elicit. “It takes teams of people many months, and you know, costs hundreds of thousands or millions of dollars to do these things.” 

Elicit can make that process much faster, and also help researchers by quickly finding and summarizing published papers related to a particular question. It can also generate tables describing a whole set of relevant papers, with columns for data points like algorithms and statistical techniques used, variables examined, and the number of participants in experiments. 

The company recommends researchers still look at the original papers, and Brady emphasizes that the tool doesn’t replace the human judgment and analysis necessary to scientific research. “It’s not like you take the final step of Elicit and hit the publish button and then it ends up in Nature or something,” he says, but it can still greatly speed the process of sifting through and understanding prior work.

Understanding how AI can help academic research is part of a larger industry question of how and when the technology can replace or supplement traditional web search tools. And since the 1990s , computer scientists have realized that the academic publishing landscape—where scholars cite each other’s papers and publish in journals with a particular reputation in a particular field—isn’t that different from the internet ecosystem . That means techniques for finding relevant materials, minimizing AI errors and hallucinations, and presenting useful and verifiable results to the user may transfer from academia to the broader web.

ABOUT THE AUTHOR

Steven Melendez is an independent journalist living in New Orleans.   More

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Streamline your research with Afforai's Prompt Library, featuring tailored prompts to search, summarize, and draft academic content.

Original Post: https://community.afforai.com/c/tips-tricks/let-s-create-a-library-of-prompts

Credit : Manuel Sánchez, Ph.D.

These prompts should enhance Afforai’s ability to search, summarize, translate, identify gaps, create bibliographies, outline, and draft research content efficiently and accurately. Here’s a comprehensive library of prompts to support these functionalities:

Search and Discover

Prompt 1: comprehensive search.

"Search within the [uploaded papers] [Semantic Scholar] for recent and relevant studies on [specific topic/keywords], prioritizing results from journals such as [the American Journal of International Law, European Journal of International Law, and Journal of International Economic Law]. Provide a list of the top 10 most relevant studies, including their titles, authors, journal name, publication dates, and abstracts."

*For this prompt, you need to choose either Document Retrieval or Semantic Scholar modes.

“You are an experienced scholar specialized in [research area(s)], and you are well equipped to carry out interdisciplinary research of the highest level as necessary. Provide a literature review for my next research. My next research is about [description of your research and research question]. In your literature review, provide the five most cited academic sources you can find for this research niche. Dig deep. Propose only the most cited and most relevant sources. Separately, provide the most cited academic sources by momentum (i.e. the most cited sources relative to their recency of publication).“

*You need to choose Semantic Scholar mode for this prompt.

Prompt 2: Focused Search by Keywords

"Identify papers related to [keyword 1], [keyword 2], and [keyword 3] within the uploaded library and Semantic Scholar. Highlight studies published in the last 5 years and provide summaries of their key findings and methodologies."

Prompt 3: Explain a Paper

"As an Academic Simplification Assistant, your task is to help users explain complex academic papers in a manner accessible to a 14-year-old audience. Begin by advising the user to first understand the core concepts and main arguments of the paper themselves. Then, respond to any follow up questions the user may have. Start with @thispaper."

*Credit for this prompt: Andy Stapleton (the final version is mine, but it is based on his original prompt)

Prompt 4: Abstract Summarization

"Generate a concise and accurate abstract (maximum 150 words) for each paper in this collection. Focus on highlighting the key findings, methodologies, and conclusions. Ensure the summaries are specific to the fields of [international law, international affairs, or political science.]"

"You are an expert research assistant. Create an abstract for this peer-reviewed paper. Use these examples from other papers to help refine the structure:

*Credit for this prompt: Andy Stapleton

Prompt 5: Collection Summarization

"Create an overarching summary for a collection of papers on [specific topic]. This summary should be no longer than 300 words and should include the primary themes, significant findings, and common methodologies used across the papers."

Translate Knowledge

Prompt 6: simplified translation.

"Translate the following academic summary into layman's terms suitable for a general audience, ensuring that the core information and key points are preserved. The summary should be accessible to individuals without a background in [international law or political science.]"

Prompt 7: Interdisciplinary Summary

"Provide a summary of this paper that is suitable for interdisciplinary collaboration between [legal] and [political science] scholars. Simplify complex legal and technical terms while maintaining the integrity of the original information. Make it understandable to researchers from both fields. Assume that the scholars involved are absolute beginners in each other's subjects. This is, legal scholars do not know anything about political science, and vice versa."

Identify Research Gaps

Prompt 8: gap analysis.

"Compare the findings of these selected papers on [specific topic]. Identify any gaps in the literature where further research is needed. Highlight areas that have been under-explored or where conflicting conclusions exist."

Prompt 9: Emerging Trends and Gaps

"Analyze the selected papers to determine emerging trends and identify any gaps in research within the field of [specific topic]. Suggest potential areas for further investigation based on the analysis."

Create Bibliographies

Prompt 10: mla bibliography.

"Generate an MLA-style bibliography based on the citations found within this collection of papers. Ensure that each entry is formatted correctly and includes all necessary details such as authors, titles, publication dates, and page numbers."

Prompt 11: OSCOLA Bibliography

"Create an OSCOLA-style bibliography for the referenced papers in this research library. Pay attention to accurate citation details, including pinpoint references for quotes and specific arguments."

Outline Papers

Prompt 12: paper outline.

"Develop a structured outline for a research paper on [specific topic]. Ensure the outline includes sections such as introduction, literature review, methodology, findings, discussion, and conclusion. Each section should have a brief description of its intended content."

"As an academic paper writing assistant, your task is to help users structure their scholarly papers efficiently. Create an outline about a peer reviewed paper on [transparent electrode materials for OPV devices]."

Credit for this prompt: Andy Stapleton

Prompt 13: Detailed Section Outline

"Create a detailed outline for the methodology section of a paper on [specific topic]. Include sub-sections for study design, data collection, and data analysis. Provide brief descriptions for each sub-section."

Prompt 14: Creation of Narratives Based on Outline

"Based on the following outline, create a narrative for this paper:

[copy/paste your outline]"

"As an academic paper writing assistant, guide users in leveraging figures to structure their research papers. Take the figures attached and create a narrative for this paper."

Draft Writing

Prompt 15: draft introduction.

"Draft the introduction for a research paper on [specific topic], incorporating background information, research questions, and the significance of the study. The tone should be academic and suitable for publication in a journal such as the [American Journal of International Law (the most prestigious journal if my field)]."

Prompt 16: Draft Literature Review

"Write the literature review section for a research paper on [specific topic]. Summarize relevant studies, highlight major contributions, and identify gaps in the literature. Ensure the tone and style are consistent with academic writing standards in international law."

Paper title

Prompt 17: evaluation of titles.

"As a title evaluation assistant for academic papers, your task is to assess the effectiveness of a paper title based on specific criteria. When a user submits a title, begin by evaluating its clarify relevance to the content, originality, and its ability to engage the intended audience. Rate each of these criteria from scale from 1 to 2.5, leading to a total score out of 10. Provide feedback for each criterion, highlighting strengths and suggesting improvements where necessary."

Prompt 18: Creation of Titles

"Based on the following research question and abstract, provide 5 title ideas for my paper.

Research question: [QUESTION?]

Abstract: [TEXT]

Guide: Creating an Academic Article

General introduction.

Julius is an excellent multidisciplinary tool that can help you streamline your research needs. The purpose of this article is to demonstrate how to effectively use Julius as an extensive tool for research, data analysis, and academic writing. This article will walk the reader through the process of using the different tools available with Julius to help search databases, find relevant literature, perform statistical analysis on data collected, and curate a full-blown academic article.

To illustrate the practical application of Julius in a research setting, consider the following situation:

Imagine a horticultural researcher working for Oregon State University. Recently, a large agricultural company approached the researcher to test the effects of different fertilizer types on the growth of French Marigolds (Tagetes patula), as they are popular in the region for multiple reasons.

The study involves a control fertilizer (NPK Fertilizer: balanced mix of nitrogen, phosphorus and potassium), a compost coupled with biofertilizer (a combination of compost and living microorganisms that promote plant growth), and an organic treatment (a formulation of organic matter used as a nitrogen source, supplemented with rock phosphate and feldspar for potassium).

The researcher is provided with a hefty supply of marigold seeds offered from the agricultural company, with the objective of determining which fertilizer type, if any, best supports the growth of T. patula under controlled conditions. If significant findings are revealed, the researcher is expected to publish the findings to an Agricultural Journal.

Having written an academic thesis before, the researcher understands the general outline and structure of a research paper – introduction and literature review, methodology, results, discussion and conclusion. However, creating a detailed outline and conducting a thorough literature review has always been difficult. Fortunately, the researcher was introduced to Julius by a coworker and has found it useful for curating academic articles and running statistical analyses.

Introduction & Literature Review

The process begins by inputting basic information provided by the agricultural company into Julius. It is important to remind Julius that academic papers may have page limits, requiring the content to remain concise and focused while still capturing all the important elements. Julius provides you with the following output:

After reviewing the outline, the researcher asks Julius to create an interactive checklist to track each task in the outline. This checklist will allow a structured and organized approach to writing (a brief overview of the table is shown below).

Next, Julius is instructed to aid in finding relevant literature for each of the sections in the introduction and literature review. The researcher then carefully fills out the table with the information gathered from their literature review with the help of Julius.

Methodology

The same process used for the Introduction & Literature Review is repeated to generate a table for the methodology section.This includes the details related to the experiment design, plant material, soil preparation, growing conditions, and data analysis. A short snippet of the table is shown below for reference.

Julius assists in sifting through the dense literature to find the needed resources for each section. Once the table has been completed, the methodology section is written, and data collection begins.

For the purpose of this demonstration, we are using a synthetic dataset.

With the data collection complete, the researcher is ready to analyze the data. The dataset can be found here . Below is also a quick preview of the dataset:

The dataset is uploaded into Julius, and two additional columns are added to sum the total dry and wet biomass.

Descriptive statistics are then run to understand the nature of the dataset.

The distribution of the dataset is examined to check the spread and range of the data compared between the three treatments.

Julius is then asked to confirm that the dataset is suitable for a one-way ANOVA, ensuring that all assumptions are met before continuing with the test.

After confirming that the dataset passes each assumption laid out by the one-way ANOVA, the researcher proceeds with the test. They retrieve the following results:

Written Results

A total of 90 plants were harvested from Oregon State University Research Greenhouse. A one-way analysis of variance was run on plant height and dry biomass weight between the different treatments.

Total Dry Biomass

The one-way ANOVA indicated a statistically significant difference in total dry biomass amongst all three treatment groups (Table 1, F(2, 87) = 193.67, p < 0.001). Tukey’s HSD post hoc test was conducted, and concluded that all three treatments were significantly different from one another, with the compost treatment consistently showing the highest mean dry biomass (11.21g± 0.96g), followed by the organic treatment (9.05g ± 0.62g), and lowest for the control treatment (7.04g±0.85g)(Figure 1).

Plant Height

Similarly, the one-way ANOVA results for Plant Height showed significant differences among the treatment groups (Table 1, F(2, 87) = 63.93, p < 0.001). Post hoc tests further revealed significant differences between all three groups, with compost treatment showing the tallest plants (30.40cm±2.36cm), followed by the organic treatment (27.29cm±1.80cm), and the control treatment producing the shortest plants (24.62cm±1.71cm)(Figure 1).

Discussion & Conclusion

The discussion section is laid out similarly to the introduction and methodology. Julius provides a structured outline, which guides the researcher through how set up their section (a short snippet is shown below).

Literature Search with Julius

After completing the chart, the researcher finishes writing their discussion and conclusion section.

Abstract & References

Finally, Julius is used to help format an outline for the abstract and create a checklist to ensure all sections are complete. The references are compiled from the documented literature in the tables, sorted alphabetically, and added to the finalized article.

With the article written and references included, the findings are ready to be sent to the agricultural company!

Keywords: Research Tool, AI, Statistical Analysis, Data Analysis, Academic Writing, Literature Review, Methodology, Discussion, Experimental Design.

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How AI can enhance research

Generative Research AI can significantly aid in literature research by providing several key advantages. Firstly, it can analyze vast amounts of text data quickly and efficiently, helping researchers to identify trends, patterns, and correlations across a wide range of literary works. This capability is especially useful for conducting comprehensive literature reviews and synthesizing information from diverse sources.

Secondly, research AI can assist in generating summaries, abstracts, and annotations of texts, saving researchers time in extracting key information and understanding complex literary theories or arguments.

Lastly, Research AI can facilitate collaborative research by providing shared platforms for annotation, discussion, and critique of literary texts among scholars and students worldwide. This collaborative capability fosters a dynamic exchange of ideas and insights, enriching the overall research process in literature and humanities. - generated by ChatGPT

Review the tabbed  Research AI tools to learn how they can be useful for your research needs. 

• Elicit: The AI Research Assistant - click to begin using Elicit searches across over 126 million academic papers (updated weekly) from Semantic Scholar, an open-source research platform, across all academic disciplines. Price: users automatically get 5,000 free credits to use. After those credits run out, users must subscribe to Elicit plus $12 monthly, billed monthly at $144 per year.
• Setting up Elicit AI for Scientific Literature searches - Tutorial 1 Basic info to use Elicit free version. The video is 1 min 17 sec. You may need to authenticate using your Trevecca credentials.
• Literature Search in Elicit AI -Tutorial 2 Gives a deeper understanding of Elicit results and search options. The video is 4 min 44 sec. You may need to authenticate using your Trevecca credentials.

• Inciteful - click to begin using Unlike a traditional search engine, citations are the cornerstone of Inciteful. Building Inciteful uses open scholarly bibliographic data. Great tools. Price: Free
• Learn Your Research Field Faster With Inciteful - Tutorial Inciteful is a tool to help you find new research articles based on a single article. You can use Inciteful to learn your field and create network analysis maps. Use DOI numbers to execute a search. Results identified 'cited by,' include links to articles, links to top authors and journals. 6min 19 sec.

• Open Knowledge Maps - click to begin using Map a research topic using AI for scientific knowledge. Increase the visibility of research findings for science and society using this open-source site. Price: Free
• Open Knowledge Maps - Webinar video Peter Kraker (founder) introduced Open Knowledge Maps (OKM), which provide an instant visual overview of a topic by showing the main areas researched at a glance and papers related to each area. It searches over 7,000 content sources in all disciplines, providing access to over 140 million documents. The knowledge maps include closed and open-access papers; however, they highlight open-access papers, most of which can be accessed from within the interface. Jump to the sections below for basic use information. 9m 11s - Use for Discovery 10m 45s - Demo

• Research Rabbit - Click to begin using Research Rabbit unlocks a novel way to search for papers and authors, monitor new literature, visualize research landscapes, and collaborate with colleagues. View the video below to ResearchRabbit, which aims to empower researchers with powerful tech - starting with our discovery app! Look at some of the exciting features in the app and how you can discover and visualize the most relevant papers, authors, alerts, and more!
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The Best 8 AI-Powered Tools for Literature Review

In this blog post, I will explore the benefits of using best AI-powered tools for literature review . Literature survey is a crucial step in any research work, but it can also be a daunting and time-consuming task.

You have to find, read, and evaluate hundreds or thousands of research papers, synthesize the existing knowledge, and identify the gaps and opportunities for your research.

Fortunately, there are some AI-powered apps that can help you with literature survey and make it easier and faster. In order to automate the tasks such as searching for articles, identifying relevant papers, and summarizing papers the apps use the artificial intelligence.

Besides, this saves you a significant amount of time and effort. It lets you focus on the more creative and strategic aspects of your research work.

Here are some of the best 8 AI-powered apps for literature review that you should try:

Research Rabbit

Research Rabbit is an AI-powered app that helps researchers find, organize, and analyze research papers.

It is a free tool that can be used by anyone, regardless of their research experience.

Research Rabbit uses AI to scan the web for relevant scholarly articles. In addition to that the AI-based tool allows you to save and organize articles in your own personal library.

To use Research Rabbit, you first need to create an account. Once you have created an account, you can start searching for research papers. You can search by keyword, topic, or author.

Once you have found the papers you are interested in, you can add them to your collections. Collections are a way to organize your research papers. You can create as many collections as you need.

Research Rabbit- Link

Rayyan is an AI-powered app that helps researchers conduct systematic literature review s.

A systematic literature review is a comprehensive research process that involves identifying, evaluating, and synthesizing all the available research on a particular topic.

It helps you work quickly through even your largest reviews. It lets you de-duplicate, screen, and organize references, as well as collaborate with your team and generate reports.

You can use Rayyan to import references from various sources, apply inclusion and exclusion criteria, assign labels and ratings, and export your data for further analysis.

Rayyan- Link

Scholarcy is an AI-powered app that can help you with your academic reading. It can automatically summarize articles, create flashcards, and generate bibliographies.

Scholarcy can also help you find related research and identify key information in articles.

In order to use Scholarcy, you first need to create an account. Once you have created an account, you can start adding articles to your library.

Additionally, you can add academic papers from a variety of sources like Google Scholar, PubMed, and your personal library.

Once you have added scholarly articles to your library, you can start using Scholarcy’s features. To summarize an article, simply click on the “Summarize” button.

Scholarcy will then generate a summary of the article in a few seconds.

Overall, Scholarcy is a valuable tool that can help you with your academic reading. If you are looking for a way to save time, improve your understanding, and increase your productivity, Scholarcy is a great option.

Scholarcy- Link

Lateral is an AI-powered app that can help you with your academic research. It can help you find relevant research, organize your findings, and write better papers.

Lateral uses artificial intelligence to analyze research papers and identify key concepts, relationships, and trends. This information can then be used to help you find relevant research, organize your findings, and write better papers.

It helps you organize, search, and save information from collections of articles. You can import articles from various sources, create tags and notes, and use natural language queries to find specific information in your collections.

You can use Lateral to manage your literature review workflow , keep track of your sources and citation s, and access your information anytime and anywhere.

Lateral – Link

Scite is a free, open-source AI-powered app that helps researchers better discover and understand research articles. Scite does this by providing Smart Citation s, which display the context of a citation and describe whether the article provides supporting or contrasting evidence.

To use Scite, simply go to the Scite website and sign up for an account. Once you have an account, you can start searching for research articles. Scite will display a list of articles that match your search criteria, and you can then click on an article to view its Smart Citation s.

You can use Scite to assess the quality and reliability of the literature, avoid citing unreliable sources, and find evidence for or against any hypothesis.

Scite- Link

If you are looking for a way to quickly and easily find answers from scientific research, you might want to try Consensus AI, a new search engine that uses artificial intelligence to extract and distill findings directly from peer-reviewed studies.

Consensus AI is a powerful tool that can help you find evidence-based answers from scientific research in a fast and easy way.

You can also use various features to refine your search and explore more options. Consensus AI can help you save time and effort in finding reliable information from scientific research.

Consensus- Link

Semantic Scholar

It is a free AI-powered academic search engine that helps you find relevant academic papers faster and easier than traditional methods. It uses natural language processing and machine learning to understand your research needs and provide you with smart filters, citation analysis, and key insights from papers.

You can use Semantic Scholar to discover the most influential and recent scientific literature in your field, compare different methods and results, and track the impact of your own publications.

Semantic Scholar- Link

It is an AI-powered reading assistant that helps you organize, annotate, and collaborate on your research. It helps you fine-tune your literature search, generate summaries, highlight and compare ideas, and share and discuss literature with your team.

You can use RAx to create different projects for your research topics, import articles from various sources, annotate them with notes and comments, and get personalized suggestions for further reading.

RAx Literature Review and Critical Analysis Tool-  Link

It is an AI-powered research assistant that can help you with tasks such as finding relevant articles, summarizing articles, and generating research ideas.

A comprehensive platform for all your research processing: Smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction and systematizing of data

Iris AI -Your Research Workspace- Link

These are some of the best 8 AI-powered apps for literature review that can help you save time and effort, improve your understanding and writing skills, and produce high-quality research. Try them out today and see how they can supercharge your literature review process.

It is important to note that AI-powered apps are not a replacement for hum an judgment. However, they can be a valuable tool for helping you to find relevant research articles, identify important concepts, and track the development of research over time.

If you are interested in trying one of these apps, I encourage you to visit their websites and sign up for a free trial. You can also read reviews from other users to get a better idea of which app is right for you.

If you want to supercharge your literature review with AI, you should try out these 8 apps today. They are easy to use, affordable, and reliable. They will transform your literature review process and help you achieve your research goals faster and easier.

Don’t wait any longer. Start using these AI-powered apps for literature review today and see the difference for yourself. You will be amazed by how much they can help you with your research.

I hope this blog post has been helpful. If you have any questions, please feel free to leave a comment below.

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Peer-reviewed

Research Article

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals

Contributed equally to this work with: Michele Salvagno, Alessandro De Cassai

Roles Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected] (AC)

Affiliation Sant’Antonio Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy

Roles Writing – original draft, Writing – review & editing

Roles Visualization, Writing – original draft, Writing – review & editing

Affiliations Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine, Ubiquitous Health Technologies Lab, University of Waterloo, Waterloo, Canada

Affiliation Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy

Affiliation Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America

Roles Supervision, Writing – original draft, Writing – review & editing

• Michele Salvagno, 
• Alessandro De Cassai, 
• Stefano Zorzi, 
• Mario Zaccarelli, 
• Marco Pasetto, 
• Elda Diletta Sterchele, 
• Dmytro Chumachenko, 
• Alberto Giovanni Gerli, 
• Razvan Azamfirei, 
• Fabio Silvio Taccone

• Published: August 23, 2024
• https://doi.org/10.1371/journal.pone.0309208
• Peer Review
• Reader Comments

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13 th , 2023, to September 1 st , 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Citation: Salvagno M, Cassai AD, Zorzi S, Zaccarelli M, Pasetto M, Sterchele ED, et al. (2024) The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals. PLoS ONE 19(8): e0309208. https://doi.org/10.1371/journal.pone.0309208

Editor: Sanaa Kaddoura, Zayed University, UNITED ARAB EMIRATES

Received: November 22, 2023; Accepted: August 8, 2024; Published: August 23, 2024

Copyright: © 2024 Salvagno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Artificial intelligence (AI) and machine learning systems are advanced computer systems designed to emulate human cognitive functions and perform a wide range of tasks independently. The giant leaps these systems provide are the possibility to learn and solve problems through autonomous decision-making if an adequate initial database is provided [ 1 ]. Natural Language Processing (NLP) represents a field within AI focused on enabling machines to understand, interpret, and respond to human language meaningfully.

One intriguing advancement within the realm of AI is the development of Large Language Models (LLMs), which are a subset of NLP technologies. They are characterized by billions of parameters, which allows them to process and generate human-like text, understanding and producing language across a wide range of topics and styles.Generative chatbots, like ChatGPT(Generative Pre-trained Transformer), Microsoft Copilot, or Google Gemini,enhance these models and offer an easy-to-use interface. These LLMs excel in natural language processing and text generation, making them invaluable for diverse applications. Specifically, they have been used in medical research for estimating adverse effects and predicting mortality in clinical settings [ 2 – 4 ], as well as in scientific writing and publishing [ 5 ]. Finally, domain-specific or fine-tuned modelsare models that undergo additional training on a specialized dataset and are tailored to specific areas of expertise. This allows these models to develop a deeper understanding of terminology, concepts, and contexts, making them more adept at handling tasks ina specific field.

Potential applications of AI, and more precisely LLMs, in scientific production, are vast and multi-faceted. These applications range from automated abstract generation to enhancing the fluency of English prose for non-native speakers and even streamlining the creation of exhaustive literature reviews [ 6 , 7 ]. However, AI output is far from being perfect, as AI hallucination has been well described and documented in the current literature [ 8 , 9 ]. Additional concerns include ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, limited knowledge, incorrect citations, cybersecurity issues, and the risk of infodemics [ 9 ].

In light ofAI’s novel application in scientific production, it remains unclear to what extent the scientific community understands its inherent potentials, limitations, and potential applications. To address this, the authors designed a survey to examine the level of familiarity, understanding, and perspectives among contributing authors in premier medical journals regarding the role and impact of artificial intelligence in top scientific research and authorship. We hypothesize that, given the novelty of large language models (LLMs), researchers might not be familiar with their use and may not have implemented them in their daily practice.

Survey design

An online survey in this study was conducted using the SurveyMonkey web instrument ( https://www.surveymonkey.com , SurveyMonkey Inc., San Mateo, California, USA). The survey protocol (P2023/262) was approved by the Hospitalo-FacultaireErasme–ULB ethical commission(Comitéd’Ethiquehospitalo-facultaireErasme–ULB, chairman: Prof. J.M. Boeynaems) on July 11 th , 2023.

Two members of the survey team (M.S. and A.D.C.) performed a bibliographic search on April 19, 2023, on PubMed and Scopus, to retrieve any validated questionnaire on the topic using the following search string: [((Artificial Intelligence) OR (ChatGPT) OR (ChatBot)) AND ((scientific production) OR (scientific writing)) AND (survey)]. No existing surveys on the specific topic were found.

Therefore, the research team constructed the questionnaire under the BRUSO acronym to create a well-constructed survey [ 10 ]. The survey consisted of 20 single-choice, multiple-choice, and open-ended questions investigating individuals’ perceptions of using Artificial Intelligence (AI) in scientific production and content. The full list of questions is available for consultation in English ( S1 Appendix Content 1, Survey Questionnaire in English).

Population of interest

The population of interest in this survey consisted of corresponding authors who published in 2022 in the 15 highest-impact medical journals ( S2 Appendix Content 2), as ranked by the Journal Citation Report from Clarivate. In this survey, we used the Journal Impact Factor (JIF) as a benchmark to target leading publications in the research field. Originally developed by Eugene Garfield in the 1960s, the JIF is frequently employed as a proxy for a journal’s relative importance within its discipline. It is calculated by dividing the number of citations in a given year to articles published in the preceding two years by the total number of articles published in those two years. The focus on the corresponding authors aimed to access a segment of the research community that is potentially at the forefront of research publishing and scientific production. For this survey, only the email addresses of the corresponding authors listed in the manuscript were sought and collected. Whenmultiple emails were listed as corresponding, only the first email for each article was collected.When no email addresses were found, no further steps were taken to retrieve them.No differentiation was made regarding the type of published article, except for excluding memorial articles dedicated to deceased colleagues. All other articles were included. The authenticity of the email addresses or their correspondence with the author’s name was not verified. As a result, it was not possible to calculate the a priori sample size.

Survey distribution plan

To enhance the survey’s effectiveness, a pretest was performed in two phases. In the first phase, the survey team reviewed the entire survey, with particular attention to the flow and the order of the questions to avoid issues with “skip” or “branch” logic. The time required to complete the survey was estimated to be around four minutes. In the second phase,the survey was distributed for validation to a small subset of participants, which included researchers working at the Erasme Hospital, to identify any issues before distributing it to the general population of interest. Their answers were not included in the final data analysis.

UsingSurveyMonkey’s email distribution feature, the survey link was disseminated to all collected email addresses of the corresponding authors. To minimize the ratio of non-responders, reminder emails were sent one, two, and three weeks after the initial contact, with a final reminder sent one month later. Responses were collected from July 13 th , 2023, to September 1 st , 2023. SurveyMonkey’s web instrument automatically identifies respondents and non-respondents through personalized links, allowing for targeted reminders to only those who had not yet completed the survey. This system also automatically prevents duplicate responses.

Statistical analysis

Descriptive statistics was used to provide an overview of the dataset. Depending on the nature of the variables the results are reported either as percentages or as medians with interquartile range (IQR). Comparison among percentages were performed with the chi-square test with a p-values significance threshold at 0.05. All statistical analyses were performed using Jamovi (Jamovi, Sydney, NSW Australia, Version 2.3) and GraphPad Prism (GraphPad Software, Boston, Massachusetts USA,Version 10).

A total of 4,302 email addresses for inclusion in the survey were collected from the list of journals in the appendix. Survey data were collected from 13 th July to 1 st September 2023. Following the initial email outreach and four subsequent reminders, 222 emails bounced back, and 142 recipients actively opted out of participating.Of those who opened the survey link, 266 respondents answered the initial questions. However, some immediately declined to continue, resulting in 236(5.5% of the emails sent) participants who started the survey and were included in the final analysis upon response.

The geographical distribution and demographic data of 229 respondents are depicted in Table 1 ,.The United States and the United Kingdom were most prominently represented, accounting for 57 (24.9%) and 41 (17.9%) of respondents, respectively. In total, English-speaking nations (USA, UK, Canada, and Australia) accounted for 124 (54.1%) of respondents.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0309208.t001

The role of 229 responders is represented in Fig 1 . Physicians, research academics and research clinicians were equally represented, with 64 (27.9%), 65 (28.4%) and 67 (29.2%) responders, respectively. The other responders declared not to be classified as the aforementioned and explained themselves mainly as journalists, students, veterinarians, editors, and pharmacists.

Proportion of respondents in various professional roles as a percentage of the total respondent pool.

https://doi.org/10.1371/journal.pone.0309208.g001

Most of the respondents to this question reported moderate 93 (40.6%) or little 60 (26.2%) familiarity with AI tools. Only 13 (5.7%) indicated extensive familiarity.Following questions up to Q14 were answered by all participants except for the 10 individuals (4.4%) who indicated no prior knowledge of AI (resulting in their automatic exclusion from answering those specific questions). Notably, 9 (69.2%)out of 13 with extensive familiarity reported AI tool usage, compared to lower rates among 20 out of 93 (21.5%)with moderate and 5 out of 60 (8.3%)minimal familiarity (p < 0.001).

More than half of 229 respondents (130, 55%) published their first medical article over 15 years ago, while 31 (13.5%) did so within the last five years. The median Scopus H-index among respondents was 24 (IQR 13–42). No statistically significant correlations were identified between H-index, AI familiarity and AI usage (p > 0.05).

Only 2 participants (< 1%), reported receiving specific training in AI for scientific production. Despite this, 55 (24.02%) out of 229 responders usedAI tools in scientific content creation.Of these, the majority (67.3%) used ChatGPT. Interestingly, among participants from the US(n = 57), a notable difference exists between those who have used AI for scientific production(n = 8, 14%) and those who have not (n = 49, 86%).Those who published the first medical article more than 15 years ago, also declared to have ever used AI tools for scientific production in a lesser amount than the ones who published the first medical article less than 15 years ago(23/130 [17.7%] vs. 32/99 [32.3%], p = 0.01).

As shown in Fig 2 , besides ChatGPT, among the 55 responders who have already published using the aid of AI during the scientific production,Microsoft Bing and Google Bard were used by 8 (14.5%) and 2 (3.6%) of respondents, respectively. Other large language models comprised 5.0% of the usage. Various software tools, including image creation and meta-analysis assistant tools, were also reported to be used by 7 (12.7%) and 6 (10.9%), respectively. Other AI tools reported are mainly Grammarly, Image Analysis tools, and plagiarism-checking tools.

The Y-axis lists the AI tools reported by respondents, while the X-axis shows their stated usage as a percentage. The total percentage exceeds 100% as respondents could report using multiple tools. LLM: Large Language Models; AI: Artificial Intelligence.

https://doi.org/10.1371/journal.pone.0309208.g002

When the 55 respondents who already used AI tools were asked about the primary applications of AI, 55.6% reported using AI for rephrasing text, 33.3% for translation, and 37.78% for proofreading. The rate of AI usage for language translation was consistent across English and non-English-speaking countries (94.4% vs 92.4%,p = 0.547). Additional applications such as draft writing, idea generation, and information synthesis were each noted by 24.4% of respondents.

In the survey, 8 of the 51 who answered this question (15.7%) admitted to using a chatbot for scientific work without acknowledgment.By contrast, 27 (11.9%)out of 226 are certain they will employ some form of Artificial Intelligence in future scientific production. The complete set of responses is summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0309208.t002

The primary challenges associated with utilizing AI in scientific research are outlined in Table 3 .

https://doi.org/10.1371/journal.pone.0309208.t003

The medical fields that respondents anticipate will gain the most from AI applications are Big Data Management and Automated Radiographic Report Generation. Additionalareas are detailed in Table 4 .

https://doi.org/10.1371/journal.pone.0309208.t004

When asked about their ability to distinguish between text written by a human and text generated by AI, 7 (3.1%) out of 226 respondents believed they could always tell the difference. Meanwhile, 120 (53.1%) felt they could only sometimes discern the difference. A total of 59 (26%)were uncertain, and a small fraction, 3 (1.3%), reported it is never possible to distinguish between the two.

Over 80% of respondents (n = 226) do not foresee AI supplanting the role of medical researchers in the future, with 81 (35.8%)strongly disagreeing and 106 (46.9%)disagreeing. A small fraction, 10 responders (4.4%), either somewhat or strongly agree that AI could take on the role of medical researchers. Meanwhile, 29 (12.8%)remain uncertain. By contrast, when it comes to the impact on clinical physicians,among the 226 responders to this last question, 177(78.3%) anticipate that AI will partially alter the nature of their work within the next two decades. A minority of 18 responders (8.0%) foresee no change at all, and a very small fraction, 2 (0.9%), predict a complete transformation in the role of clinical physicians. To conclude, 14 (6.0%)are still unsure about the future impact of AI on clinical practice.

The present study aimed to explore the perceptions and utilization of Artificial Intelligence (AI) tools in scientific production among corresponding authors who published in the 15 most-impacted factor medical journals in 2022.

Familiarity and training in AI

Intriguingly, this survey indicated that less than 1% of respondents had undergone formal training specifically designed for the application of AI in scientific research. This highlights a critical need for educational programs tailored to empower researchers with the necessary skills for effective AI utilization. The dearth of formal training may also contribute to the observed "limited" to "moderate" familiarity with AI concepts and tools among most survey participants, without a difference among ages and genders.Generally, AI tools are user-friendly and straightforward, requiring no specialized skills for basic usage. This could account for the lack of a significant difference between younger and older users.However, even though the basic use appears straightforward, a lack of comprehension may lead individuals to commit unnoticed errors with these tools, stemming from an unawareness of their own knowledge gaps [ 11 ].

Although beyond the primary focus of this study, we find it noteworthy to comment on the responses concerning the Scopus H-index. This score remains a subject of debate and is fraught with limitations, including self-citation biases, equal attribution regardless of author order and academic age,as well as gender-based disparities other than topic-specific biases. In our survey, the responders presented a median H-index of 24 (IQR 13–42), without statistically significant correlationsbetween H-index values and the variables of interest. Remarkably, two respondents indicated a lack of interest in monitoring their H-index. One respondent, a journal editor, expressed outright indifference with the remark "Who cares", probably echoing a sentiment that could be ascribed to Nobel Laureate Tu Youyou, whose current relatively low Scopus H-index of 16 belies her groundbreaking work on artemisinin, a treatment for malaria that has saved millions of lives.

Applications of AI in scientific production

The survey results underscore a paradoxical relationship between familiarity with AI concepts and its actual utilization in scientific production. While many respondents indicated a “limited” to “moderate” familiarity with AI, around 25% reported employing AI tools in their research endeavors. This suggests that while the theoretical understanding of AI might be limited among the surveyed population, its practical applications are cautiously being explored. It is plausible that the rapid advancements in AI, coupled with its increasing accessibility, have allowed researchers to experiment with these tools without necessarily delving deep into the underlying algorithms and principles.Notably, the preponderance of the surveyed gravitated toward ChatGPT, suggesting a proclivity for natural language processing applications. Indeed, ChatGPT could assist scientists in scientific production in several ways [ 12 ].

The principal tasks for which AI was employed encompassed rephrasing, translation, and proofreading functions. AI tools, especially natural language processing models like ChatGPT, can significantly improve the fluency and coherence of scientific texts, especially for non-native English speakers. This is crucial in the globalized world of scientific research, where effective communication can determine the reach and impact of a study. Interestingly, the rates of AI use for language translation were quite similar between English-speaking and non-English-speaking countries, at 94.4% and 92.4%, respectively. This is unexpected since English is often the preferred language for communication in scientific fields, diminishing the perceived need for translation tools. Several factors could explain this trend. First, these countries have a high proportion of expatriates, leading to many non-native English speakers in the workforce. One limitation of our study is that we did not inquire about the respondents’ countries of origin, so we cannot provide further insights. Another possible explanation could be the selectivity of our respondent pool, which may not be sufficiently representative to show a difference in this variable.Nevertheless, ifthe predominant use of AI for tasks such as rephrasing, translation, and proofreading underscores its potential to enhance the quality of research output, it is essential to strike a balance to ensure that the essence and originality of the research are maintained in the pursuit of linguistic perfection.

This pattern intimates that, in its current stage, AI is predominantly perceived as a facilitator for enhancing the textual quality of scholarly work, rather than as an instrument for novel research ideation or data analysis. In response to this evolving landscape, academic journals, for example, JAMA and Nature, have issued guidelines concerning the judicious use of large language models (LLMs) and generative chatbots [ 13 , 14 ]. Such guidelines often stipulate authors’ need to disclose any AI-generated content explicitly, including the specification of the AI model or tool deployed.

While the survey highlighted the use of LLMs predominantly in textual enhancements, the potential of other AI in data analysis still needs to be explored among the respondents. Indeed, LLM and NLP, in general, currently have a very weak theoretical basis for data prediction.Nevertheless, longitudinal electronic health record (EHR) data have been effectively tokenized and modeled using transformer approaches, to integrate different patient measurements, as reported in the field of Intensive Care Medicine [ 15 ], even if this field is still insufficiently explored. Advanced AI algorithms can process vast datasets, identify patterns, and even accurately predict future trends, often beyond human capabilities. For instance, in biomedical research, numerous machine learning applications tailored to specific tasks or domains can assist in analyzing complex genomic data, predicting disease outbreaks, or modeling the effects of potential drugs. As indicated by the survey, the limited utilization of AI in these areas may be due to the lack of specialized training or apprehensions about the reliability of AI-generated insights.

Future prospects

Most respondents were optimistic about the future role of AI in scientific production, with nearly 12% stating they would "surely" use AI in the future. This optimism towards integrating AI in scientific production can be attributed to the numerous advancements and breakthroughs in AI in recent years. As AI models become more sophisticated, their potential applications in research expand, ranging from data analysis and visualization to hypothesis generation and experimental design. The increasing availability of open-source AI tools and platforms makes it more accessible for researchers to incorporate AI into their work, even without extensive technical expertise.

However, most respondents (> 80%) did not believe that AI would replace medical researchers, suggesting a balanced view that AI will serve as a complementary tool rather than a replacement for human expertise. The sentiment that AI will augment rather than replace human expertise aligns with the broader perspective in the AI community, often termed “augmented intelligence” [ 16 ]. This perspective emphasizes the synergy between human intuition and AI’s computational capabilities. While AI can handle vast amounts of data and rapidly perform complex calculations, human researchers bring domain expertise, critical thinking, and ethical considerations [ 17 ]. This combination can lead to more robust and comprehensive research outcomes [ 16 , 18 ].

Moreover, the evolving landscape of AI in research also presents opportunities for interdisciplinary collaboration [ 19 ]. As AI becomes more integrated into scientific research, there will be a growing need for collaboration between AI specialists and domain experts. Such collaborations can ensure that AI tools are developed and applied in contextually relevant and scientifically rigorous ways. This interdisciplinary approach can lead to novel insights and innovative solutions to complex research challenges.

Ethical and technical concerns

This survey identified a wide range of concerns regarding the integration of Artificial Intelligence (AI) into the realm of scientific research. Among these, content inaccuracies emerged as the most salient, flagged by over 80% of respondents. The risks associated with AI-generated content include creating ostensibly accurate but factually erroneous data, such as fabricated bibliographic references, a phenomenon described as "Artificial Intelligence Hallucinations"[ 20 ]. It has already been proposed that the Dunning-Kruger effect serves as a pertinent framework to consider the actual vs. the perceived competencies that exist regarding the application of AI in research [ 21 ]. Furthermore,the attitudes and expectations surrounding such technologies, just one year following the release of OpenAI’s ChatGPT, can be aptly illustrated by the Gartner Hype Cycle [ 22 ]. Consequently, it is imperative that content generated by AI algorithms, even translations, undergo rigorous validation by subject matter experts.

Moreover, the rapid evolution of AI models, especially deep learning architectures, has created ’black box’ systems where the decision-making process is not transparent [ 23 ]. This opacity can further exacerbate researchers’ trust issues towards AI-generated content. The lack of interpretability can hinder the widespread adoption of AI in scientific research, as researchers might be hesitant to rely on tools they need to understand fully. Efforts are being made in the AI community to develop more interpretable and explainable AI models, but the balance between performance and transparency remains a challenge [ 24 ].

Beyond the ethical implications, another emerging concern is the potential for AI to perpetuate existing biases in the training data or continue "citogenesis"[ 25 ], which represents an insidious form of error propagation within the scientific corpus [ 26 ]. If AI models are trained on biased datasets, they can produce skewed or discriminatory results, leading to flawed conclusions and the perpetuation of systemic inequalities in research. This is particularly concerning in social sciences and medicine, where biased conclusions can have far-reaching implications [ 27 ]. For this reason, researchers must be aware of these pitfalls and advocate for the usage of data that is as unbiased and representative as possible in training AI models. The full spectrum of potential negative outcomes remains largely unquantified. Furthermore, using AI complicates the attribution of accountability, particularly in clinical settings. Ethical concerns, echoed by most of our respondents, coexist with legal considerations [ 28 ].

Additionally, integrating AI into scientific research raises data privacy and security questions [ 29 ]. As AI models often require vast amounts of data for continued training,there is the risk of submitted sensitive information being unintentionally exposed or misused during the process.This is one of the main reasons why several AI companies recently came out with enterprise and on-premise software versions.Such measures are especially pertinent in medical research, where patient data confidentiality is paramount [ 23 , 30 ]. Ensuring robust data encryption and adhering to stringent data handling protocols becomes crucial when incorporating AI into the research workflow.

Various policy options have been tabled to govern the use of AI in the production and editing of scholarly texts. These range from a complete prohibition on using AI-generated content in academic manuscripts to mandates for clear disclosure of AI contributions within the text and reference sections [ 31 ]. Notably, accrediting AI systems as authors appear to be universally rejected.Given these challenges, the concerns identified are legitimate and necessitate comprehensive investigation, particularly as AI technologies continue to advance and diversify in application.

A collaborative approach that includes AI experts, ethicists, policymakers, and researchers is crucial to manage the ethical and technical complexities and fully leverage AI in a responsible and effective manner. Furthermore, it is advisable for journal editors to establish clear guidelines for AI use, as some have already begun [ 14 ], including mandating the disclosure of AI involvement in the research process. Strict policies should be implemented to safeguard the data utilized by AI systems. Human oversight is necessary to interpret the data and results produced by AI. Additionally, an independent group should assess the impact of AI on research outcomes and ethical issues.

Lastly, attention must be paid to the energy consumption of AI systems and their consequent carbon footprint, which can be considerable, especially in the case of large-scale computational models [ 32 ]. AI and machine learning models, particularly those utilizing deep learning, require extensive computational resources and use significant amounts of electricity. To minimize this footprint, researchers should focus on optimizing AI algorithms to increase their energy efficiency and employ these systems only when absolutely necessary. It is essential for researchers to consider the environmental impact of their AI usage, treating ecological sustainability as a critical factor in today’s world.

Future in healthcare

The advent of AI in healthcare is rapidly evolving, and our responders anticipate Big Data Management [ 33 ] and Automated Radiographic Report Generation [ 34 ] to be the most impactful areas influenced by AI applications in the next few years. These results underline the growing recognition of AI’s transformative potential in these domains [ 35 ]. Indeed, the current healthcare landscape generates massive amounts of data from diverse sources, including electronic health records, diagnostic tests, and patient monitoring systems [ 36 ]. AI-powered analytics tools could revolutionize how we understand and interpret this data, thus aiding in more accurate diagnosis and personalized treatment protocols. Similarly, medical imaging studies require considerable time and expertise for interpretation, representing a potential bottleneck in clinical workflow. Automated systems powered by AI can analyze images and rapidly generate reports with a speed and consistency that could vastly improve throughput and possibly contribute to improved patient outcomes, bolstering the assumption that AI-assisted radiologists work better and faster [ 37 ]. By contrast, these systems have been demonstrated to generate more incorrect positive results compared to radiology reports, especially when dealing with multiple or smaller-sized target findings [ 38 ]. Despite these and other limitations such as privacy security concerns, computer-aided diagnosis is promising and could impact several specialties [ 39 ]. In the market, there are already various user-friendly and easy-to-use mobile apps available, designed for healthcare professionals as well as patients, that offer quick access to artificial intelligence tools for obtaining potential diagnoses.Nevertheless, AI currently lacks the precision and capability to make clinical diagnoses, and thus cannot be a substitute for a doctor.

Finally, the development of AI in diagnosis and drug development was also highly rated in the survey. These results mirror current research trends, where AI has been applied for early disease detection and drug discovery processes, significantly cutting down time and costs. Even so, the essential human interaction between patient and clinician remains a core aspect of medical care, making it unlikely that AI will soon replace the need for in-person connection [ 40 ]. Our survey respondents echo this sentiment, as the majority believe clinical doctors will only be partially replaced by technological advancements. Interestingly, in the open-ended responses, among the others, we found this comment “Humans do not want an AI-doctor”. Even though literature tells us that AI could be more empathetic than human doctors [ 41 ], for the moment, everyone agrees.

Limitations

While this study provides valuable insights into the understanding and utilization of Artificial Intelligence (AI) in scientific research, there are some noteworthy limitations. First, the study sample focuses exclusively on corresponding authors from high-impact medical journals. Although this allows us to capture perspectives from researchers at the forefront of scientific advancements, it may limit the generalizability of our findings to the broader scientific and medical community, including early-career researchers and students. Future surveys should aim to include a more diverse range of participants for a fuller picture.

Second, the survey had a low response rate. Physicians are generally challenging to be involved in survey research, and web-based surveys often yield lower participation rates [ 42 ]. Additionally, the accuracy of the email addresses is not guaranteed in email surveys, as evidenced by the emails that were bounced back, likely due to outdated or incorrect institutional email addresses. Nevertheless, although we didn’t conduct an a priori sample size calculation, our aim was to collect responses from at least 300 participants to obtain a substantial perspective on the subject.

Third, the data was gathered through an online survey, which might introduce selection bias as those who are more comfortable with technology and AI may have been more inclined to participate.

Fourth, there was no verification process for the authenticity of the email addresses used in our study, which leaves room for potential inaccuracies in the data collected.

Conclusions

This survey revealed varying degrees of familiarity with AI tools among researchers, with many in high-impact journals beginning to integrate AI into their work. The majority of respondents were from the USA and UK, with 54.1% from English-speaking countries. Only 5.7% indicated extensive familiarity with AI, and 24% used AI tools in scientific content creation, predominantly ChatGPT. Despite low training rates in AI (less than 1%), its use is gradually becoming more prevalent in scientific research and authorship.

Supporting information

S1 appendix. survey questionnaire..

https://doi.org/10.1371/journal.pone.0309208.s001

S2 Appendix. List of the leading 15 medical journals by impact factor.

https://doi.org/10.1371/journal.pone.0309208.s002

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• 25. 978: Citogenesis ‐ explain xkcd n.d. https://www.explainxkcd.com/wiki/index.php/978:_Citogenesis (accessed September 3, 2023).

Best AI Tools For Writing Research Papers for 2024

• August 22, 2024 Updated

The future of research writing is here, driven by AI technology that combines precision and efficiency like never before. With the best AI Tools for Writing Research Papers, you can elevate your work to new heights, ensuring that every word is meticulously crafted and every argument is clearly articulated.

Choosing the right AI writing tool is crucial for producing high-quality research papers that meet academic standards . These tools enhance productivity and make sure your work is original, well-structured, and free from errors .

Over the years, AI writing tools have significantly evolved . What started as simple grammar checkers have become sophisticated platforms offering content generation, style optimization, and citation management, making the research writing process more streamlined .

Whether looking for a tool to refine your writing or seeking advanced features for managing citations and references , the best AI writing tools for 2024 provide various options to suit your academic needs . Let’s explore these tools and how they can elevate your research writing.

Best AI Tools For Writing Research Papers: Quick Comparison

In this section, I’ll cover the best AI writing tools designed specifically for Writing Research Papers . These tools are essential for enhancing academic writing by offering features such as grammar checks, content generation, and citation management, ensuring that your research paper is polished and professional.

To help you choose the right tool, I’ve created a quick comparison table that outlines the key features and pricing of the top AI tools available. Depending on your needs, you might find that one of these options is also among the best AI tools in 2024 available for various writing tasks.

Best AI Tools For Writing Research Papers: In-Depth Analysis

Let’s take a closer look at the best AI tools available for Writing Research Papers . Each tool offers unique features that cater to different aspects of academic writing, from grammar and style to content generation and citation management.

1. Jenni AI : Your Go-To Tool for Research Writing

Jenni AI is a powerful writing assistant designed to streamline research paper writing . With features like AI Autocomplete , in-text citations , and the ability to generate content from various sources, Jenni AI enables you to create well-structured and comprehensive research papers efficiently.

One of the standout features of Jenni AI is its support for five languages , making it accessible to a global audience.

Additionally, the tool allows you to produce 200+ words per day , which is ideal for consistent progress on lengthy academic projects. With 5+ user seats available, Jenni AI is perfect for collaborative research efforts, enabling multiple users to contribute to a single project.

While Jenni AI excels in areas such as multilingual support and ease of use , it does lack a plagiarism checker , which is an important feature for ensuring the originality of academic work. However, its intuitive interface and mobile compatibility make it easy to use, whether you’re working from a desktop or on the go.

Jenni AI shines with its AI Autocomplete and multilingual support , making it an excellent choice for researchers and students alike. However, the absence of a plagiarism checker means you might need to use additional tools to ensure your work is free from unintentional duplication.

• Intuitive and user-friendly interface.
• Strong AI Autocomplete and in-text citation features.
• Supports multiple languages.
• Lacks a built-in plagiarism checker.
• Limited template options.
• Some advanced features are not applicable.

Customer Support and Refund Policy

Jenni AI offers robust customer support, which is available through various channels, including email and live chat.

The tool also provides a refund policy, though specific details on coverage days are not provided. If you’re considering Jenni AI , it’s worth reading a more detailed Jenni AI review to understand its full capabilities.

2. Paperpal: My Go-To Tool for Research Manuscript Writing

Paperpal is an AI-powered tool explicitly designed for article writing and manuscript writing . It leverages Artificial Intelligence (AI) , Machine Learning (ML) , and Natural Language Processing (NLP) to provide comprehensive writing assistance, from grammar checks to plagiarism detection.

Paperpal is particularly useful for researchers and authors who need to ensure their manuscripts meet the highest standards of academic writing.

From the moment you log in, Paperpal is intuitive and straightforward to use. It offers a seamless integration with MS Word through an add-in, allowing you to access its features directly within your document.

The tool supports over 25 languages and provides 7,000 words per month of plagiarism-checked content, making it a versatile option for multilingual writers.

However, while Paperpal excels in language support and data security, its limited word count for the free version and lack of detailed template options may be drawbacks for some users.

Paperpal shines with its AI-driven capabilities, offering robust grammar checks and language support. This makes it a great choice for academic and professional writers.

However, the tool is rated slightly lower due to its limited word count in the free version and the absence of extensive template options.

• Strong integration with MS Word.
• Supports over 25 languages, making it versatile for multilingual writing.
• Offers robust grammar and plagiarism checks.
• Limited word count for the free version.
• Lack of detailed templates.
• No clear specification on the number of user seats.

Paperpal offers customer support via email, ensuring users can get assistance when needed. The tool also provides a money-back guarantee , with 30 days coverage for annual subscriptions and 7 days for monthly subscriptions.

For a deeper insight into Paperpal’s capabilities, consider checking out a comprehensive Paperpal review .

3. QuillBot : My Essential Tool for Paraphrasing and Research Writing

QuillBot has become my go-to tool when paraphrasing text or refining my research writing . It’s particularly effective for rephrasing complex ideas, which is essential when avoiding plagiarism while still accurately conveying my research findings.

Whether I’m working on articles or longer academic papers, QuillBot’s range of features helps me maintain clarity and precision.

What I appreciate most about QuillBot is its easy use, from logging in to exploring its various features. It integrates smoothly with Google Docs , Microsoft Word , and MacOS , so I can work across different platforms without hassle.

The tool supports 30 languages and offers unlimited word count, which is useful when working on large research projects.

However, while QuillBot excels in paraphrasing and grammar checking, it doesn’t offer advanced templates, which could be a downside if you need more structured content creation.

QuillBot is invaluable for paraphrasing and ensuring the accuracy of my research writing. However, the lack of advanced templates and a short refund period may be limiting for some users.

• Excellent paraphrasing and grammar-checking features.
• Works well with Google Docs, Microsoft Word, and MacOS.
• It supports 30 languages, making it versatile for multilingual research.
• Doesn’t offer advanced templates for structured academic writing.
• Customer support is limited to website messaging.
• Short refund period of only 3 days.

QuillBot offers customer support through messages on their website, which is convenient for quick inquiries.

They also provide a 100% money-back guarantee , but the refund period is only 3 days , so you must decide quickly if it’s the right tool. For a more detailed exploration of QuillBot’s features, I suggest checking out a full QuillBot review .

4. Anyword: Your Precision Tool for Research and Academic Writing

Anyword is a powerful AI-driven writing assistant designed to support various writing needs, particularly research writing , academic content creation , and legal writing .

What makes Anyword especially useful for research writing is its predictive performance model , which offers insights into how effectively your academic content will resonate with its intended audience, ensuring your work makes the desired impact.

When I began using Anyword for my research writing, I was immediately impressed by its user-friendly interface. The tool simplifies the writing process from when you log in, offering seamless navigation through its features.

With support for 30+ languages and 100+ templates , Anyword is versatile enough to handle complex research documents, ensuring they are both clear and compelling.

The integration with Google Chrome is particularly handy, allowing me to draft and refine my research papers directly in the browser.

However, while Anyword excels in predictive content performance and template variety, its higher pricing tiers and the absence of refunds for canceled subscriptions may limit some users, particularly those in academia, on a budget.

Anyword excels in providing content analytics and predictive performance , making it an excellent choice for researchers and academics who require data-driven insights to enhance the effectiveness of their writing.

However, the tool’s lack of a refund policy and the higher costs associated with advanced plans may be a concern for budget-conscious users in academia.

• Predictive performance scores provide valuable insights for optimizing research papers.
• It offers a wide range of templates and supports over 30 languages , which is useful for multilingual research.
• Smooth integration with Google Chrome for efficient browser-based writing.
• Higher pricing tiers , particularly for small research teams or students, can be expensive.
• There are no refunds for canceled subscriptions, which might be a drawback for some users.
• Advanced features like custom-built AI models are only available in the business plan .

Anyword offers customer support via email, which I find effective for quickly resolving issues during my research writing. However, it’s important to note that they do not offer refunds for canceled subscriptions, so it’s wise to fully assess your needs before committing to a plan.

For a more detailed comparison with other AI writing tools, I recommend reading a thorough Anyword review to see if it aligns with your research writing requirements.

5. Bit AI: The Ultimate Tool for Research Document Collaboration

But AI is cloud-based document collaboration and knowledge management software , making it ideal for researchers and academic teams that need to create, share, and track their research documents in real time .

It streamlines document collaboration, enhances knowledge sharing , and significantly boosts productivity , making it indispensable for teams working on research papers , proposals , and academic reports .

When I first started using Bit AI for my research writing, I was immediately impressed by its intuitive interface and ease of use .

The tool integrates seamlessly with over 100 popular applications , which allows me to embed various types of digital content directly into my documents.

This feature is incredibly useful for creating rich, interactive research papers that go beyond text to include images, videos, and other media.

I also appreciate Bit AI’s mobile compatibility , which enables me to manage my research documents on the go.

However, while Bit AI excels in collaboration features and integration , the limitations of the free plan and the lack of advanced grammar and plagiarism checks might be drawbacks for some researchers.

Bit AI is particularly strong in document collaboration and knowledge management , making it a great choice for academic teams that require real-time collaboration on research papers. However, its limited free plan and the absence of specified grammar and plagiarism detection features may be restrictive for researchers who need comprehensive writing tools.

• Extensive integration with over 100 applications enhances document functionality, ideal for embedding research data and media.
• It offers comprehensive knowledge management and collaboration tools that are perfect for managing complex research projects.
• Mobile compatibility allows for document management on the go, ensuring you can keep up with your research tasks anywhere.
• The free plan is limited , especially for larger research teams needing more advanced features.
• Lacks specified grammar check and plagiarism detection features, which are crucial for academic integrity.
• There are no refunds for canceled subscriptions, which could be a drawback for some researchers.

Bit AI offers customer support via phone, email, and live chat, which I find very responsive and helpful, particularly when managing complex research documents.

However, it’s important to note that Bit AI does not offer refunds for canceled subscriptions, so it’s essential to consider this before committing to a paid plan.

6. Writesonic : Your Go-To AI for Research Writing and Content Creation

Writesonic is a versatile AI-powered writing assistant designed to enhance research writing and content creation efficiency and quality .

Whether you’re brainstorming article ideas , crafting concise summaries , or generating SEO meta-descriptions for your research publications, Writesonic provides various tools tailored to meet your academic needs.

When I first started using Writesonic , I found the platform incredibly intuitive and easy to navigate.

The user experience is seamless, from logging in to exploring its features. The tool supports 24+ languages and offers access to over 100+ templates , making it ideal for creating structured and polished research documents.

Additionally, Writesonic’s integration with platforms like Google Docs and WordPress simplifies incorporating research content into various formats.

However, while Writesonic excels in generating high-quality content, some users might find the limited credits in the free plan restrictive, especially when working on extensive research projects.

Writesonic is particularly strong in content generation and language support , making it an excellent tool for researchers who must produce well-structured and engaging academic content.

However, the limited credits in the free plan and the need for a premium plan to access unlimited features may be a consideration for those working on larger projects.

The best AI for writing research papers helps streamline the writing process with advanced features.

• Extensive template library with over 100 templates, perfect for various research writing tasks.
• It supports 24+ languages , making it suitable for multilingual research teams.
• Seamless integration with popular platforms like Google Docs and WordPress.
• Limited credits in the free plan may restrict usage for extensive research projects.
• Premium plans require unlimited access to word count and other advanced features.
• Some advanced features, such as brand voice , are better suited for marketing than academic writing.

Writesonic offers 24/7 customer support via live chat, which I find particularly helpful when working on research projects that require quick assistance.

They also offer a 100% refund within 7 days , which provides peace of mind when trying out the platform.

If you want to explore how Writesonic compares to other AI writing tools for research, consider reading a comprehensive Writesonic review to see if it fits your academic needs.

7. Scalenut: Elevate Your Research Writing with AI-Driven Content Optimization

Scalenut is a comprehensive AI-driven writing assistant that excels at research writing , especially content planning , SEO optimization , and content creation .

Designed to help researchers create impactful and optimized content, Scalenut’s capabilities extend beyond writing. It also offers tools to research SEO topics and analyze traffic , making it an all-in-one platform for academic and research professionals aiming to increase their online visibility.

From the moment I started using Scalenut , I was struck by how user-friendly it is.

The platform makes it easy to log in and craft well-structured research content with its intuitive interface.

One of the standout features is its deep learning model and NLP-generated key terms , significantly enhancing the quality and relevance of your research writing.

Moreover, Scalenut’s integration with WordPress ensures you can directly publish your optimized content without any hassles.

However, while Scalenut offers extensive features, its English-only language support and potential data-sharing practices might be limiting factors for some researchers.

Scalenut is particularly effective in content optimization and SEO-driven research writing , making it a top choice for researchers who want to enhance the reach and impact of their work. However, the tool’s English-only support and potential data-sharing practices may not suit everyone, particularly those working in multilingual or highly confidential research environments.

• AI-driven content optimization with deep learning models enhances the quality and reach of research papers.
• It provides over 40+ templates for various writing tasks, which is ideal for structured academic writing.
• Integration with WordPress allows for seamless publishing of research content.
• English-only language support may be limiting for non-English researchers.
• Potential data-sharing practices might raise concerns for those handling sensitive research data.
• Customer support options are not clearly provided , which could be a drawback if you need immediate assistance.

Scalenut offers a 7-day free trial for users to explore its features, which I found useful for getting acquainted with the platform. However, the tool does not clearly provide customer support options, and there is no refund policy mentioned, so it’s important to consider these factors before subscribing.

For a more detailed comparison with other AI writing tools, I recommend reading a comprehensive Scalenut review to see if it aligns with your research writing needs.

8. ChatGPT: Your Essential AI for Research Writing and Content Generation

ChatGPT , developed by OpenAI, is a powerful AI-driven writing assistant that has quickly become a go-to tool for research writing and content generation .

Whether you’re drafting complex research papers, summarizing intricate topics, or translating documents, ChatGPT offers a versatile platform that can significantly enhance your academic work.

With the ability to handle large language models (LLMs) , ChatGPT is particularly adept at generating high-quality content on diverse and complex topics, making it an invaluable tool for researchers.

When I started using ChatGPT for my research writing, I found the platform incredibly intuitive and easy to navigate.

The tool supports 50+ languages , making it highly adaptable to various research tasks. The mobile compatibility on iOS and Android further ensures that I can work on my research projects anywhere, anytime.

However, while ChatGPT excels in generating and summarizing content, the lack of a built-in plagiarism check and limited advanced grammar features may require additional tools to ensure the academic integrity of your work.

ChatGPT stands out for its ability to handle complex research topics and generate comprehensive content, making it a top choice for academics and researchers.

However, the tool’s lack of integrated plagiarism detection and the absence of refunds could be a consideration for those needing these specific features.

These Best AI tools for research in 2024 provide top-notch support for academic work.

• Extensive language support with over 50 languages, making it suitable for multilingual research.
• Access on multiple platforms (web, iOS, Android), ensuring flexibility in research writing.
• Offers a vast array of templates and tools , including access to GPT-4 and other advanced AI capabilities.
• No built-in plagiarism checker , which may require the use of additional tools for academic integrity.
• Limited advanced grammar features , making it less comprehensive compared to dedicated grammar tools.
• No refunds for subscriptions, which could be a drawback for some users.

ChatGPT offers customer support via live chat, which I find helpful for quick queries and troubleshooting.

However, it’s important to note that ChatGPT does not offer refunds for canceled subscriptions, so it’s crucial to evaluate the tool’s features thoroughly during the free trial.

To dive deeper into how ChatGPT compares to other AI tools, consider reading a comprehensive ChatGPT review to determine if it fits your research writing needs.

9. Research Rabbit: Your Go-To Tool for Efficient Research and Literature Mapping

Research Rabbit is a unique, citation-based literature mapping tool designed to optimize your time when searching for references, whether you’re starting an essay, a minor project, or a comprehensive literature review.

This free tool is invaluable for researchers who want to streamline their research process by finding relevant papers quickly and efficiently.

When I first used Research Rabbit for my research writing, I was impressed by how it simplifies the literature review process.

The tool’s ability to search for relevant literature based on a “seed paper” concept is a game-changer. It allows me to input one or more research papers and receive suggestions for additional relevant papers.

This eliminates the need to switch between various databases, making the research process faster and more efficient.

Additionally, Research Rabbit is continuously evolving, with a mission-first team dedicated to advancing human knowledge.

The platform is easy to navigate, but it is primarily focused on literature mapping and lacks more advanced content generation features like those found in ChatGPT .

Research Rabbit excels in citation-based literature mapping , making it a must-have tool for any researcher looking to optimize their literature review process.

However, it is specialized in this area and does not offer broader content generation or editing features, which could limit its utility for those needing a more comprehensive tool like ChatGPT .

Free AI tools for research papers writing offer valuable resources for generating high-quality content.

• Free access to a powerful citation-based literature mapping tool, making it accessible to all researchers.
• Efficient literature searching through the “seed paper” concept saves significant time during the research process.
• The mission-first team focused on advancing human knowledge and continuously improving the tool.
• Limited to literature mapping , lacking broader content creation or editing features.
• English-only support may limit its use for non-English research materials.
• Does not offer advanced integrations or additional research tools in more comprehensive platforms.

Research Rabbit offers customer support via email at [email protected] , which I found responsive and helpful. As the tool is completely free, there is no refund policy, which is understandable given its no-cost model.

To complement your research toolkit with broader capabilities, consider exploring other tools like ChatGPT to see how they can work alongside Research Rabbit.

10. Perplexity AI: Your Real-Time Research Assistant for Accurate Information Retrieval

Perplexity AI is a cutting-edge AI-powered research assistant designed to provide real-time information retrieval and fact-checking, making it an ideal tool for research writing .

Whether you’re conducting in-depth research on complex topics or need quick access to accurate and up-to-date information, Perplexity AI’s real-time web search and transparent source citation capabilities ensure that you get the most relevant and trustworthy results for your research projects.

When I began using Perplexity AI for my research writing, I was immediately struck by how efficient and user-friendly the platform is.

The Perplexity Copilot feature integrates real-time web searches with transparent citations, providing reliable sources for every answer, which is crucial for academic integrity.

The interface is clean and intuitive, allowing me to focus on my research without distractions.

Perplexity AI is particularly strong in real-time information retrieval and source transparency , making it an excellent tool for researchers who need accurate and trustworthy data quickly.

However, the platform’s lack of advanced grammar or plagiarism checking features and limited information on data security may require users to complement it with other tools for a more comprehensive research writing process.

• Real-time web search provides up-to-date information crucial for current research topics.
• Transparent source citation enhances academic integrity, ensuring that all information is traceable.
• A User-friendly interface allows for a distraction-free research experience.
• No built-in plagiarism or grammar checks , which may require additional tools.
• Limited data security details which could be a concern for sensitive research.
• The Professional plan may be necessary for more advanced features, which adds to the cost.

Perplexity AI offers customer support via email at [email protected] , which is responsive and helpful for addressing user inquiries.

The platform provides a 24-hour window for refunds after purchase, with a full refund available within 72 hours for annual subscriptions, making it relatively risk-free to try.

For a more detailed comparison with other research tools, consider exploring a comprehensive Perplexity AI review to see how it fits into your research writing workflow.

How To Choose The Best AI Tools For Writing Research Papers?

When I set out to find the best AI tools for writing research papers, I focused on several key factors:

• Content Accuracy and Credibility : The tool’s ability to generate well-researched and accurate content is crucial for producing credible research papers.
• Language Capabilities : It was essential to choose AI tools that support a wide range of languages, ensuring they can assist with research papers in different regional contexts.
• Integration Capabilities : I looked for AI tools that seamlessly integrate with platforms like Google Docs or WordPress, enhancing the writing process and allowing for smooth transitions between the writing and editing stages.
• Data Security and Privacy : Ensuring the AI tool complies with data protection standards is vital, particularly for researchers handling sensitive academic work, especially in regions with strict data privacy regulations.

This detailed guide on artificial intelligence provides more insights into how I evaluate AI tools and what factors to consider. Moreover, the best AI tool for writing research papers in 2024 meets the demands of modern academic writing.

How do AI Writing Tools Help Write Research Papers?

In my experience, AI writing tools have greatly enhanced my ability to produce high-quality research papers by offering several key benefits:

• Efficiency : AI tools speed up tasks like organizing references and generating literature reviews, allowing more time for analysis and critical thinking.
• Accuracy : These tools improve grammar, sentence structure, and clarity by leveraging natural language processing (NLP), ensuring professional-quality writing.
• Data Analysis : AI can quickly scan and summarize large volumes of data, making it easier to identify trends and build stronger research arguments.
• Collaboration : Many AI tools support real-time collaboration, enabling seamless teamwork across different regions, especially useful for international research projects.

For more on how AI tools enhance research writing, understanding the basics of NLP (Natural Language Processing) can provide deeper insights into these advancements.

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Can I use AI to write my research paper?

What is the best ai for summarizing research papers, is there a completely free ai writing tool, can professors detect ai-written papers, can chatgpt summarize a research paper.

Choosing the best AI Tools for writing Research Papers involves considering content accuracy, language capabilities, integration options, and data security. AI tools like ChatGPT, Quillbot, and Research Rabbit offer unique features that cater to different aspects of the research writing process, making them valuable assets for academics.

You should explore these AI writing tools to enhance your content writing projects, whether you’re working on a research paper, summarizing literature, or managing complex academic tasks. By integrating these tools into your workflow, you can improve both the efficiency and quality of your writing.

Digital marketing enthusiast by day, nature wanderer by dusk. Dave Andre blends two decades of AI and SaaS expertise into impactful strategies for SMEs. His weekends? Lost in books on tech trends and rejuvenating on scenic trails.

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