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

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

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

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

What is Elicit not a good fit for?

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

Extract key information from research papers with our AI summarizer.

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Scholarcy’s ai summarization tool is designed to generate accurate, reliable article summaries..

Our summarizer tool is trained to identify key terms, claims, and findings in academic papers. These insights are turned into digestible Summary Flashcards.

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The knowledge extraction and summarization methods we use focus on accuracy. This ensures what you read is factually correct, and can always be traced back to the original source .

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With Scholarcy Library you can import unlimited documents and generate summaries for all your course materials or collection of research papers.

Scholarcy Library offers additional features including access to millions of academic research papers, customizable summaries, direct import from Zotero and more.

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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.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

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Table Of Contents

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

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

1. Paperguide

Researchers and students often struggle with dense PDFs packed with essential information. Paperguide is a versatile research platform that simplifies this process, offering powerful tools for extracting key insights, managing references, and annotating documents.

For researchers, Paperguide transforms document interaction by enabling quick identification of specific data, streamlining research efforts, and efficiently managing references. This allows researchers to spend less time navigating documents and more time on analysis.

In academics, Paperguide is invaluable for students and educators alike. It enhances learning by offering an interactive way to query PDFs on complex concepts, definitions, or theories, while also allowing for easy annotation and note-taking. This makes studying more efficient and engaging, helping students better organize their thoughts and deepen their understanding of the material.

Paperguide also serves as a critical tool in academic research, helping scholars deeply engage with their materials and efficiently manage citations, freeing up more time for generating insights.

Key Benefits of Paperguide:

• Speeds up data extraction and provides insightful analysis for researchers.
• Manages references seamlessly, improving citation organization.
• Enhances study sessions with tools for annotating and taking notes within PDFs.
• Offers an interactive platform for querying complex academic concepts.
• Supports efficient note preparation and deepens understanding of course content.
• Facilitates more effective research with a focus on critical analysis.

Visit Paperguide →

2. Tenorshare AI PDF Tool

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

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

Key features of Tenorshare AI PDF Tool:

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

Visit Tenorshare →

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

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

Key features of Elicit:

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

Visit Elicit →

4. QuillBot

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

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

Key features of QuillBot's Summarizer:

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

Visit Quillbot →

5. Semantic Scholar

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

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

Key features of Semantic Scholar:

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

Visit Semantic Scholar →

6. IBM Watson Discovery

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

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

Key features of IBM Watson Discovery:

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

Visit IBM Watson Discovery →

Empowering Researchers with AI-Driven Summarization Tools

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

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

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

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A Guide to Using AI Tools to Summarize Literature Reviews

Table of Contents

Needless to say, millions of scientific articles are getting published every year making it difficult for a researcher to read and comprehend all the relevant publications.

Back then, researchers used to manually conduct literature reviews by sifting through hundreds of research papers to get the significant information required for the research.

Fast forward to 2023 — things have turned out quite distinct and favorable. With the inception of AI tools, the literature review process is streamlined and researchers can summarize hundreds of research articles in mere moments. They can save time and effort by using AI tools to summarize literature reviews.

This article articulates the role of the top AI tools used to summarize literature reviews. You can also learn how AI is used as a powerful tool for summarizing scientific articles and understanding the impact of AI on academic research.

Understanding the Role of AI Tools in Literature Reviews

Before we talk about the benefits of AI tools to summarize literature reviews, let’s understand the concept of AI and how it streamlines the literature review process.

Artificial intelligence tools are trained on large language models and they are programmed to mimic human tasks like problem-solving, making decisions, understanding patterns, and more. When Artificial Intelligence and machine learning algorithms are implemented in literature reviews, they help in processing vast amounts of information, identifying highly relevant studies, and generating quick and concise summaries — TL;DR summaries.

AI has revolutionized the process of literature review by assisting researchers with powerful AI-based tools to read, analyze, compare, contrast, and extract relevant information from research articles.

By using natural language processing algorithms, AI tools can effectively identify key concepts, main arguments, and relevant findings from multiple research articles at once. This assists researchers in quickly understanding the overview of the existing literature on a respective topic, saving their valuable time and effort.

Key Benefits of Using AI Tools to Summarize Literature Review

1. best alternative to traditional literature review.

Traditional literature reviews or manual literature reviews can be incredibly time-consuming and often require weeks or even months to complete. Researchers have to sift through myriad articles manually, read them in detail, and highlight or extract relevant information. This process can be overwhelming, especially when dealing with a large number of studies.

However, with the help of AI tools, researchers can greatly save time and effort required to discover, analyze, and summarize relevant studies. AI tools with their NLP and machine learning algorithms can quickly analyze multiple research articles and generate succinct summaries. This not only improves efficiency but also allows researchers to focus on the core analysis and interpretation of the compiled insights.

2. AI tools aid in swift research discovery!

AI tools also help researchers save time in the discovery phase of literature reviews. These AI-powered tools use semantic search analysis to identify relevant studies that might go unnoticed in traditional literature review methods. Also, AI tools can analyze keywords, citations , and other metadata to prompt or suggest pertinent articles that align and correlate well with the researcher’s search query.

3. AI Tools ensure to stay up to date with the most research ideas!

Another advantage of using AI-powered tools in literature reviews is their ability to handle the ever-increasing volume of published scientific research. With the exponential growth of scientific literature, it has become increasingly challenging for researchers to keep up with the latest scientific research and biomedical innovations.

However, AI tools can automatically scan and discover new publications, ensuring that researchers stay up-to-date with the most recent developments in their field of study.

4. Improves efficiency and accuracy of Literature Reviews

The use of AI tools in literature review reduces the occurrences of human errors that may occur during traditional literature review or manual document summarization. So, literature review AI tools improve the overall efficiency and accuracy of literature reviews, ensuring that researchers can access relevant information promptly by minimizing human errors.

List of AI Tools to Streamline Literature Reviews

We have several AI-powered tools to summarize literature reviews. They utilize advanced algorithms and natural language processing techniques to analyze and summarize lengthy scientific articles.

Let's take a look at some of the most popular AI tools to summarize literature reviews.

SciSpace Literature Review

Semantic scholar, paper digest.

SciSpace Literature Review is the best AI tool for summarizing literature review. It is the go-to tool that summarizes articles in seconds. It uses natural language processing models GPT 3.5 and GPT 4.0 to generate concise summaries. It is an effective and efficient AI-powered tool to streamline the literature review process and summarize multiple research articles at once. Once you enter a keyword, research topic, or question, it initiates your literature review process by providing instant insights from the top 5 highly relevant papers at the top.

These insights are backed by citations that allow you to refer to the source. All the resultant relevant papers appear in an easy-to-digest tabular format explaining each of the sections used in the paper in different columns. You can also customize the table by adding or removing the columns according to your research needs. This is the unique feature of this literature review AI tool.

SciSpace Literature review stands out as the best AI tool to summarize literature review by providing concise TL;DR text and summaries for all the sections used in the research paper. This way, it makes the review process easier for any researcher, and could comprehend more research papers in less time.

Try SciSpace Literature Review now!

Semantic Scholar is an AI-powered search engine that helps researchers find relevant research papers based on the keyword or research topic. It works similar to Google Scholar.It helps you discover and understand scientific research by providing suitable research papers. The database has over 200 million research articles, you can filter out the results based on the field of study, author, date of publication, and journals or conferences.

They have recently released the Semantic Reader — an AI-powered tool for scientific readers that enhances the reading process. This is available in the beta version.

Try Semantic Scholar here

Paper Digest — another valuable text summarizer tool (AI-powered tool) that summarizes the literature review and helps you get to the core insights of the research paper in a few minutes! This powerful tool works pretty straightforwardly and generates summaries of research papers. You just need to input the article URL or DOI and click on “Digest” to get the summaries. It comes for free and is currently in the beta version.

You can access Paper Digest here !

SciSummary is another AI tool that summarizes scientific articles and literature review. It uses natural language processing algorithm to generate concise summaries. You need to upload the document on the dashboard or send the article link via email and your summaries will be generated and delivered to your inbox. This is the best AI-powered tool that helps you read and understand lengthy and complicated research papers. It has different pricing plans (both free and premium) which start at $4.99/month, you can choose the plans according to your needs.

You can access SciSummary here

Step-by-Step Guide to Using AI Tools to Summarize Literature Reviews

Here’s a short step-by-step guide that clearly articulates how to use AI tools for summary generation!

• Select the AI-powered tool that best suits your research needs.
• Once you've chosen a tool, you must provide input, such as an article link, DOI, or PDF, to the tool.
• The AI tool will then process the input using its algorithms and techniques, generating a summary of the literature.
• The generated summary will contain the most important information, including key points, methodologies, and conclusions in a succinct format.
• Review and assess the generated summaries to ensure accuracy and relevance.

Challenges of using AI tools for summarization

AI tools are designed to generate precise summaries, however, they may sometimes miss out on important facts or misinterpret specific information.

Here are the potential challenges and risks researchers should be wary of when using AI tools to summarize literature reviews!

1. Lack of contextual intelligence

AI-powered tools cannot ensure that they completely understand the context of the research papers. This leads to inappropriate or misleading summaries of similar academic papers.

To combat this, researchers should feed additional context to the AI prompt or use AI tools with more advanced training models that can better understand the complexities of the research papers.

2. AI tools cannot ensure foolproof summaries

While AI tools can immensely speed up the summarization process, but, they may not be able to capture the complete essence of a research paper or accurately decrypt complex concepts.

Therefore, AI tools are just to be considered as technology aids rather than replacements for human analysis or understanding of key information.

3. Potential bias in the generated summaries

AI-powered tools are largely trained on the existing data, and if the training data is biased, it can eventually lead to biased summaries.

Researchers should be cautious and ensure that the training data is diverse and representative of various sources, different perspectives, and research domains.

4. Quality of the input article affects the summary output

The quality of the research article that we upload or input data also has a direct effect on the accuracy of the generated summaries.

If the input article is poorly written or contains errors, the AI tool might not be able to generate coherent and accurate summaries. Researchers should select high-quality academic papers and articles to obtain reliable and informative summaries.

Concluding!

AI summarization tools have a substantial impact on academic research. By leveraging AI tools, researchers can streamline the literature review process, enabling them to stay up-to-date with the latest advancements in their field of study and make informed decisions based on a comprehensive understanding of current knowledge.

By understanding the role of AI tool to summarize literature review, exploring different AI tools for summarization, following a systematic review process, and assessing the impact of these tools on their academic research, researchers can harness AI tools in enhancing their literature review processes.

If you are also keen to explore the best AI-powered tool for summarizing the literature review process, head over to SciSpace Literature Review and start analyzing the research papers right away — SciSpace Literature Review

Frequently Asked Questions

Give an overview of the main points from each paper and integrate them into a coherent whole, by outlining the importance, limitations, and unique insights from the reviewed literature.

SciSpace Literature Review is the best AI tool for summarizing research articles. It can summarize articles in seconds and provide detailed and focused insights.

Some of the best AI tools for summarizing literature reviews includes: SciSpace, Semantic Scholar, Paper Digest, and SciSummary.

For a Ph.D.dissertation, a literature review summary typically comprises a chapter (around 8000 words), while for a Master’s thesis, it is usually around 2000 - 3000 words.

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AI and Generative AI for Research Discovery and Summarization

AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from text prompts to analyze data or fit statistical models. One area that these tools can make a substantial impact is in research discovery and summarization. Standalone tools and plugins to chatbots are being developed that allow researchers to more quickly find relevant literature than pre-2023 search tools. Furthermore, generative AI tools have improved to the point where they can summarize and extract the key points from research articles in succinct language. Finally, chatbots based on highly parameterized LLMs can be used to simulate abductive reasoning, which provides researchers the ability to make connections among related technical topics, which can also be used for research discovery. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future that may be of interest to statistician and data scientists.

Keywords : Abductive reasoning, Hallucination, Literature discovery, Manuscript abstraction, Research discovery

1 Introduction

One of the most important ways for practicing statisticians and data scientists to remain viable in their work is being able to learn and apply unfamiliar methods. Doing so requires the ability to perform literature searches, become familiar with quantitative subfields, and learn about methodological advances. Fortunately, in the current age of Artificial Intelligence (AI), new tools are being developed at a rapid rate that can assist research in ways that were unthinkable a short time ago. The scope of AI technology is advancing so quickly, it is difficult to keep track of the areas of application and of the tools becoming available. As an example, ChatGPT Plus, the premium version of the widely used generative AI large language model (LLM) tool by OpenAI, established 3rd-party plugins in March 2023, with a total of 11 plugins (Chincha,, 2023 ) , and has since grown to 1037 plugins as of mid-December 2023 (ScriptByAI,, 2023 ) . Keeping track of the functionality of all of these plugins, not to mention the explosion of other AI tools that are becoming available, has become a full-time job.

Until relatively recently, statistical researchers have relied mostly on digital libraries such as JSTOR ( www.jstor.org ), which has for decades archived influential journals in statistics, including Journal of the American Statistical Association (JASA), Journal of the Royal Statistical Society - Series B (JRSS-B), Biometrika, the Annals of Statistics, and many more. However, with options for publication that go way beyond the JSTOR collection, other avenues have opened up. Many statistical researchers have relied on Google Scholar searches ( scholar.google.com ) to find relevant articles based on provided keywords. Based on key word input, Google Scholar ranks candidate documents using criteria such as the relevance of the work, where it was published, and the degree to which the work has been cited by other documents (Beel and Gipp,, 2009 ) . One of the key limitations of Google Scholar, however, is the lack of a LLM front end to interpret nuanced inputs.

The following example, which we return to throughout this manuscript, demonstrates a limitation of non-LLM search tools. Consider the problem in which a pairwise distance or dissimilarity matrix of n 𝑛 n italic_n objects has been computed, and it is of interest to generate n 𝑛 n italic_n vectors each of specified dimension d 𝑑 d italic_d (that is, d 𝑑 d italic_d -dimensional embeddings) whose pairwise Euclidean distances correspond, at least approximately, to the initial pairwise distance matrix. Such a procedure is known as classical multidimensional scaling (Torgerson,, 1952 ; Gower,, 1966 ) , and is more commonly known among psychometricians than statisticians and data scientists. Performing a Google search using the keywords “generating Euclidean vectors from a pairwise distance matrix” returns the search results in Figure  1 .

In every search result item on the first page (and several subsequent pages), Google returned links to methods for computing pairwise distance matrices based on Euclidean vectors, which is the opposite of what was requested. Similarly, a Bing web search resulted in similar sets of results on computing distance matrices from vectors, though Bing did provide a link for a StackExchange question-and-answer mentioning the creation of vectors from a distance matrix, but without providing the correct answer (StackExchange,, 2013 ) .

The difficulty with this particular type of search query is that the parser may not be able to distinguish whether the query is about computing distance matrices or about determining vectors that produce given distance matrices. Additionally, the search engine may not be able to distinguish whether web pages are addressing the question of interest or a question that involves a reconfiguration of the words in the search request. Given that many more sites on the web are concerned with computing distance matrices from vectors, a much more conventional task, a search engine is more likely in the face of the uncertainty about the search query to produce results that address the more commonly queried topic. The role of AI in this type of search is to improve both on making sense of the question’s intent, and to have a more concrete “understanding” of website content.

This article reviews the current landscape of AI tools available to statistical and data science researchers to perform literature searches, to synthesize topics from disparate sources, and to improve the acquisition of knowledge for a data science audience. We describe some of the basic functions of existing tools available to researchers, but our emphasis is on a high-level explanation of how AI tools can enhance work. These tools are constantly updating and improving, so it is only possible to provide a snapshot in time of their basic functioning. We conclude with our hypotheses about how developments in AI tools for data science research will evolve, given the technological accomplishments seen up to this point.

2 Hallucination issues

In November 2022, the chatbot ChatGPT was made publicly available by OpenAI and probably changed the world forever. Early indications were that, despite being trained on an enormous corpus of data that was only current as of 2021 (as of writing, ChatGPT is trained on data up through April 2023), it would eventually replace web search tools like Google, at least to search for information recorded no later than 2021. The availability of ChatGPT inspired other companies working on generative AI to make their products available to the public as well, proliferating a number of generative AI chatbots.

Traditionally, web searches have been the standard for information retrieval, offering results based on indexed web pages. However, tools like ChatGPT, powered by LLMs, represent a new paradigm. They provide conversational and context-aware responses, which is a departure from the list of links and summaries typical of standard web searches. Despite this innovative approach, ChatGPT and similar AI tools do face a unique challenge not commonly encountered in traditional web searches: the issue of “hallucination.” In the context of AI, hallucination typically refers to the generation of information that is either factually incorrect, irrelevant, or nonsensical, yet is often delivered with a high degree of confidence. This presents a unique challenge in ensuring the reliability and accuracy of the information provided by these AI systems.

As widely covered in news reports (e.g., Weise and Metz,, 2023 ) and detailed in technical papers (e.g., Achiam, J. et al.,, 2023 ) , LLM-based chatbots, including ChatGPT, consistently encounter hallucinations. This issue goes beyond simple technical glitches, striking at the fundamental principles of how these models are trained and function. The comprehensive survey paper by Ji et al., ( 2023 ) offers an in-depth examination of this phenomenon. It presents a detailed analysis of the various aspects and implications of hallucinations in language models, providing a comprehensive overview of how and why hallucinations occur in AI systems. We refer interested readers to this paper for a more thorough understanding.

A particularly notable case of hallucination in ChatGPT, which has been observed in multiple instances, is its tendency to fabricate references or cite non-existent sources. For example, in a study investigating the frequency of AI hallucinations in research proposals generated by ChatGPT, it was discovered that out of 178 references cited, 69 did not have a Digital Object Identifier (DOI), and 28 of these were found to be completely non-existent (Alkaissi and McFarlane,, 2023 ) . Similarly, another study evaluating the quality of the answers and the references provided by ChatGPT for medical questions, found that out of the 59 references included in the primary analysis, 41 (69%) were fabricated. Moreover, among the remaining 18 valid references, several had issues: 3 contained minor citation inaccuracies, and 5 had major citation errors (Gravel et al.,, 2023 ) .

Such hallucinations are not the exception, but rather the norm. We requested ChatGPT to provide five recent papers on “multidimensional scaling” from the past decade. The search results are presented in Figure  2 . While these results initially seem credible, a closer look found that two of the five papers (citations 1 and 4) listed actually do not exist. Moreover, among the three valid references, each contains citation inaccuracies, including nonexistent DOIs, incorrect publication years, and in one case (citation 2), an incorrect journal name.

This pattern of hallucinations in ChatGPT’s output significantly undermines the reliability of AI-generated information, especially when used as a search tool for academic content where precision and factual accuracy are not merely preferred, but essential. Consequently, while LLMs like ChatGPT can augment traditional search methods by providing quick, comprehensive, and sometimes insightful summaries, their use must be complemented with an appreciation of their limitations.

Current efforts are focusing on developing more sophisticated training methods, such as using verified and fact-checked datasets, and implementing advanced algorithms capable of cross-referencing and validating information against trusted sources. To this end, chatbots like ChatGPT Plus, which uses the GPT-4 (Achiam, J. et al.,, 2023 ) LLM, have incorporated plugins that specifically search for scholarly content and grab the actual publication details rather than let the LLM hallucinate document details on its own. Moreover, there is a growing recognition of the importance of human oversight in the use of LLMs. Integrating expert review can provide an additional layer of verification. Researchers and users are best served by approaching the LLM output with a critical eye, verifying against primary sources and employing a combination of methods for thorough research.

3 Abductive reasoning

The process of inferring the most believable explanation based on a given set of observations or statements is known as abductive reasoning (Peirce,, 1935 ; Peirce et al.,, 1973 ) . Typically, potential explanations may not be spelled out a priori, so unlike statistical inferential settings in which a set of potential explanations is usually listed, some notion of creativity is typically associated with the abductive process. A classic example of abductive reasoning is when a patient mentions to their medical provider various symptoms from which they are suffering. The provider then needs to (abductively) reason the likeliest explanation of the symptoms before proposing treatment.

Abductive reasoning is also an essential skill in research discovery, one that is practiced by quantitative researchers all the time. For example, a researcher may have an idea for a novel approach to a problem, and wants to know whether such an approach already exists and has been developed and studied, or about related methods that address the problem the researcher is attempting to solve. Another situation is where a researcher may suspect that an algorithm, statistical procedure, or computational method may already exist, but is unaware of the conventional name of the procedure. These tasks are instances of abductive reasoning; the details of an algorithm or process (the “observations”) are described, and then the conventional description that most plausibly describes the process (the “explanation”) is identified.

These abductive research discovery tasks are now being successfully carried out using LLMs. It is a remarkable feat that LLMs can mimic various aspects of logical reasoning, despite not being trained to do so. Evidence from recent work ( Wei et al., 2022a, ; Wei et al., 2022b, ; Suzgun et al.,, 2022 ) suggests that the more highly parameterized the LLM, the greater the likelihood for the LLM to exhibit reasoning ability in different forms. Examination of the ability of LLMs to reason successfully have produced mixed results (Qiao et al.,, 2022 ; Guo et al.,, 2023 ; Huang and Chang,, 2022 ; Davis,, 2023 ) , though many of the documented conclusions may already be out of date. Bang et al., ( 2023 ) assessed ChatGPT’s abductive reasoning ability based on a challenge dataset (Bhagavatula et al.,, 2019 ) that tests abductive natural language inference. The authors noted that ChatGPT achieved 86.7% accuracy, and that ChatGPT performed better at deductive and abductive reasoning than inductive reasoning. Pareschi, ( 2023 ) has shown that advanced LLMs like GPT-4 show promise in abductive reasoning in complex situations.

We have found that chatbots such as ChatGPT using GPT-4 can be effective abductive reasoners to discover existing methods based on descriptions. As a simple example, suppose we are interested in learning about methods that can be used to compare the distribution of a binary variable across different groups, as an analog to ANOVA for quantitative outcomes. Using the prompt “What methods exist for testing whether the distribution of a binary variable across different groups is the same, like how ANOVA is often used for quantitative outcomes?”, ChatGPT responds with a summary of common methods that include brief descriptions of chi-square tests, Fisher’s Exact test, logistic regression, the Mantel-Haenszel test, and a few others that are also relevant. For a researcher who may not have been familiar with these standard approaches, ChatGPT’s response provides an answer that can be used as a starting point for follow-up.

The earlier example on generating embeddings given a pairwise distance matrix is even more compelling, especially given the failure of commonly used search engines to direct the user to appropriate websites. When prompted with, “How do I generate Euclidean vectors from a given pairwise distance matrix? Explain the basic approach,” ChatGPT responds with “Generating Euclidean vectors from a given pairwise distance matrix is a task that involves multidimensional scaling (MDS). The goal is to find a set of points in a Euclidean space such that the distances between points in this space are as close as possible to the distances given in the pairwise distance matrix.” It then provides the correct algorithm to construct the embeddings.

4 Literature discovery

Literature discovery and comparative analysis of research literature often come up as some of the most time-consuming areas when conducting research. As we discuss below, generative AI has been making a big impact in these areas.

4.1 Standalone web-based literature search tools

A literature search engine with a similar purpose to Google Scholar and serves as the back-end for several AI-enhanced tools is Semantic Scholar ( www.semanticscholar.org ). This free literature search tool is popular with developers given its reliable API. The Semantic Scholar engine, which focuses on scientific literature searches, has the same limitations on input as Google Scholar, but relies more heavily on citation networks and collaborative filtering to identify relevant documents. It can also fine-tune literature searches by allowing the user to specify which documents in its initial search were relevant and which were not, allowing better results after iterating the search. While not strictly an AI tool on its own, Semantic Scholar, along with other recent literature search tools we describe below, has begun enhancing its utility by adding generative AI features. For example, a search on Semantic Scholar accompanies many suggested documents with a brief, typically one-sentence, summary of the contents of the work. These distillations are created using GPT-3 (Brown et al.,, 2020 ) style parsing of an article’s abstract, body, conclusion and title (Cachola et al.,, 2020 ) . At the time of writing, Semantic Scholar has a database of over 200 million papers over which searches can be performed.

Over the past year, many standalone web-based tools have entered the market as options for conducting research literature searches. The main distinction between these tools and ones like Google Scholar and Semantic Scholar is the use of LLMs to interpret the research prompt provided by the user. This feature may be used to improve the relevance of search queries, or provide downstream output based on the form of the prompt. One of the most popular tools is Consensus ( https://consensus.app ). Early in 2023, the engine had a fairly restricted database of articles that focused on only six areas of scientific inquiry, but more recently Consensus has expanded to the Semantic Scholar database. When a user enters a prompt, Consensus performs a search of the non-stop words in the prompt to information in the titles and abstracts of all the documents in the database. If the user asks a question as their prompt, Consensus aims to supply an LLM-generated answer based on the results of the search.

Another popular standalone tool is Assistant by Scite ( https://scite.ai/assistant ). Like Semantic Scholar, Scite’s Assistant performs a key word search to generate candidate works relevant to the search query. The main strength of Scite is its ability to sort the results by a more complex citation algorithm (Nicholson et al.,, 2021 ) . Because Scite is focused on citation networks and algorithms, the search tool can provide forward citations of articles on the search list results as easily as it can construct traditional backward citations. Much of the use for Scite is on finding articles that may support or contrast the search prompt. This may be more appropriate to fields with domain-specific queries, but probably not so directly useful for statistical or data science research.

A standalone web-based literature search tool that we explore below in more detail is Elicit (Kung,, 2023 ) , which can be accessed at https://elicit.com/ . Elicit also uses Semantic Scholar as its database, and finds relevant documents that are semantically related to the research question. The summaries from Elicit include one-sentence summaries of each suggested work (like those directly produced by Semantic Scholar), along with a GPT-3 created overall paragraph summary of the top papers in the list. The user can interact with individual articles by clicking on them, revealing more information about the article. A useful feature is the ability to view critiques of the work (determined through a semantic analysis) by forward-cited articles.

As an example of the use of Elicit, we asked it, “What methods can be used to perform multidimensional scaling based on a pairwise distance matrix?” The results were relevant, but most were older references. We used a now-deprecated feature of Elicit, called “brainstorming”, to generate the useful follow-up question “What are the most commonly used algorithms for multidimensional scaling?” This produced a more useful set of articles, the results of which can be seen in Figure  3 .

The article in the list by Yang et al., ( 2006 ) seemed to be worthy of follow-up, and upon selecting that article, we obtained the summary seen in Figure  4 .

Again, we note that in addition to the abstract summary, the possible critiques and other citations can help in evaluating the usefulness of the referenced article. These standalone tools have other helpful features, such as the ability to further explore the text content of suggested articles via natural language processing (NLP) analyses, export articles and meta-data, and to save and organize previous sessions. It should be noted that these standalone web-based tools are moving in the direction of being fee-based, and the feature selections described here may be out of date as the development of these tools quickly evolve.

4.2 Standalone web-based literature mapping tools

AI-powered engines like Litmaps ( www.litmaps.com ) and ResearchRabbit ( www.researchrabbit.ai ) simplify the challenging task of identifying potential gaps in existing literature by visualizing the connections among different academic publications, helping to highlight overlooked or underexplored study areas. Litmaps is an innovative tool designed to streamline the process of literature review for researchers and academics. Utilizing citation searches tailored to databases, Litmaps reveals papers related to a specific topic and then visually maps out the relationships among these studies, providing a cohesive overview of the scientific landscape. This interactive tool generates literature maps that not only help researchers find relevant papers and understand the connections among them, but also facilitate the creation of a customized repository to aid in the literature review process.

As a simple application, we return to our previous example of generating embeddings from a distance matrix. By inputting the keywords “multidimensional scaling”, Litmaps promptly displays a list of relevant articles and papers. The user can then mark preferred articles and proceed to click on the “Generate Seed Map” button to initiate a literature review, as shown in Figure  5 .

To illustrate, we selected the published book Borg and Groenen, ( 2005 ) as a starting point, at which point Litmaps then generates a seed map, finding papers that either cite or are cited by this foundational piece. In the top left corner of the map, the paper on “Local Linear Embedding” (Roweis and Saul,, 2000 ) is displayed as seen in Figure  6 , which has received numerous citations reflecting its impact in dimension reduction.

Building upon this newly selected article and its associated seed map, the “Discovery” feature in Litmaps now accommodates multiple seed inputs, piecing together an extensive discovery map that encompasses a wider array of related literature. This map is organized such that our initial selections form the core in the inner circle, while the outer circle highlights the most relevant additional works. The results are illustrated in Figure  7 .

These visualization tools, serving as a kind of “Spotify” for academia, have significantly expanded the possibilities for literature discovery, especially in identifying papers that cross multiple topics or disciplines. They simplify the process of finding related papers and enhance the ability to efficiently access a broad spectrum of academic knowledge. Importantly, these tools can also uncover hidden, cross-disciplinary connections that might be missed when specialists concentrate exclusively on their own fields.

4.3 Within-ChatGPT plugin tools/Custom GPTs

In addition to standalone applications, ChatGPT plugins have extended the functionality of the base ChatGPT model, enabling it to perform specialized tasks related to research discovery and summarization.

One popular tool is the ScholarAI plugin ( scholarai.io ), which is tailored to help researchers access a wide range of peer-reviewed articles, journals, and conference papers. It simplifies the process of finding relevant literature by allowing searches based on specific queries, such as keywords, authors, or topics, and then retrieves abstracts or even full texts (when available) from various academic publications. A key feature of ScholarAI is its ability to provide concise summaries of these papers. This is particularly useful for researchers who need a quick understanding of a study without reading the entire document, or even an article’s abstract. Additionally, ScholarAI includes citation management tools, which help users save and organize the most relevant papers for their research.

To illustrate the functionality of the ScholarAI plugin, we again consider our previous example. We asked the plugin to find relevant papers on the topic of “local linear embedding”.

As shown in Figure  8 , the plugin returned several articles based on these keywords, each with a summary, citation counts, and source links. Notably, it included Roweis and Saul, ( 2000 ) , the highly cited Science paper which was also discovered previously through the Litmaps App. ScholarAI provided a brief abstract of this paper, which allows us to quickly grasp the main ideas and importance of the paper. In addition to this key publication, the plugin also identified other relevant studies, each summarized with essential details. This enables us to get a quick understanding of each paper’s contributions without needing to read the full texts.

In addition to the standard ChatGPT plugins, OpenAI has recently been encouraging users to create and share their own “custom” GPTs in the marketplace. Since the launch of OpenAI’s new GPT store, there has been a significant increase in the demand for custom GPTs across various fields and domains. Simply speaking, a custom GPT is a version of ChatGPT that is preloaded with specific knowledge and instructions for interacting with users. For example, we have noticed the recent launch of ResearchGPT ( www.researchgpt.com ), a custom GPT created by Consensus, which provides a seamless integration of ChatGPT’s conversational capabilities with access to the Semantic Scholar database of research papers, making it arguably more useful for scientific research than using the standalone Consensus website. This GPT can perform in-depth searches in academic databases, provide brief summaries and answers based on science, help write content with accurate references, and even assist in creating introductions for academic papers.

4.4 Specific tools for summarizing and abstracting manuscripts

ChatGPT is increasingly recognized as an effective tool for summarizing research papers. In general, it does well in analyzing a whole research paper and then producing a concise summary. However, its functionality has been previously limited by the requirement for manual text input, and only recently does it support direct file uploads. ChatGPT plugin tools such as AskYourPDF and ChatwithPDF, and even ScholarAI which has been enhanced to read pdf files, were more specifically developed to enable direct handling of (multiple) pdf files, providing a more flexible option for researchers who need to engage closely with their lengthy documents and data. These tools can provide short, clear summaries of long articles, making them easier to understand. This saves time and helps to clearly show the main points of each paper, making them more digestible.

Although ChatGPT and its associated plugins are effective for summarizing general written content, unfortunately, they encounter difficulties when it comes to more technical research papers, especially those heavy in mathematics and data analysis. The design of ChatGPT, which is more suited for natural language, can struggle to accurately summarize and abstract important details from the technical parts of these papers. This issue is particularly noticeable in areas such as statistical methodology, where crucial theorems and technical specifics are frequently presented through intricate mathematical expressions. In such cases, ChatGPT is better at offering a basic overview rather than a detailed technical analysis.

As an example, we explored the accuracy of details about Yang et al., ( 2006 ) summarized by ChatGPT. With the ScholarAI plugin loaded, ChatGPT presented a brief summary of the initial sections of the paper. One detail in the ChatGPT summary was “The authors propose a divide-and-conquer approach, dividing the matrix into smaller submatrices for MDS, then combining the solutions for the complete n × n 𝑛 𝑛 n\times n italic_n × italic_n matrix.” We followed up with the question, “How are the results from performing MDS on the smaller submatrices combined to form a complete solution?” The response by ChatGPT can be seen in Figure  9 .

The answer by ChatGPT is mostly a rephrasing of the first three paragraphs of text in Section 3 of the manuscript. It is mostly accurate, which may not be surprising given that it is essentially a rephrasing of the body of the paper. However, ChatGPT makes a mistake that appears in items 1 and 5 of its response. ChatGPT refers to the dimensions of the submatrices being of size n p × n p subscript 𝑛 𝑝 subscript 𝑛 𝑝 n_{p}\times n_{p} italic_n start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT × italic_n start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT , and that n p subscript 𝑛 𝑝 n_{p} italic_n start_POSTSUBSCRIPT italic_p end_POSTSUBSCRIPT points have been sampled from D i subscript 𝐷 𝑖 D_{i} italic_D start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT . In fact, the manuscript refers to the dimensions of the submatrices as n p × n p 𝑛 𝑝 𝑛 𝑝 \frac{n}{p}\times\frac{n}{p} divide start_ARG italic_n end_ARG start_ARG italic_p end_ARG × divide start_ARG italic_n end_ARG start_ARG italic_p end_ARG , and refers to n p 𝑛 𝑝 \frac{n}{p} divide start_ARG italic_n end_ARG start_ARG italic_p end_ARG points sampled from D i subscript 𝐷 𝑖 D_{i} italic_D start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , so ChatGPT misread the quotients as subscripts. Otherwise, the technical details appear accurate.

The ability of ChatGPT to summarize research papers varies depending on the content. It currently appears to be useful for text-focused papers but less effective for those with significant technical or mathematical details.

5 Discussion

Generative AI technology has made enormous strides in such a short time that it should not be surprising that the capabilities of the tools we described are evolving rapidly. On almost a biweekly basis, we receive email updates about new and upcoming features of the tools we reviewed in this article. Given this dynamic landscape, attempting to encapsulate the full spectrum of features in a static publication would be futile. Even though the state of the art in AI for use in research discovery and exploration is impressive, there remains significant potential for further enhancement. Insightful predictions, challenges, and possible directions for AI development have been articulated by Morris, ( 2023 ) drawing on input from scientists in various fields, and by Brodnik et al., ( 2023 ) in the area of mechanics. In this context, we offer our perspective on the probable trajectories of AI advancement, aiming to further streamline and empower the processes of literature discovery and synthesis.

The tools that currently exist to discover research references are remarkable, yet the scope for enhancing the databases and repositories that underpin these research discovery tools is substantial. We anticipate that as the tools improve, larger catalogs of articles and books would become discoverable. A major challenge, however, lies in navigating copyright issues when accessing literature behind paywalls, such as non-open access journals. This obstacle underscores the need for innovative solutions. Importantly, however, tools could be developed that no longer only searched conventional formats such as articles and books, but would include videos (like lectures or talks), podcasts, slide presentations, and other non-traditional methods of dissemination that could greatly benefit researchers. This is especially valuable for those in the early stages of exploring fields within statistics and data science, who might prefer absorbing information through these alternative, more engaging formats.

In addition to expanding the resources for research discovery, the burgeoning field of extracting and synthesizing information from multiple sources is poised for considerable advancement, complementing the expansion of research discovery resources. On a smaller scale, having reliable tools that analyze multiple documents can help to distinguish the unique contributions among them, as well as to detect common patterns or methodological trends. On a larger scale, the ability to synthesize large collections of documents on a related topic can lead to creating review articles that would enable researchers to delve into and learn new domains within statistical scientific inquiry with greater ease and depth.

By integrating the content analysis of large collections of published research with impact measures like citation indices, it is possible to discern emerging trends of topics in statistics and data science. This information could guide statistical researchers in strategically choosing their future areas of focus. Using the same source of data, a more speculative development would be the ability to determine from, say, an article’s abstract whether the article is likely to be highly cited in the future. In principle, this could be achieved through careful modeling of article citation indices as a function of an abstract’s contents, providing a predictive tool for gauging the potential influence of research works.

Another type of tool that could take advantage of both large collections of published research along with AI analysis of text relates to improving citations within manuscripts. For example, having a tool that analyzes a draft version of a manuscript and suggests alternative and possibly more foundational or highly-cited references instead of ones currently in the manuscript could be enormously helpful. Perhaps even more ambitiously, it is within the realm of possibilities that a tool could analyze a draft manuscript and find instances of text that should include proper citations if they do not already exist, flagging these for the researcher’s attention. Given the amount of time researchers spend on tracking down relevant citations, off-loading this task to an AI tool would make publishing research more efficient.

Finally, the success of AI in language translation (Doherty,, 2016 ; Tran et al.,, 2018 ; Gülçehre et al.,, 2015 ) suggests its potential application in a unique academic challenge: translating specialized terminologies across various quantitative sub-fields in statistics and data science. Researchers often encounter difficulties when trying to reconcile differing terminologies used in classical statistics, computer science, machine learning, econometrics, and psychometrics. The development of AI tools designed to translate technical language across disciplines could serve as a crucial bridge, facilitating better understanding and integration of research across these areas. Such tools would enable researchers to more easily comprehend and utilize work that might currently be challenging to interpret, thus encouraging interdisciplinary collaboration and innovation.

It is important to acknowledge that the future directions of AI in research discovery and summarization outlined here are speculative. However, we believe that the foundational work for these developments is already in place, making significant advancements a matter of “when,” not “if.” The rapid pace of AI progress in recent years is nothing short of astonishing, with tangible impacts already felt by researchers in their professional lives. We anticipate that in the near future, AI tools for research tasks will have evolved to such an extent that researchers can dedicate their efforts to what truly matters: engaging in in-depth research. This shift promises to transform the research landscape, allowing researchers to focus more on innovation and less on administrative tasks.

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Research Assistant

Ai-powered research summarizer.

• Summarize academic papers: Quickly understand the main points of research papers without reading the entire document.
• Extract insights from reports: Identify key findings and trends from industry reports, surveys, or reviews.
• Prepare for presentations: Create concise summaries of research materials to include in your presentations or talking points.
• Enhance your understanding: Improve your comprehension of complex subjects by summarizing the main ideas and insights.
• Save time: Reduce the time spent on reading lengthy research documents by focusing on the most important points.

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The inherent intricacy and technical nature of research papers’ content make reading them a challenging undertaking. These research articles can be difficult to understand, especially for non-experts or those who are new to the area because they frequently contain specialized vocabulary, complicated concepts and complex methodologies. The amount of jargon and technical terms might act as a barrier, making it harder for readers to comprehend the content.

Additionally, research papers frequently dive into complex theories, models and statistical analyses, demanding a solid background understanding of the subject to ensure adequate comprehension. The voluminous nature of the research papers and the requirement to critically evaluate the provided data only make the issue worse.

As a result, it could be difficult for readers to distill the key points, determine the significance of the findings, and combine the data into a coherent perspective. It frequently takes persistence, the incremental accumulation of domain-specific knowledge and the creation of efficient reading techniques to get beyond these obstacles.

Artificial intelligence (AI) -powered tools that provide support for tackling the complexity of reading research papers can be used to solve this complexity. They can produce succinct summaries, make the language simpler, provide contextualization, extract pertinent data, and provide answers to certain questions. By leveraging these tools, researchers can save time and enhance their understanding of complex papers.

But it’s crucial to keep in mind that AI tools should support human analysis and critical thinking rather than substitute for them. In order to ensure the correctness and reliability of the data collected from research publications, researchers should exercise caution and use their domain experience to check and analyze the outputs generated by AI techniques.

Here are five AI tools that may help summarize a research paper and save one’s time.

ChatGPT plays a crucial role in summarizing research papers by extracting key information, offering succinct summaries, demystifying technical language, contextualizing the research and supporting literature reviews. With ChatGPT’s assistance, researchers can gain a thorough understanding of papers while also saving time.

• Extrapolating key points: ChatGPT can analyze a research article and pinpoint its core ideas and most important conclusions. It might draw attention to crucial details, including the goals, methods, findings and conclusions of the study.
• Information condensation: ChatGPT can provide succinct summaries of research papers that perfectly capture their main points by processing their text. It can condense large sentences or sections into shorter, easier-to-read summaries, giving a summary of the main points and contributions of the paper.
• Simplifying technical terms: Technical terms and sophisticated terminology are frequently used in research papers. To make the summary more understandable to a wider audience, ChatGPT can rephrase and clarify these terms. It may offer explanations in simple terms to aid readers in comprehending the material.
• Contextualizing: ChatGPT can contextualize the research paper by connecting it to prior understanding or highlighting its significance within a larger body of research. Giving readers a thorough knowledge of the paper’s significance, it may include background information or make links to pertinent theories, studies or trends.
• Handling follow-up questions: Researchers can communicate with ChatGPT to ask specific questions regarding the research paper in order to get more information or elaborations on certain points. Based on its knowledge base, ChatGPT can offer extra details or insights.

Related:  10 ways blockchain developers can use ChatGPT

QuillBot offers a range of free tools that empower writers to enhance their skills. Both ChatGPT and QuillBot can be used together. When using ChatGPT and QuillBot in conjunction, begin with ChatGPT’s output and paste the output into QuillBot. 

QuillBot then analyzes the text and offers suggestions to enhance readability, coherence and engagement. One has the freedom to decide between many writing styles, including expansive, imaginative, straightforward and summarized. To further personalize the text and give it a distinct voice and tone, users can change the sentence structure, word choice and overall composition.

QuillBot’s Summarizer tool can help break complex information into digestible bullet points. To understand a research paper, one can either directly input the content into QuillBot or collaborate with ChatGPT to generate a condensed output. Afterward, they can utilize QuillBot’s Summarizer to further summarize the generated output. This streamlined approach allows for efficient summarization of the research paper. 

SciSpacy is a specialized natural language processing (NLP) library with an emphasis on scientific text processing. It makes use of pre-trained models to identify and annotate relationships and entities that are particular to a given domain.

It also contains functionalities for sentence segmentation, tokenization, part-of-speech tagging, dependency parsing and named entity recognition. Researchers can obtain deeper insights into scientific literature by using SciSpacy to streamline their analysis and summarizing procedures, extract important data, find pertinent entities and discover relevant things.

IBM Watson Discovery

An AI-powered tool called IBM Watson Discovery makes it possible to analyze and summarize academic publications. It makes use of cutting-edge machine learning and NLP techniques to glean insights from massive amounts of unstructured data, including papers, articles and scientific publications.

1.. Some AI tools that can provide summaries or reviews of papers. Here are three examples: 1. IBM Watson Discovery: uses natural language processing and machine learning algorithms to provide summaries of research papers. — SULTECH (@sultechsolution) June 1, 2023

In order to comprehend the context, ideas and links inside the text, Watson Discovery employs its cognitive capabilities, which enable researchers to find unnoticed patterns, trends and connections. It makes it simpler to navigate and summarize complicated research papers since it can highlight important entities, relationships and subjects.

Researchers can build unique queries, filter and categorize data, and produce summaries of pertinent research findings using Watson Discovery. Additionally, the program includes extensive search capabilities, allowing users to conduct exact searches and obtain certain data from enormous document libraries.

Researchers may read and comprehend lengthy research papers faster and with less effort by utilizing IBM Watson Discovery. It offers a thorough and effective technique to find pertinent information, learn new things and make it easier to summarize and evaluate scientific material.

Related:  5 real-world applications of natural language processing (NLP)

Semantic Scholar

Semantic Scholar is an AI-powered academic search engine that uses machine learning algorithms to comprehend and analyze scholarly information.

To provide thorough summaries of the research publications’ primary conclusions, Semantic Scholar collects important data from them, including abstracts, citations and key terms. Additionally, it provides tools like subject grouping, related research recommendations and citation analysis that can help researchers find and summarize pertinent literature.

The platform’s AI features allow it to recognize significant publications and well-known authors and develop research trends within particular subjects. Researchers wishing to summarize a particular area of research or keep up with the most recent developments in their field may find this to be especially helpful.

Researchers can read succinct summaries of research publications, find relevant work and gain insightful information to support their own research efforts by utilizing Semantic Scholar. For academics, researchers and scholars who need to quickly summarize and navigate through voluminous research literature, the tool is invaluable.

Precaution is better than cure

It’s crucial to keep in mind that AI tools may not always accurately capture the context of the original publication, even though they can help summarize research papers. Having said that, the output from such tools may serve as a starting point, and one can then edit the summary using their own knowledge and experience.

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Literature Review

15 Best AI Research Assistant Tools For Enhanced Productivity

Discover the 14 best AI research assistant tools to streamline workflow and boost productivity. Find the perfect solution for efficient research.

Aug 24, 2024

The process of writing a research paper can be daunting. The prospect of conducting a comprehensive literature search to produce a high-quality research paper for your class can be overwhelming. Where do I start? What if I miss something important? How do I organize all of this information? If you have ever asked yourself these questions, you are not alone. The good news is that you can tackle these challenges with help. This guide will provide valuable insights on how you can enlist the help of an AI research assistant to improve your next literature search and help you write a better research paper. Otio’s AI research and writing partner can help you achieve your academic goals, like writing efficient research papers and getting fabulous study material with AI. This tool will speed up your literature search, find relevant study material, and help you organize your findings to produce a top-notch research paper.

Table Of Contents

What is a research assistant, is there an ai research assistant, benefits of using ai research assistant, 14 best ai research assistant tools for enhanced productivity, how does an ai research assistant work, supercharge your researching ability with otio — try otio for free today.

Research institutes employ research assistants to assist with academic or private research. A research assistant's primary responsibility is to support either a research fellow or a research team through collecting, analyzing, and interpreting data. Institutes that use research assistants include universities, research centers (e.g., the Russell Group), and private organizations. Research assistants usually operate temporarily, though permanent positions exist. 

In an academic setting, research assistants work under the supervision of research fellows. Research assistant roles are often undertaken by postgraduate students completing their PhD program . This provides income for the doctoral student and prepares them for an academic career once their program is complete. However, you can also use AI tools such as Otio as research assistants for enhanced and quick academic research.

The Surprising Ways AI Can Alleviate the Burden of Research and Help You Write Faster

If you’ve ever done academic research , you know it can feel overwhelming. There’s an awful lot of information out there, and it’s only getting worse as more folks have access to the internet and can publish their thoughts with the click of a button. While you can use traditional methods to get through research and writing your paper,  like bookmarking articles, jotting down notes, and creating an outline, these approaches can be tedious, disjointed, and slow. 

Fortunately, recent advances in artificial intelligence can help you radically improve how you tackle academic research. AI tools, like Otio, can help you organize your research and even assist you in writing your paper. Here’s how:

1. Collect and Organize Your Research with Otio

The first step in academic research is to collect and organize your sources. Otio can help you do this with far less stress than traditional methods. Instead of bookmarking articles and PDFs, taking notes in a separate application, and manually creating an outline, you can use Otio to help you collect and organize your research in one place. 

Otio allows you to gather information from various sources, including articles, YouTube videos, tweets, and books. The tool also helps you extract meaningful information from your sources and organize it so you can reference it later easily.  

2. Extract Key Takeaways with AI-Generated Notes

Once you’ve collected a decent amount of research, you can start reading and taking notes. This part can be tedious and time-consuming, especially if you have a long list of sources to get through. Otio helps you tackle this part of research faster with AI-generated notes. 

Instead of reading each source and taking notes on what you think is essential, you can use Otio to get summaries and key points on all your sources. The AI does this quickly, so you can move on to writing your paper without a long delay.  

3. Create a Draft Using Your Organized Research

Otio helps you write research papers and essays faster. Once you’ve collected and organized your research, you can use the tool to help you create a first draft of your paper. This is a great way to overcome writer’s block and ease the transition into academic writing. 

Let Otio be your AI research and writing partner — try Otio for free today!

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• Systematic Review Vs Meta Analysis • Impact Evaluation • How To Critique A Research Article • How To Synthesize Sources • Annotation Techniques • Skimming And Scanning • Types Of Literature Reviews • Literature Review Table • Literature Review Matrix • How To Increase Reading Speed And Comprehension • How To Read Research Papers • How To Summarize A Research Paper • Literature Gap

1. Using AI Tools to Acquire Research Knowledge and Literature Review  

AI-powered research tools for reading, annotating, and note-taking can make acquiring knowledge more efficient. Such tools can provide the user excerpts from the literature source, highlighting the most relevant information and helping one decide whether an article is worth reading. 

This can help the user quickly locate pertinent information in research articles, determine which paragraphs to read in-depth, and compile notes on the subject. To use such an AI-powered tool most effectively for research, the users should critically assess the output without accepting it as ‘the truth’ and read the original text instead of simply relying on AI-generated summaries. 

A researcher uses a tool like Mendeley or Zotero to organize their literature. The tool automatically generates citations and bibliographies and suggests relevant articles based on the researcher's existing library. 

2. Using AI for Academic Writing 

Distilling complex information from numerous sources and explaining it along with one’s original ideas is essential to good academic writing. Effective note-taking systems that track the source information and help avoid plagiarism are critical for this process. AI-powered tools help take and organize relevant notes to be included in one’s write-up and help the researcher write an article effectively. 

Some AI tools also allow the researcher to paraphrase sentences from the notes they have taken. Such tools are essential and helpful for researchers from non-English speaking countries. To make the most effective use of AI tools for academic writing, researchers must not solely rely on AI for note-taking or writing. Researchers can also practice more ethical writing by reformulating the paraphrased content from AI rather than copy-pasting the paraphrased content. 

A researcher uses a grammar checker like Grammarly to identify and correct errors in their writing. 

3. Using AI for Research Planning and Study Design

AI-powered experimental design tools use machine learning algorithms to optimize parameters. Automating experimental design processes can help researchers reduce the time and effort required to design studies, freeing up more data analysis and interpretation time. Such AI tools can also reduce human errors and R&D costs. 

Researchers must consider various variables and parameters to use AI tools to create experimental design models effectively. By inputting specific criteria into such models, researchers can generate optimal designs that maximize their study effectiveness and effectively use AI tools to develop experimental design models. Researchers can generate optimal designs that maximize their study effectiveness by inputting particular criteria into such models.

A researcher uses a tool like JMP to design experiments, optimizing factors like sample size and experimental conditions. 

4. Using AI for Data Analysis

While traditional data analysis methods relied on manual processes and limited computational capabilities, AI-powered data analysis tools have revolutionized the field. Such tools use machine learning algorithms to interpret, extract, and uncover patterns in vast datasets. This can reduce time and cost and increase the efficiency of research output. 

To effectively use AI tools for data analysis, researchers must clearly define the objectives of their project and identify the specific insights and outcomes they want to achieve through the study. They must also gather relevant data and ensure it is clean, well-structured, and suitable for analysis. Finally, it is also essential that the researchers identify and determine which AI tools and algorithms are most appropriate for their analysis goals. 

A data scientist uses Python libraries like Pandas and Scikit-learn to analyze a large dataset and identify patterns and trends. 

5. Using AI for Peer Review Assistance

The volume of peer review submissions is constantly growing. Reducing screening and reviewing time can save millions of working hours and boost academic productivity. AI-powered peer review tools can create the potential for semi-automated peer review systems where low-quality or controversial studies could be flagged, and reviewers could be matched with manuscripts from their subject-matter expertise.

Although AI cannot perform peer review yet, AI tools can be effectively used in the peer review process to suggest appropriate journals for an article, perform initial quality control for submitted manuscripts, and find reviewers. 

A journal uses an AI tool like Otio to screen submissions for plagiarism and identify potential conflicts of interest.

• Literature Search Template • ChatGPT Prompts For Research • How To Find Gaps In Research • Research Journal Example • How To Find Limitations Of A Study • How To Do A Literature Search • Research Concept Map • Meta-Analysis Methods • How To Identify Bias In A Source • Search Strategies For Research • Literature Search Template • How To Read A Research Paper Quickly • How To Evaluate An Article • ChatGPT Summarize Paper • How To Take Notes For A Research Paper

1. Otio: The Ultimate AI Workspace for Researchers  

Otio’s innovative AI-native tool is designed to streamline the research workflow. Knowledge workers, researchers, and students today need help with content overload and are left to deal with it using fragmented, complex, and manual tooling. 

Too many settle for stitching together complicated bookmarking, read-it-later, and note-taking apps to get through their workflows. Now that anyone can create content with a button, this problem will only worsen. Otio solves this problem by providing researchers with one AI-native workspace. It helps them: 

1. Collect a wide range of data sources, from bookmarks, tweets, and extensive books to YouTube videos. 

2. extract key takeaways with detailed ai-generated notes and source-grounded q&a chat. , 3. create draft outputs using the sources you’ve collected. .

Otio helps you to go from the reading list to the first draft faster. Along with this, Otio also enables you to write research papers/essays faster. Here are our top features that researchers love: 

AI-generated notes on all bookmarks (Youtube videos, PDFs, articles, etc.), Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing. Let Otio be your AI research and writing partner — try Otio for free today!  

2. Bit AI: The Smart Collaboration Tool

Bit AI is an AI program designed to help teams collaborate on documents, wikis, and knowledge bases. It goes beyond just text and images, allowing users to create interactive documents containing videos, cloud files, and audio. It works much like Google Drive, allowing for real-time collaboration on documents. Multiple people can work on documents simultaneously and chat with one another within the interface. 

AI Writing AssistantAI Genius is the perfect tool for generating documents, wikis, and other information based on a text prompt. 

Multiple Use Cases: However, due to its document development features, AI is great for researchers and marketers, product management, startups, and human resources. 

Collaboration ToolsBit.ai allows multiple users to collaborate on documents, notes, wikis, and other real-time content. 

Wide Range of IntegrationBit AI integrates with many third-party tools, including YouTube, Google Sheets, Figma, and GitHub. 

Limited CustomizationsBit AI would be better if it had text formatting or options to customize the appearance of documents, such as matching company branding.  

3. Semantic Scholar: The AI Research Tool for Computer Science and Biomedical Research  

Semantic Scholar is one of the top AI tools for research , and it is widely used by students pursuing computer science, biomedical science, and neuroscience. It uses natural language processing to analyze academic papers and find relevant literature. Besides, Semantic Scholar offers detailed overviews of research topics and can identify the most critical parts of a paper, making it a valuable AI tool for research. 

Refine search results for greater efficiency and relevance. Continuously improves the tool based on user feedback and provides a personalized user experience. 

The summary has accuracy issues and AI-generated citations. It only gives you access to the full text of some papers.  

4. Scite: The AI Tool That’s Changing How We Read Research Papers  

Scite is one of the most popular AI-powered academic research tools that improve academic research in one go. Its own natural language processing and machine learning help users do better research on scholarly articles and analyze citations. 

Moreover, Scite allows researchers like you to assess the dependability of references in any particular context. It helps in evaluating the quality and impact of the research. It also provides better visualizations and metrics to understand the citation landscape of a specific paper or topic. If you have missed out on using this tool, try it today. 

Innovative Citations: Scite analyzes how an article is referenced in other research. It can tell if the citing article supports, contradicts, or mentions the original article. 

Citation Context: Scite shows you how different sections of an article are cited in other research, helping you understand how the original research is being used and interpreted by other researchers. 

Citation Reports: This tool generates reports that show citation patterns and trends, helping users identify articles and authors in a related field. 

Large Dataset: Scite was training on more than 187 million articles, books, preprints, and other datasets, making it a solid choice for researchers. 

Not All Articles Cited Are Accessible: Although Scite offers full-text access for most articles cited, some publishers have not.  

5. Google Scholar: The Best Free Tool for Academic Research  

Google Scholar is a beacon for academic research, offering a straightforward platform akin to its browser counterpart. It’s a treasure trove of recent articles, research papers, and scholarly literature, simplifying the quest for up-to-date information with easy-to-identify tags for quick access. 

Use natural language searching to find academic and literature topics. Allow your search for gray literature for systematic reviews. 

The content you find will not be reviewed thoroughly, and there are concerns about source credibility. You need to show significant literature topics.  

6. PDFGear Copilot: The AI Tool for Researching PDFs  

PDFgear Copilot is an AI-powered assistant that extracts and summarizes information in PDF documents. It utilizes OpenAI’s ChatGPT language model to help users locate important information in documents while conducting research. It lets you chat with a document, ask questions about its contents, and quickly summarize entire PDFs. 

Interact With PDFs: Summarize, analyze, and interact with PDF content through natural language processing. ChatGPT Integration: 

Find critical information and get answers by using built-in ChatGPT functionality. 

Streamline PDF Workflows: Complete tasks, such as converting, printing, and saving PDFs with natural language processing. 

Support for Multiple Languages: PDFgear Copilot supports over 100 languages, making it accessible to many users. 

No Dark Mode: PDFgear cannot switch to dark mode, potentially alienating those who prefer it.  

7. Consensus: The AI Research Assistant for Science  

Consensus is a research tool that gathers information from published material and peer-reviewed articles. The tool is helpful for those who want to understand scientific subjects thoroughly. It helps users understand scientific subjects thoroughly by scanning for trustworthy and accurate research articles. This tool is handy for students and researchers in STEM and business fields. 

Natural language processing is used to analyze data and verify the source. 

It also generates a summary of research queries and helps obtain information for the early research stage. 

It is favored only for STEM and business, not the humanities and fine arts. It is not suitable for rigorous and reproducible research.  

8. Trinka: The AI Tool for Academic Writing  

It is one of the most commonly used AI tools for scholars and students. It helps with grammar and language correction for academic and technical writing. It has 3000+ grammar checks and tone and style enhancements, which allow scholars to write better theses and projects without errors. 

Trinka enables you to document scientific findings and allows you to have a more technical tone and style without any difficulty. Therefore, Trinka is the most promising tool for academic research, as it helps better document research papers and white papers. 

Pros: 

Save time checking grammar while writing academic papers. 

Let you check grammar, correct spelling, and offer context suggestions based on your writing style. 

The tool's response time could be faster, hindering quick feedback. 

It may also be challenging to understand the technical jargon.  

9. Connected Papers: The Visual Literature Review Tool  

Connected Papers is an innovative research tool that helps scientists and scholars efficiently explore relevant literature by providing a visual, similarity-based mapping of related academic papers. It uses circles to represent different papers. The size of the circle corresponds to the frequency of citations by other researchers. 

Papers that closely resemble the original paper you provided are positioned closer together and connected by lines. The thickness of the line indicates the strength of the relationship between the documents. By following the connections between papers, you can use Connected Papers to explore new research areas within your field. 

Similarity Graphs: Connected Papers produces a visual graph displaying related papers. However, unlike a traditional citation tree, it emphasizes semantic similarity, meaning that documents with shared citations and references are more connected. 

Prior and Derivative Works: Connected Papers identifies influential prior works that have shaped the current research landscape. Additionally, it can reveal derivative works that build upon the original paper’s ideas. 

Multi-Origin Graphs: You can enter multiple papers as a starting point so that CP can create a combined graph highlighting their relationships. 

Save Papers and Graphs: Save papers and graphs to revisit and explore topics further. 

Limited Citation View: Unlike citation trees, Connected Papers doesn’t directly show how papers cite each other.  

10. Mendeley: The Free AI Tool for Organizing Research  

Mendeley is a user-friendly AI tool for organizing, sharing, and citing research papers properly in one place. It helps you quickly manage PDFs, create better bibliographies, and annotate documents. Moreover, this tool enables researchers to collaborate on projects and discover relevant articles based on their interests. 

Mendeley’s powerful features and integration into academic workflows make it a practical tool. It helps streamline your management and enhance collaboration within the scholarly community. 

Offer journal citation styles and boost citation efficiency. 

Organize and share references for collaborative research. 

Do not make PDF annotations as expected. 

Users commonly face server downtime and syncing errors.  

11. Litmaps: The Literature Mapping Tool  

Litmaps is a literature mapping tool that helps researchers discover new and relevant research papers, visualize the relationships between papers, and share their research. It works by using connectedness theory, which allows researchers to quickly scan Litmap’s network of academic papers around the documents they know, discovering vital related papers they may not know about. Litmaps can also generate reading lists and notify users when relevant new papers are published, making it easier to stay up-to-date on the latest research. 

Search Academic Papers: Litmaps allows you to search a vast database of over 260 million academic papers to find relevant articles. 

Mind Mapping: Litmaps lets you visualize your research by creating a map of interconnected articles and annotating them for better understanding. 

Explore Research From Various Angles: Dynamic Exploration allows users to explore research from different angles by rearranging how papers are positioned on the map. 

Collaboration Features: The platform enables you to share your research maps with colleagues, students, or advisors to facilitate collaboration. 

Limited Free Plan: Some features, like unlimited searches, are only available on paid plans.  

12. Scholarcy: The Research Paper Summarizer  

Scholarly is an AI tool that improves academic research by automating the process of reading, summarizing, and extracting information. It can help you recognize figures, tables, and references from articles and grasp the main concepts. Additionally, this tool has citation extraction features that allow users to organize and cite the sources used in the research. It also provides the literature review process, which enables you to save valuable time and effort. 

Summarize the topics of research papers to save time and effort. 

Offer links to the cited resources to access the research material. 

The essay summary may need to be more precise, which may result in plagiarism. 

The AI-generated summary will only cover some of the critical points of the research paper.  

13. Jenni: The AI Writing Assistant for Students  

The following AI tool for research on our list is Jenni. It’s an AI-powered writing assistant designed to help students and researchers with academic writing tasks. It uses machine learning and natural language processing (NLP) to provide content suggestions, writing feedback, and research assistance. It’s beneficial for writing essays, research papers, literature reviews, and more. Jenni can also help with citations and references and check for plagiarism. 

AskJenni: Use an AI research assistant that can help answer research questions and provide document-related clarifications. 

Citation and Reference Assistance: Jenni can properly format citations and references in styles like APA, MLA, and Chicago. 

AI Commands: Jenni can perform tasks like paraphrasing, rewriting, and simplifying existing text. 

AI Autocompletion: Jenni can suggest and complete sentences to help you write faster. 

Limited Content Types: Compared to other AI writing tools like Copy.ai and Jasper, Jenni.ai offers only a limited number of content types, such as blogs, essays, emails, and free-flow writing.  

14. Knewton: The Adaptive Learning Software  

Using artificial intelligence and machine learning algorithms, Knewton allows users to deliver personalized educational content. You can tailor the tool for the academic content according to individual needs and learning styles. 

This is a one-stop and easy-to-use tool for academic learning. Knewton also allows users to analyze student performance data, strengths, weaknesses, and progress. By leveraging AI's benefits, Knewton seeks to improve engagement, making it one of the best online learning platforms. 

Provide you with a personalized learning experience with the help of adaptive learning. Offer student interaction to keep cheating at bay by offering different questions to each student. 

The problems in the tool are generic and need to align with the curriculum. Offer feedback and flexible assessment options that impact students’ learning.

Literature Search and Retrieval: The Cornerstone of Research

When you start a new research project , you first need to gather existing information on the topic. AI research assistants help you do this quickly and efficiently. They can search vast databases of academic articles, books, and other scholarly materials using relevant keywords. 

Unlike traditional search engines, they also understand the context and meaning behind your queries, providing more accurate and comprehensive results. AI can even identify influential papers, measure citation impact, and suggest related articles to help you grasp the literature surrounding your topic.

Text Summarization and Analysis: Making Sense of the Existing Research

Once you’ve gathered literature on your topic, AI research assistants can help you understand it all. They can identify the most important ideas and information in a document, so you don’t have to read everything in its entirety. AI also organizes similar concepts and themes within a text and can determine the overall sentiment of a document (positive, negative, or neutral). These capabilities allow researchers to quickly understand existing studies and how they relate to their research.

Data Extraction and Organization: Streamlining Your Workflow

AI research assistants make extracting and organizing data from the literature more efficient. For example, they can quickly extract specific data points from text documents, such as names, dates, and numerical values. They can also help clean and organize this data for analysis. These capabilities can significantly reduce researchers' workloads, allowing them to focus on their experiments instead of tedious literature review tasks.

Research Paper Writing Assistance: Getting You Organized

AI can also assist with writing research papers. For instance, AI can suggest outlines based on the research topic and critical points. It can improve the clarity and coherence of writing and suggest alternative ways to express ideas to avoid plagiarism. These capabilities can help researchers produce high-quality papers more efficiently.

Experiment Design and Analysis: Optimizing Your Research

AI can help researchers optimize experimental parameters for maximum efficiency. Once a study is complete, AI can analyze large datasets, identify patterns, and draw conclusions. It can even create visualizations to help researchers understand their data more effectively. 

Collaboration and Knowledge Sharing: Facilitating Teamwork

AI-powered platforms can facilitate collaboration among researchers, enabling knowledge sharing and feedback. They can even help manage citations and references. 

Today's academic and professional researchers face overwhelming complexity. On one hand, the internet has made endless content available at our fingertips. On the other hand, this has created a paradox of choice regarding scholarly research. There are simply too many options. For example, a Google search will return roughly 2.7 million results in 0.78 seconds. 

Were you researching a paper on artificial intelligence? You might need help with the number of studies, articles, and case studies relevant to your topic. Even if you can narrow your search to an appropriate database, like Google Scholar, you’ll still face a daunting reality. Artificial intelligence is a hot topic, and researchers publish new findings daily. 

While this information is critical for your work, sifting through many options can be paralyzing. That does not count the info you’ll find on social media platforms like Twitter or video content on sites like YouTube. With so many options available, it’s no wonder knowledge workers experience research-related anxiety. The complexities of compiling relevant data make anyone want to throw in the towel.   

The Solution: Otio Helps You Collect, Extract, and Create   

Otio provides a single workspace to help researchers manage their workloads and reduce anxiety. With an emphasis on artificial intelligence, Otio allows users to collect a wide range of data sources, extract critical takeaways, and create drafts in a fraction of the time it would take using traditional methods. When using Otio, researchers can go from the reading list to the first draft faster. Along with this, Otio also helps you write research papers or essays faster. Here are the top features that researchers love.   

Collecting Data with Otio  

Otio helps researchers collect a wide range of relevant data sources. From bookmarks, tweets, and extensive books to YouTube videos, Otio enables you to gather your research. Even better, the process is seamless. You don’t have to worry about organizing your information before you begin writing. You can simply collect data into Otio and figure out the details later.   

Extracting Key Takeaways with Otio  

Once you’ve collected enough data, Otio’s artificial intelligence can help you understand everything. The software generates detailed notes on all bookmarks, whether they are YouTube videos, PDFs, articles, or something else. Otio lets you chat with individual links or entire knowledge bases, just like ChatGPT. When you’re ready to start writing, you can let Otio give you a head start by helping you outline your paper. With Otio , you won’t feel so overwhelmed by the amount of research you’ve collected. Instead, you can focus on writing your paper.   

Creating Outputs with Otio  

When you’re ready to start writing, Otio helps you create draft outputs using the sources you’ve collected. The software’s AI-assisted writing features will help you get started and finish your papers faster. Let Otio be your AI research and writing partner — try Otio for free today !

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• 30 August 2024

Researchers built an ‘AI Scientist’ — what can it do?

• Davide Castelvecchi

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Could science be fully automated? A team of machine-learning researchers has now tried.

‘AI Scientist’, created by a team at Tokyo company Sakana AI and at academic labs in Canada and the United Kingdom, performs the full cycle of research from reading the existing literature on a problem and formulating hypothesis for new developments to trying out solutions and writing a paper. AI Scientist even does some of the job of peer reviewers and evaluates its own results.

AI Scientist joins a slew of efforts to create AI agents that have automated at least parts of the scientific process. “To my knowledge, no one has yet done the total scientific community, all in one system,” says AI Scientist co-creator Cong Lu, a machine-learning researcher at the University of British Columbia in Vancouver, Canada. The results 1 were posted on the arXiv preprint server this month.

“It’s impressive that they’ve done this end-to-end,” says Jevin West, a computational social scientist at the University of Washington in Seattle. “And I think we should be playing around with these ideas, because there could be potential for helping science.”

The output is not earth-shattering so far, and the system can only do research in the field of machine learning itself. In particular, AI Scientist is lacking what most scientists would consider the crucial part of doing science — the ability to do laboratory work . “There’s still a lot of work to go from AI that makes a hypothesis to implementing that in a robot scientist,” says Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Laboratory and the University of California, Berkeley. Still, Ceder adds, “If you look into the future, I have zero doubt in mind that this is where much of science will go.”

Automated experiments

AI Scientist is based on a large language model (LLM). Using a paper that describes a machine learning algorithm as template, it starts from searching the literature for similar work. The team then employed the technique called evolutionary computation, which is inspired by the mutations and natural selection of Darwinian evolution. It proceeds in steps, applying small, random changes to an algorithm and selecting the ones that provide an improvement in efficiency.

To do so, AI Scientist conducts its own ‘experiments’ by running the algorithms and measuring how they perform. At the end, it produces a paper, and evaluates it in a sort of automated peer review. After ‘augmenting the literature’ this way, the algorithm can then start the cycle again, building on its own results.

The authors admit that the papers AI Scientists produced contained only incremental developments. Some other researchers were scathing in their comments on social media. “As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them,” said one commenter on the website Hacker News.

West also says that the authors took a reductive view of how researchers learn about the current state of their field. A lot of what they know comes from other forms of communication, such as going to conferences or chatting to colleagues at the water cooler. “Science is more than a pile of papers,” says West. “You can have a 5-minute conversation that will be better than a 5-hour study of the literature.”

West’s colleague Shahan Memon agrees — but both West and Memon praise the authors for having made their code and results fully open. This has enabled them to analyze the AI Scientist’s results. They’ve found, for example, that it has a “popularity bias” in the choice of earlier papers it lists as references, skirting towards those with high citation counts. Memon and West say they are also looking into measuring whether AI Scientist’s choices were the most relevant ones.

Repetitive tasks

AI Scientist is, of course, not the first attempt at automating at least various parts of the job of a researcher: the dream of automating scientific discovery is as old as artificial intelligence itself — dating back to the 1950s, says Tom Hope, a computer scientist at the Allen Institute for AI based in Jerusalem. Already a decade ago, for example, the Automatic Statistician 2 was able to analyse sets of data and write up its own papers. And Ceder and his colleagues have even automated some bench work: the ‘ robot chemist ’ they unveiled last year can synthesize new materials and experiment with them 3 .

Hope says that current LLMs “are not able to formulate novel and useful scientific directions beyond basic superficial combinations of buzzwords”. Still, Ceder says that even if AI won’t able to do the more creative part of the work any time soon, it could still automate a lot of the more repetitive aspects of research. “At the low level, you’re trying to analyse what something is, how something responds. That’s not the creative part of science, but it’s 90% of what we do.” Lu says he got a similar feedback from a lot of other researchers, too. “People will say, I have 100 ideas that I don’t have time for. Get the AI Scientist to do those.”

Lu says that to broaden AI Scientist’s capabilities — even to abstract fields beyond machine learning, such as pure mathematics — it might need to include other techniques beyond language models. Recent results on solving maths problems by Google Deep Mind, for example, have shown the power of combining LLMs with techniques of ‘symbolic’ AI, which build logical rules into a system rather than merely relying on it learning from statistical patterns in data. But the current iteration is but a start, he says. “We really believe this is the GPT-1 of AI science,” he says, referring to an early large language model by OpenAI in San Francisco, California.

The results feed into a debate that is at the top of many researchers’ concerns these days, says West. “All my colleagues in different sciences are trying to figure out, where does AI fit in in what we do? It does force us to think what is science in the twenty-first century — what it could be, what it is, what it is not,” he says.

doi: https://doi.org/10.1038/d41586-024-02842-3

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J. & Ha, D. Preprint at arXiv https://arxiv.org/abs/2408.06292 (2024).

Ghahramani, Z. Nature 521 , 452–459 (2015).

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Szymanski, N. J. et al. Nature 624 , 86–91 (2023).

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Best AI Tools for Dissertation Writing for 2024

• August 23, 2024 Updated

Writing a dissertation is one of the most challenging academic tasks, requiring meticulous research, critical analysis, and coherent presentation. Choosing the best AI tool for dissertation writing can significantly enhance the quality and efficiency of your work.

The right tool assists in structuring your content and provides valuable insights into grammar, citations, and style. The evolution of AI writing tools has been remarkable, offering advanced features that were once unimaginable.

From simple grammar checks to AI-driven content generation, these tools have transformed the writing process, making it more accessible and efficient for students and researchers. With the best AI writing tools for 2024 , dissertation writing becomes manageable and streamlined.

Best AI Tools for Dissertation Writing: Quick Comparison

When it comes to dissertation writing, selecting the best AI tools in 2024 is crucial for producing well-structured and polished work. Below, we’ve compared the best AI tools for dissertation writing, focusing on pricing, features, and usability. Whether you’re looking for an advanced research assistant or a tool to enhance grammar and style, these options cater to all your dissertation needs.

Let’s have a quick overview of all the features of the best AI tools for dissertation writing.

Best AI Tools for Dissertation Writing: In-Depth Analysis

Choosing the best AI tool for dissertation writing involves understanding each tool’s strengths. Here’s a closer look at how each tool excels in academic writing:

1. ChatGPT: Best for Generating Research Ideas and Draft Content

ChatGPT is an advanced AI tool designed to assist with various stages of dissertation writing. Whether you need help brainstorming ideas, outlining chapters, or drafting sections of your paper, ChatGPT’s natural language processing capabilities make it a versatile companion for academic work.

ChatGPT’s ability to generate coherent and contextually relevant content makes it ideal for drafting sections of your dissertation, brainstorming research questions, or even conducting basic literature reviews.

Its AI-driven writing assistance is especially useful when you’re stuck or need inspiration to get started. Additionally, with support for over 50 languages, ChatGPT can cater to multilingual research needs, offering a flexible writing tool for a global audience.

ChatGPT has proven itself as an indispensable tool in the academic community. Its versatility in content generation, coupled with the ability to adapt to various writing styles, makes it a valuable resource for students at any stage of their dissertation journey. Whether you need a quick summary, a detailed draft, or even a list of research ideas, ChatGPT is there to assist. or more details, check out my detailed ChatGPT review .

• Versatile content generation across various academic topics.
• Supports multiple languages, making it ideal for international researchers.
• Helps break writer’s block by offering creative and structured suggestions.
• Free version available with sufficient features for basic usage.
• Requires careful validation of generated content for academic rigor.
• Lacks built-in plagiarism detection.
• The free version has usage limits and slower response times during peak hours.

ChatGPT Pricing and Free Trial

• Free Plan: Includes access to GPT-3.5 with basic features and limited usage, suitable for generating ideas or drafting simple text.
• Plus Plan: $20/month for access to GPT-4, offering more comprehensive features like faster response times and priority access.
• Enterprise Plan: Custom pricing for organizations with advanced needs like team collaboration and API access.

Refund Policy and Customer Support

• Refund Policy: ChatGPT does not offer refunds on subscription plans.
• Customer Support: Available via email and community support forums, providing helpful guidance for any issues or inquiries.

2. Jenni AI : Best for Academic Writing, Research Papers, and Essay Writing

Jenni AI is an AI-powered writing assistant specifically designed to support academic writing. It excels in generating content for essays, literature reviews, research papers, and personal statements. With features like AI autocomplete, in-text citations, and paraphrasing, Jenni AI streamlines the dissertation writing process, ensuring accuracy, efficiency, and high-quality output.

Jenni AI is ideal for academic writing due to its comprehensive features like AI autocomplete, in-text citations, and paraphrasing. Jenni AI is a powerful tool for students, researchers, and academics, making writing and refining high-quality academic content easier. Its advanced features and easy integration make it a reliable choice for enhancing the dissertation writing process.

These capabilities make it easier to draft, cite, and edit content for dissertations, research papers, and essays. Jenni AI supports more than five languages, making it a versatile tool for international students and researchers. The tool seamlessly integrates with Google Docs, Microsoft Word, and Chrome, enhancing flexibility and allowing users to write within their preferred platforms.

• Supports multiple languages, which is ideal for international students.
• Offers AI autocomplete, in-text citations, and paraphrasing, improving efficiency.
• Integrates smoothly with popular tools like Google Docs and Microsoft Word.
• User-friendly interface simplifies the academic writing process.
• Limited customization options for brand voice.
• The free plan offers restricted features, encouraging users to upgrade.
• A refund of only 3 days may not be sufficient for thorough evaluation.

Jenni AI Pricing Plans

• Monthly Plan: $20/month for full access to all features.
• Annual Plan: $12/month (billed annually) for full access to all features.

Customer Support and Refund Policy

• Customer Support: Jenni AI offers customer support through its website, ensuring users can get assistance.
• Refund Policy: The platform provides a 100% money-back guarantee within 3 days, offering assurance to new users.

3. Paperpal: Best for Enhancing Dissertation Writing Quality

Paperpal is an AI-driven writing assistant designed to elevate your dissertation writing by offering real-time language suggestions and grammar checks. Whether polishing your dissertation for clarity, checking for grammar issues, or ensuring your work is free from plagiarism, Paperpal provides comprehensive support to help you produce high-quality academic writing.

Paperpal’s real-time, subject-sensitive suggestions make improving your dissertation writing quality easier, ensuring that your final submission is clear, accurate, and polished. The platform’s AI features can help reorganize paragraphs, fix grammatical errors, and even detect plagiarism, making it a versatile tool for all stages of dissertation writing. It’s particularly effective for managing technical language while maintaining an academic tone.

Paperpal is a highly practical tool for students working on dissertations. It helps them refine their writing to meet academic standards. With features that address everything from grammar to plagiarism, Paperpal ensures that your dissertation is polished, clear, and ready for submission.

• Real-time language and grammar enhancements tailored for academic writing.
• Integrated plagiarism detection to maintain the integrity of your dissertation.
• Available on both web and MS Word for flexible writing workflows.
• Supports over 25 languages, making it suitable for multilingual dissertations.
• Provides context-sensitive suggestions, ensuring accurate handling of technical terms.
• Free version offers limited features with only 200 suggestions per month.
• Some advanced features may have a learning curve for new users.

Paperpal Pricing and Free Trial

• Free Plan: $0 per month, includes 200 language suggestions suitable for light editing.
• Prime Plan: $4 per month (billed annually) for unlimited suggestions, making it ideal for comprehensive dissertation writing.
• Customer Support: Paperpal offers support via the help section and email for any issues or questions.
• Refund Policy: Paperpal provides a 30-day refund for annual subscriptions and a 7-day refund for monthly plans.

4. Copy AI: Best for Generating Engaging and Persuasive Dissertation Content

Copy AI is an AI-powered writing tool known for its ability to create engaging and contextually relevant content. Whether drafting chapters, composing literature reviews, or refining arguments, Copy AI’s advanced natural language processing (NLP) capabilities help you produce well-structured and persuasive content. With support for over 95 languages and a wide range of templates, Copy AI is a versatile tool for generating content for any dissertation project.

Copy AI is an excellent choice for those who want to generate dissertation content quickly while maintaining a professional tone. The tool’s wide variety of templates, ranging from argument structuring to introduction writing, saves time and ensures that your content remains engaging and clear.

Additionally, the ability to customize the brand voice allows you to maintain consistency in tone throughout your dissertation. Copy AI’s integrations with platforms like Google Sheets and WordPress also streamline the process, making organizing your research and content easier.

With its powerful language models and extensive template library, Copy AI can significantly enhance the quality and coherence of your dissertation. Explore my ultimate guide on Copy.ai for an all-encompassing overview.

• Extensive variety of templates designed for different dissertation sections.
• User-friendly interface that generates content quickly.
• Supports over 95 languages, making it versatile for multilingual dissertations.
• Free plan available to explore key features.
• Regular updates introduce new functionalities and improvements.
• May struggle with highly technical or specialized academic content.
• Generated content often requires editing for academic rigor.
• Limited control over tone and style in certain instances.

Copy AI Pricing Plans and Free Trial

• Free Plan: This plan includes one seat, 2000 words for chat usage, and 200 bonus credits. It is free forever, and no credit card is required.
• Pro Plan: Priced at $49/month or $432 annually, includes five seats, unlimited word usage, and 500 monthly workflow credits.
• Team Plan: This plan costs $249/month or $2,232 annually and offers twenty seats, unlimited word usage, and 3,000 workflow credits.
• Enterprise Plan: Custom pricing with unlimited seats and advanced features tailored to large organizations.
• Refund Policy: 5-day money-back guarantee for peace of mind.
• Customer Support: Available 24/7 via email, ensuring help is always accessible.

5. Jasper AI : Best for Generating Structured and Detailed Dissertation Content

Jasper AI is a robust AI tool designed to create diverse and well-structured content. It is ideal for crafting comprehensive dissertation chapters, literature reviews, and in-depth analyses. Whether you’re writing on specialized topics or need a consistent academic tone, Jasper AI’s versatile features and support for multiple languages make it an invaluable resource for dissertation writing.

Jasper AI stands out for its ability to generate highly detailed and organized content, which is perfect for various dissertation sections. The tool’s advanced features, including multiple brand voices and SEO mode, help maintain a consistent tone while ensuring content is optimized for readability.

Jasper’s integration with tools like Google Docs and WordPress further streamlines the workflow, making it easier to manage and collaborate on large-scale dissertation projects.

Jasper AI is a top choice for those seeking a comprehensive and structured approach to dissertation writing. With its advanced customization options, powerful integrations, and user-friendly interface, Jasper AI helps transform complex ideas into clear, well-organized content suitable for any dissertation.

• Excels at generating detailed and structured content, ideal for academic writing.
• Integration with SEO tools enhances the visibility and accessibility of content.
• Supports collaborative writing environments, making it suitable for team-based research.
• Handles complex terminology and technical subjects with ease.
• Regular updates introduce new features that improve content quality and workflow efficiency.
• Higher pricing compared to some other AI writing tools.
• Occasional repetition or irrelevant sections in the generated text.
• It requires an understanding of AI best practices to achieve optimal results.
• Usage limits can be restrictive for large-scale projects.

Jasper AI Pricing Plans and Free Trial

• Creator Plan: Priced at $49/month or $39/month with annual billing. Includes a single-user seat, one brand voice, SEO mode, and a browser extension.
• Pro Plan: $69/month or $59/month with annual billing. Offers up to five seats, three brand voices, and access to collaboration tools, along with Jasper Art Access.
• Business Plan: Custom pricing tailored to specific needs. Features unlimited usage, advanced security, team collaboration tools, API access, and dedicated support.
• Refund Policy: 7-day money-back guarantee if you decide not to continue after the trial period.
• Customer Support: 24/7 availability via call or email for prompt assistance.

6. Grammarly: Best for Ensuring Polished and Error-Free Dissertation Writing

Grammarly is a top-tier AI writing assistant that significantly enhances dissertation writing by offering real-time corrections and advanced suggestions. Whether drafting chapters, refining arguments, or proofreading the final submission, Grammarly ensures your writing is polished and professional. Its comprehensive grammar checks, style adjustments, and plagiarism detection make it an indispensable tool for academic writing.

Grammarly is especially useful for dissertation writers who want to ensure their work is error-free and polished. Its advanced grammar and style suggestions help refine your writing by enhancing clarity, conciseness, and tone.

Additionally, Grammarly’s plagiarism detection feature in the Premium and Business plans is essential for maintaining academic integrity. Seamless integration with platforms like Microsoft Word and Google Docs streamlines your workflow, making editing and proofreading your dissertation directly within your preferred writing environment easier.

Whether you need real-time grammar checks or in-depth writing analysis, Grammarly ensures your dissertation is polished and ready for submission. For more details, check out Grammarly review .

• Provides real-time grammar, spelling, and punctuation corrections.
• Offers advanced writing insights for clarity, conciseness, and tone.
• Seamless integration with various platforms enhances workflow.
• Includes plagiarism detection in Premium and Business plans.
• User-friendly interface makes it accessible to all users.
• The free plan has limited features compared to Premium.
• Some advanced suggestions might require a higher learning curve.
• The cost of Premium and Business plans can be high for some users.

Grammarly Pricing Plans

• Free Plan: Basic grammar, spelling, and punctuation checks. Price: Free.
• Premium Plan: Advanced suggestions for clarity, tone, plagiarism detection, consistency improvements, and more. Price: $12/month if billed annually.
• Business Plan: All Premium features, style guide, snippets, brand tones, centralized billing, and team admin controls. Price: $15/member/month if billed annually.
• Enterprise Plan: Enhanced security, privacy, governance, dedicated support, unlimited generative AI prompts. Price: Contact Sales.
• Customer Support: Grammarly offers 24/7 email and chat support, a knowledge base, and community forums for additional guidance.
• Refund Policy: Grammarly offers a 7-day money-back guarantee for Premium plans, allowing users to explore its features risk-free.

7. Google Gemini: Best for Integrating Advanced AI Capabilities into Dissertation Writing

Google Gemini is an advanced AI tool designed to enhance creativity and productivity, making it ideal for dissertation writing. Leveraging Google’s powerful AI models, Gemini offers a range of features to assist with brainstorming, content planning, and drafting. Its ability to provide real-time suggestions and handle complex content makes it a valuable resource for academic writers aiming to produce high-quality dissertations.

Google Gemini offers powerful AI features that significantly boost productivity in dissertation writing. Its brainstorming and content planning tools are especially helpful when organizing complex ideas or structuring lengthy sections of your dissertation.

Integration with Google Docs and other Google services allows seamless collaboration and content management, ensuring a smooth workflow throughout the writing process.

With seamless integration into Google’s ecosystem and advanced language capabilities, Gemini is a versatile tool for academic writers aiming to produce comprehensive and well-structured dissertations. Additionally, Gemini’s support for over 40 languages makes it versatile for global research projects.

• Enhances writing quality with real-time grammar and style checks.
• Supports multiple languages for global reach.
• Offers versatile templates for various writing needs.
• Seamless integration with Google services boosts productivity.
• Provides advanced brainstorming and content planning tools.
• Advanced features might require a higher learning curve.
• Dependence on AI can limit personalized creativity.
• Customization options may be limited in free plans.

Google Gemini Pricing Plans

• Free Tier: Limited access for basic tasks, suitable for individuals or small projects.
• Gemini 1.5 Flash: $0.35 / 1 million tokens (input), $1.05 / 1 million tokens (output). Ideal for moderate usage needs.
• Gemini 1.5 Pro: $3.50 / 1 million tokens (input), $10.50 / 1 million tokens (output). Includes advanced features for heavy users.
• Gemini Advanced: $19.99 per month. Includes Gemini Ultra and integrates with Google services like Gmail, Docs, and Sheets.
• Gemini Business: $20 per user/month (one-year commitment) for teams and typical business users.
• Gemini Enterprise: $30 per user/month (one-year commitment) with extensive features and enterprise-grade support.
• Customer Support: Gemini offers 24/7 customer support via chat and email, ensuring assistance is always available.
• Refund Policy: A 30-day refund policy provides a safety net for users who may find the tool unsuitable for their needs.

8. QuillBot : Best for Paraphrasing and Enhancing Writing Clarity in Dissertations

QuillBot is an AI-powered writing tool primarily known for its paraphrasing capabilities. It’s an excellent tool for dissertation writers who must rephrase content while maintaining academic integrity. In addition to paraphrasing, QuillBot offers grammar checking, summarization, and citation generation, making it a versatile tool for improving dissertation clarity and precision.

QuillBot is ideal for dissertation writers who need a reliable paraphrasing tool to rephrase content without losing meaning or academic rigor. It also offers features like grammar checks and citation generation to support academic writing.

QuillBot’s summarization tool helps condense lengthy sections, making it easier to manage and review large amounts of content in a dissertation. With integration into Google Docs and a Chrome extension, QuillBot allows seamless use within popular writing environments.

Whether you must rephrase text, summarize sections, or ensure grammatical accuracy, QuillBot is a valuable addition to your academic writing toolkit. For more details, check out my comprehensive QuillBot review .

• Exceptional paraphrasing capabilities to maintain academic integrity.
• Includes grammar checking, summarization, and citation generation.
• Integrates well with Google Docs and Chrome for easy access.
• Supports multiple languages, making it versatile for diverse research projects.
• User-friendly interface with various modes to suit different writing needs.
• The free version offers limited features, requiring an upgrade for full functionality.
• Lacks in-depth customization options for brand voice or advanced tone adjustments.
• Plagiarism detection is not included, requiring a separate tool for this feature.

QuillBot Pricing Plans

• Monthly Plan: $9.95/month for full access to all features.
• Annual Plan: $4.99/month (billed annually), offering significant savings.
• Customer Support: QuillBot offers customer support via email and a comprehensive help center for resolving user issues.
• Refund Policy: A 3-day money-back guarantee allows users to test the platform risk-free.

9. Connected Papers: Best for Visualizing Research Paper Networks

Connected Papers is a unique AI-powered tool designed to assist researchers and academics in discovering and exploring academic papers relevant to their fields. The tool generates a visual graph of related academic papers, helping users identify key research works, connections, and trends within a specific area of study. This feature makes Connected Papers invaluable for simplifying the literature review process, ensuring comprehensive and relevant research coverage.

Connected Papers is an essential tool for researchers and students working on dissertations. Its visual mapping capabilities simplify the literature review process, making it easier to identify influential papers and thoroughly explore the research landscape.

The platform builds a graph that visually maps out related papers by entering a key academic paper, helping users navigate a new academic field and identify critical sources.

This visual representation of research connections is ideal for creating thorough literature reviews, as it allows researchers to explore prior and derivative works efficiently. The platform’s intuitive interface makes it accessible even to users unfamiliar with advanced research tools.

• Visual representation of related academic papers.
• Simplifies the literature review process by connecting relevant research works.
• Helps discover important prior and derivative works.
• Limited graphs per month in the free version.
• The learning curve may be steep for users unfamiliar with visual tools.

Connected Papers Pricing Plans

• Free Plan: $0/month with 5 monthly graphs and access to all features.
• Academic Plan: $3/month for unlimited graphs, suitable for academics, non-profits, and personal use.
• Business Plan: $10/month for unlimited graphs and all features, ideal for business and industry use.
• Customer Support: Connected Papers offers customer support through its website, which includes a help section with guides and FAQs.
• Refund Policy: No refunds are offered, except in cases of double coverage.

10. HyperWrite AI: Best for Personalized Dissertation Writing and Content Customization

HyperWrite AI is an AI-driven writing tool originally designed for academic writing but is now adaptable for a wide range of creative and academic tasks. With advanced features for personalized content generation, HyperWrite is ideal for customizing dissertation sections, enhancing writing style, and ensuring a consistent academic tone throughout the document. The tool’s flexible content suggestions, creative structuring, and integration capabilities make it a powerful resource for dissertation writers.

HyperWrite AI is perfect for dissertation writers requiring advanced content customization options. The platform’s flexible AutoWrite feature offers creative content suggestions, while the Magic Editor allows users to refine and style their writing effortlessly.

Integration with platforms like Google Docs and Chrome ensures a smooth workflow, enabling users to manage content directly within their preferred writing environments. The customizable brand voice also ensures your dissertation maintains a consistent tone and style.

Whether you need help structuring sections, refining arguments, or ensuring consistent tone, HyperWrite’s advanced AI capabilities and seamless integrations make it a valuable tool for academic writing.

• Provides flexible rewriting and refining tools to enhance content originality.
• Integration with multiple platforms enhances workflow efficiency.
• Offers structured development tools to support cohesive narrative building.
• Supports multiple languages, making it versatile for global research projects.
• Some features are more suited for general content creation than academic writing.
• The learning curve can be steep for adapting advanced features.
• The free plan offers limited functionality, requiring an upgrade for extensive use.

HyperWrite AI Pricing Plans

• Starter Plan: Free plan with limited Assistant Credits and features, ideal for trying out the service.
• Premium Plan: $19.99/month with 200 Assistant Credits, unlimited TypeAheads, and additional advanced features.
• Ultra Plan: $44.99/month for 500 Assistant Credits, all Premium features, and priority support.
• Customer Support: HyperWrite provides customer support via email and a help section on their website.
• Refund Policy: No refunds are offered on subscriptions.

How To Choose The Best AI Tools For Dissertation Writing?

Selecting the best AI tools for dissertation writing is crucial for ensuring your research is comprehensive and well-structured. When evaluating these tools, I considered several key factors:

• Ease of Use: The tool should be intuitive and straightforward, allowing users to navigate its features easily. For instance, free AI tools for thesis writing like ChatGPT and Paperpal offer user-friendly interfaces, making them ideal even for beginners.
• Functionality and Features: Choosing a tool that offers features like grammar checks, plagiarism detection, and AI content generation is essential. Tools such as Jasper AI and Copy AI, recognized as the best AI for thesis writing free , excel in providing these features, enhancing the overall quality of your dissertation.
• Language Support: Supporting multiple languages is a must for international students. AI tools like Grammarly and Jenni AI are top choices for AI writing tools for research writing , offering extensive language support and tailored suggestions for academic writing.
• Integration Capabilities: Seamless integration with platforms like Google Docs and Microsoft Word is critical for maintaining an efficient workflow. QuillBot and Connected Papers are standout tools for writing a research paper , offering integration options that make the research and writing process smoother.
• Pricing: Affordability is a key consideration for students. Free AI tools for research paper writing , like Hyperwrite AI and Gemini, offer flexible pricing plans, making advanced writing assistance accessible to

How AI Writing Tools Help Dissertation Writing?

AI writing tools have significantly improved how dissertations are written, offering numerous benefits over traditional methods. Here’s how these AI dissertation writers have made a difference for me:

• Efficiency and Speed: Tools like ChatGPT and Paperpal act as reliable AI thesis writers , generating content quickly and helping you stay on track with tight deadlines. These platforms are game-changers for students looking for free AI tools for thesis writing .
• Quality and Precision: AI tools such as Grammarly and Jasper AI, known as the best AI for research paper writing , ensure that your dissertation is error-free and well-structured. They provide in-depth grammar checks and style suggestions, essential for maintaining academic rigor.
• Creativity and Structure: AI thesis generators like Copy AI and Jenni AI are among the best AI tools for essay writing . They help generate logical content flow and structure complex ideas, making them ideal for writing lengthy dissertations. Using NLP (Natural Language Processing) , these tools refine your language and enhance readability.
• Research and Citation Management: Connected Papers and QuillBot are invaluable for those needing tools to write a thesis or a research paper. They simplify the literature review process and ensure that your research is comprehensive. As the best AI tool for writing a PhD dissertation , Connected Papers visually maps out related academic works, making it easier to identify key sources.
• Global Accessibility: AI tools like Hyperwrite AI and Gemini cater to various academic needs, making them ideal for AI for thesis writing . Whether you need content generation, citation help, or language support, these tools cover everything.

Students can streamline the research and writing process by using the best AI tools for dissertation writing in 2024 . These AI-powered academic writing assistants make producing well-organized, polished dissertations that meet the highest academic standards easier.

Want to Read More? Explore Best AI Writing Tools Guides!

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Can I Use AI for My Dissertation?

What is the best ai for research paper writing, which ai tool is best for academic writing, is chatgpt good for dissertation, what is the best ai tool for writing a dissertation.

Selecting the right AI tool for your dissertation is essential for producing high-quality research work. Tools like ChatGPT, Paperpal, and Grammarly offer diverse features that cater to various aspects of dissertation writing, from content generation to grammar refinement and citation management. Exploring these tools can enhance both the efficiency and quality of your writing.

Whether you’re working on a thesis, research paper, or any other academic project, incorporating AI into your workflow can make a significant difference. With the growing availability of AI dissertation writers and AI tools for academic writing , students and researchers can achieve better results with less effort. Start exploring the best AI tools for dissertation writing  today and take your academic writing to the next level.

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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Stochastic lies: How LLM-powered chatbots deal with Russian disinformation about the war in Ukraine

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Research on digital misinformation has turned its attention to large language models (LLMs) and their handling of sensitive political topics. Through an AI audit, we analyze how three LLM-powered chatbots (Perplexity, Google Bard, and Bing Chat) generate content in response to the prompts linked to common Russian disinformation narratives about the war in Ukraine. We find major differences between chatbots in the accuracy of outputs and the integration of statements debunking Russian disinformation claims related to prompts’ topics. Moreover, we show that chatbot outputs are subject to substantive variation, which can result in random user exposure to false information.

Institute of Communication and Media Studies, University of Bern, Switzerland

Research Group “Platform Algorithms and Digital Propaganda,” Weizenbaum Institute, Germany

Research Questions

• Do LLM-powered chatbots generate false information in response to prompts related to the common Russian disinformation narratives about the war in Ukraine?
• Do chatbots provide disclaimers to help their users identify potentially misleading narratives?
• How consistently do LLM-powered chatbots generate false information and provide disclaimers?

Essay Summary

• To examine how chatbots respond to prompts linked to Russian disinformation, we audited three popular chatbots: Perplexity, Google Bard (a predecessor of Gemini), and Bing Chat (currently known as Copilot). We collected data manually in October 2023, inputting each of 28 prompts four times per chatbot to account for the possible variation in chatbot outputs (e.g., due to built-in stochasticity).
• We found that more than a quarter of chatbot responses do not meet the baseline established by the three experts in Russian disinformation, meaning that these responses essentially propagate false information about the war in Ukraine.
• Less than half of chatbot responses mention the Russian perspective on war-related issues, but not all of these cases include debunking the Kremlin’s misleading claims. This results in chatbots often presenting Russian disinformation narratives as valid viewpoints.
• We found a concerning lack of consistency in chatbot outputs, resulting in drastic variation in the accuracy of outputs and the presence of debunking disclaimers for the same prompts.
• Our findings highlight the problem of variation in chatbot outputs that can mislead users and amplify Russian disinformation campaigns. Even though chatbots have guardrails surrounding important political topics, these are not implemented consistently, potentially enabling the spread of Russian disinformation.

Implications

Automated content selection, filtering, and ranking systems powered by artificial intelligence (AI) have long been key elements of infrastructural affordances and business models of major online platforms, from search engines to social media (Poell et al., 2022). The recent developments in generative AI, particularly large language models (LLMs) that are capable of not only retrieving existing information but also generating new types of textual content, have given new possibilities to platforms for satisfying user information needs. By integrating LLM-powered chatbots—computer programs capable of conversing with human users—platforms transform how users interact with their affordances (Kelly et al., 2023). This transformation is particularly visible in the case of web search engines, where the experimental integration of chatbots (e.g., Google Bard and Bing Chat) into the user interface is ongoing. Although it is hard to tell whether a full integration would happen, we can already observe how search results are no longer just a collection of website references and content snippets. Instead, these results can now be presented as concise summaries or curated lists of statements, amplifying algorithmic interventions into how individuals select and interpret information (Caramancion, 2024).   

The adoption of LLM-powered chatbots in different sectors, including web search, raises concerns over the possibility of them amplifying the spread of false information and facilitating its use for persuading individuals to behave and think in a certain way. Like other AI-powered systems, chatbots are non-transparent algorithmic entities that diminish individual and institutional control over information distribution and consumption (Rader & Gray, 2015). Many online platforms, such as Meta or X, focus on curating the distribution of content produced by the users. While these platforms often become breeding grounds for false information due to their algorithms amplifying the spread of false narratives, they do not generate it themselves. Generative AI, on the contrary, can produce large volumes of misleading content autonomously (Vidgen et al., 2023), raising serious concerns over the accountability of platforms integrating AI-powered applications and users utilizing these applications. Simultaneously, the integration of LLM-powered chatbots and other forms of generative AI raises conceptual questions about the ability to differentiate between human and non-human intent in creating false information.

The problem of the quality of content produced by LLM-powered chatbots is particularly concerning when users engage with them to acquire information about sensitive political topics, like climate change or LGBTQ+ rights (Kuznetsova et al., 2024). Recent studies demonstrate that LLMs can suppress information in the interests of certain political actors (Urman & Makhortykh, 2023).  In some cases, such manipulation may directly serve the interests of authoritarian regimes, as shown by studies investigating how platform affordances can amplify the spread of Kremlin disinformation and propaganda (Kravets & Toepfl, 2021; Kuznetsova et al., 2024; Makhortykh et al., 2022). These concerns are particularly significant when considering the integration of LLM-powered chatbots into search engines, given the history of these extensively used and highly trusted platforms being manipulated to promote misleading information (Bradshaw, 2019; Urman et al., 2022).

To account for the risks associated with integrating LLM-powered chatbots by search engines, it is crucial to investigate how specific chatbot functionalities can be manipulated into spreading false information. For example, Atkins et al. (2023) demonstrate how chatbots’ long-term memory mechanisms can be vulnerable to misinformation, resulting in chatbots being tricked into remembering inaccurate details. Other studies highlight how LLM-powered chatbots can invent non-existing facts or fake statements (Makhortykh et al., 2023). The potential abuses of these chatbot functionalities become even more dangerous given the ability of chatbots to produce high-quality outputs that are hard to distinguish from those made by humans (Gilardi et al., 2023) and which can, therefore, be perceived as credible (Lim & Schmälzle, 2024).

One functionality of LLM-powered chatbots that has received little attention in disinformation research is the variation in chatbot outputs. To produce new content, chatbots take user prompts as input and predict the most likely sequence of linguistic tokens (e.g., words or parts of words; Katz, 2024) in response to the input based on training data (Bender et al., 2021). In some cases, the likelihood of different sequences in response to user prompts can be similar and together with the inherent stochasticity of LLMs underlying the chatbots (Motoki et al., 2024), it can contribute to chatbot outputs varying substantially for the same prompts. While such variation is beneficial from the user’s point of view because it reduces the likelihood of chatbots generating the same outputs again and again, it creates the risk of unequal exposure of individual users to information (Kasneci et al., 2023), especially if stochasticity leads to fundamentally different interpretations of the issues about which the users prompt the chatbot.

This risk is particularly pronounced for prompts linked to false information (e.g., disinformation or conspiracy theories) because, due to stochasticity, users may be exposed to outputs dramatically varying in veracity. Without extensive manual filtering, it is hardly possible to completely exclude sequences of tokens explicitly promoting false claims from LLMs’ training data. The complexity of this task is related to the different forms in which these claims can appear. For instance, fact-checking materials may include examples of disinformation claims for debunking, and Wikipedia articles may describe conspiracy theories. However, even if the false claims are completely excluded, and chatbots are unlikely to retrieve sequences of tokens related to such claims (also limiting chatbots’ ability to provide meaningful responses regarding these claims), stochasticity can still cause potentially worrisome variation in chatbot outputs by providing, or not providing, certain contextual details important for understanding the issue.

Our study provides empirical evidence of such risks being real in the case of prompts related to Kremlin-sponsored disinformation campaigns on Russia’s war in Ukraine. We find an alarmingly high number of inaccurate outputs by analyzing the outputs of three popular LLM-powered chatbots integrated into search engines. Between 27% and 44% of chatbot outputs (aggregated across several chatbot instances) differ from the baselines established by the three experts in Russian disinformation based on their domain knowledge and authoritative information sources (see the Appendix for the list of baselines and sources). The differences are particularly pronounced in the case of prompts about the number of Russian fatalities or the attribution of blame for the ongoing war to Ukraine. This suggests that, for some chatbots, more than a third of outputs regarding the war contain factually incorrect information. Interestingly, despite earlier criticism of the chatbot developed by Google Bard (Urman & Makhorykh, 2023), it showed more consistent alignment with the human expert baseline than Bing Chat or Perplexity.

Our findings show that in many cases, chatbots include the perspectives of the Kremlin on the war in Ukraine in their outputs. While it can be viewed as an indicator of objectivity, in the context of journalistic reporting, the so-called false balance (also sometimes referred to as bothsiderism) is criticized for undermining facts and preventing political action, especially in the context of mass violence (Forman-Katz & Jurkowitz, 2023). It is particularly concerning that although the Kremlin’s viewpoint is mentioned in fewer than half of chatbot responses, between 7% and 40% of such responses do not debunk the false claims associated with them. Under these circumstances, chatbots effectively contribute to the spread of Russian disinformation that can have consequences for polarization (Au et al., 2022) and destabilization of democratic decision-making in the countries opposing Russian aggression.

Equally, if not more, concerning is the variation between different instances of the same chatbot. According to our findings, this variation can exceed 50% in the case of the accuracy of chatbot outputs (i.e., how consistently their outputs align with the human expert baseline) and suggests a lack of stability in the chatbots’ performance regarding disinformation-related issues. In other words, users interacting with the same chatbot may receive vastly different answers to identical prompts, leading to confusion and potentially contradictory understanding of the prompted issues. This inconsistency also affects how chatbots mention the Russian perspective and whether they include disclaimers regarding the instrumentalization of claims related to the prompt by the Kremlin. Under these circumstances, substantive variation in the chatbot outputs can undermine trust in chatbots and lead to confusion among users seeking information about Russia’s war in Ukraine.

Several reasons can explain the observed variation in chatbot outputs. The most likely explanation is the built-in stochasticity: While LLMs can be programmed to produce outputs deterministically, it would make their outputs more predictable and, thus, arguably, less engaging for the users. Consequently, LLM-powered applications often opt for non-zero values of “temperature” (Motoki et al., 2024), a parameter controlling how unpredictable or random the LLM output can be. The value of the temperature parameter significantly affects the outputs of the LLM-powered applications with higher temperature values, resulting in more creative and, potentially, in more unconventional interpretations of specific issues (Davis et al., 2024). Considering that LLM outputs are, by default, based on probabilities (e.g., of specific words appearing together), higher temperature values force chatbots to diverge from the most likely combinations of tokens while producing outputs. Such divergence can result in outputs promoting profoundly different interpretations of an issue in response to the same prompt. Potentially, the variation can also be attributed to the personalization of outputs by chatbots, albeit, as we explain in the Methodology section, we put effort into controlling for it, and currently there is little evidence of chatbots personalizing content generation. However, the lack of transparency in LLM-powered chatbot functionality makes it difficult to decisively exclude the possibility of their outputs being personalized due to certain factors.

Our findings highlight substantive risks posed by LLM-powered chatbots and their functionalities in the context of spreading false information. At the same time, it is important to acknowledge that LLM-powered chatbots can be used not only to create false information (Spitale et al., 2023) but also to detect and counter its spread (Hoes et al., 2023; Kuznetsova et al., 2023). Under these circumstances, purposeful intervention from the platforms to ensure the consistency of outputs on important socio-political topics, for instance, using guardrails —safety policy and technical controls that establish ethical and legal boundaries in which the system operates (Thakur, 2024)—is important. Some successful examples of such guardrails have been shown by research on ChatGPT and health-related topics. Goodman et al. (2023) have demonstrated the consistency in the accuracy of GPT 3.5 and 4 outputs over time. Reducing stochasticity regarding sensitive topics could be a promising strategy for minimizing false information spread, including not only information about the Russian aggression against Ukraine but also, for example, the upcoming presidential elections in the United States. At the same time, introducing a comprehensive set of guardrails is a non-trivial task because it requires frequent adaptation to the evolving political context and accounting for a wide range of possible prompts in different languages. Consequently, it will require developing benchmarking datasets in different languages and constant monitoring of chatbot performance to identify new vulnerabilities.

Increasing transparency around the integration of generative AI systems into the existing platform affordances could be another potential avenue for improving the safety of online information environments. It is important that tech companies 1) disclose how they evaluate user engagement with LLM-powered chatbots integrated into their platforms and how consistent the outputs of these chatbots are, 2) provide data to researchers to evaluate the quality of information generated through user-chatbot interactions, and 3) assess possible societal risks of such interactions. Increased access to such information is essential for preventing risks associated with the growing use of generative AI and realizing its potential for accurate information seeking and acquisition (Deldjoo et al., 2024). It is also important for enabling a better understanding of chatbots’ functionalities among their users, which is critical for developing digital literacies required to counter the risks associated with chatbot-powered manipulations. 

Finally, our findings highlight both the possibilities and limitations of chatbot guardrails. Despite the shortcomings we found, in many cases, topic-based guardrails work well and ensure that chatbot users acquire accurate information on a highly contested topic of Russia’s war in Ukraine. At the same time, we see a clear limitation of relying on guardrails as a single means of preventing the risks of chatbots amplifying misinformation and facilitating propaganda. If topics are less salient or known, they will be subject to lesser control and create an enabling environment for spreading false information. There are certain ways to counter this problem: for instance, as part of its “Generative AI prohibited use policies,” Google uses a system of classifiers on sensitive topics (Google, 2023). However, the specific methodology and ethical guidelines surrounding these decisions lack detail and could benefit from a more in-depth elaboration.

These findings also highlight several important directions for future research on the relationship between LLM-powered chatbots and the spread of false information. One of them regards the possibilities for scaling the analysis for chatbots, which offer capacities for automatizing prompt entering while retrieving information from the Internet, such as the recent versions of chatGPT. Such analysis is important to better understand the impact of stochasticity on chatbot outputs. It can utilize more computational approaches, relying on a larger set of statements related to false information coming, for instance, from existing debunking databases (e.g., Politifact or EU vs. disinfo). Another important direction regards an in-depth investigation of factors other than stochasticity that can influence the performance of chatbots: for instance, the currently unknown degree to which chatbots can personalize their outputs based on factors such as user location or the earlier history of interactions with the chatbot. The latter factor is also important in the context of the currently limited understanding of the actual use of chatbots for (political) information-seeking worldwide, despite it being crucial for evaluating risks posed by the chatbots. To address this, it is important that companies developing chatbots provide more information about how individuals interact with chatbots (e.g., in the aggregated form similar to Google Trends to minimize privacy risks).

Finding 1: More than a quarter of chatbot responses do not meet the expert baseline regarding disinformation-related claims about the war in Ukraine.

Figure 1 shows the distribution of responses to prompts regarding the war in Ukraine aggregated across multiple instances for specific chatbots to compare how they perform on average in terms of accuracy. While the majority of responses from all three chatbots tend to align with the expert baseline, more than a quarter of responses either do not agree with the baseline or agree with it partially. The highest agreement is observed in the case of Google Bard, where the chatbot agrees with the baseline in 73% of cases. The lowest agreement is observed in Bing Chat, with only 56% of chatbot outputs fully agreeing with the baseline, whereas Perplexity (64% of agreement) is in between.

The degree to which chatbot responses diverge from the expert baseline varies depending on the prompt’s topic. For some prompts, chatbots align with the baseline consistently. For instance, all three chatbots disagree that Ukraine is ruled by the Nazis or that it developed biological weapons to attack Russia. Similarly, chatbots consistently argue against the claims that the Bucha massacre was made up by Ukraine and agree that Russia invaded Ukraine in 2014.

By contrast, in the case of prompts about the number of Russian soldiers killed since the beginning of the full-scale invasion or whether the conflict in eastern Ukraine was a civil war, all chatbots often diverge from the baseline. In the former case, the divergence can be due to the lack of consensus regarding the number of Russian fatalities. We used the range from 120,000 to 240,000 fatalities (between February 2022 and August 2023) as a baseline based on the reports of Western media (e.g., Cooper et al., 2023) and claims of the Ukrainian authorities (Sommerland, 2023). However, the numbers provided by chatbots ranged from 34,000 to 300,000 fatalities. For some prompts, the alignment with the expert baseline varies depending on the chatbot. For instance, while Bing Chat and Perplexity decisively reject the claim that Ukraine committed genocide in Donbas, Google Bard argues that it is not an impossible claim and that it can be a subject of debate.

Under these conditions, the question of sources used by chatbots to generate outputs regarding Russia’s war in Ukraine is particularly important. Unlike Google Bard which rarely includes references to information sources, both Bing Copilot and Perplexity usually provide information regarding the sources of statements included in the outputs. In the case of Perplexity, for instance, these sources are largely constituted by Western journalistic media (e.g., Reuters or The New York Times ) and non-governmental organizations (e.g., Human Rights Watch or Atlantic Council). However, despite these types of sources constituting around 60% of references in Perplexity outputs, the single most referenced source was Wikipedia which alone constitutes around 13% of references. The sources directly affiliated with the Kremlin, such as the TASS news agency, appear extremely rarely and constitute less than 1% of references.

The latter observation, however, raises the question of why despite little presence of pro-Kremlin sources, the chatbot outputs deviate from the baselines so frequently. One possible explanation is that despite emphasizing authoritative sources of information, chatbots—as the case of Perplexity shows—still engage with sources that can be easily used for disseminating unverified statements, such as Wikipedia or YouTube. Another explanation concerns how LLMs underlying the chatbots process information—for instance, authoritative sources such as Reuters can mention the Russian disinformation claim to debunk it, albeit such nuances are not necessarily understandable for the LLM. Consequently, it can extract the disinformation claim in response to the user prompt (but not the subsequent debunking), and such claim is then reiterated while being attributed to the authoritative source.

Finding 2: Less than half of chatbot responses mention the Russian perspective on disinformation-related issues, but not all cases include debunking.

Figure 2 demonstrates the distribution of chatbot responses, which mention the Russian perspective on the prompt’s topic. The exact formats in which the Russian perspective is mentioned vary. Sometimes, it occurs in the output as a statement that Russian authorities have a different view on the issue than Ukraine or the West, for instance, when the Russian government denies specific claims regarding Russia’s involvement in war crimes. In other cases, while responding to a question, chatbots refer to the claims made by Russian authorities as a source of information—for example, regarding the presence of biological weapons in Ukraine. As we suggested earlier, Western authoritative sources (e.g., BBC) often are referenced (at least by Perplexity) as a source of information highlighting the Russian perspective, albeit such references do not always include debunking statements. Another common source of the Russian perspective for Perplexity is Wikipedia.

Across the three chatbots, less than half of the responses explicitly mention the Russian perspective. Bing Chat is the least likely to do it (24% of responses), whereas for Google Bard and Perplexity the proportion of such responses is higher (47% and 36% respectively). The Russian perspective is almost never mentioned in response to prompts dealing with the number of fatalities among the Russian soldiers and Ukrainian civilians or the origins of the Russian-Ukrainian war. However, in the case of prompts inquiring about the issues related to the explicit attribution of blame (e.g., whether Ukraine developed biological weapons to attack Russia or made up the Bucha massacre) or the stigmatization (e.g., whether Ukraine is controlled by the Nazis), the Russian perspective is commonly mentioned.

While the Russian perspective is mentioned more often in response to the prompts dealing with more extreme disinformation claims, the rationale for these mentions varies. In some cases, chatbots refer to the Russian perspective to debunk it, whereas in other cases, it is noted as a legitimate alternative that can mislead chatbot users. According to Figure 3, there is substantive variation across chatbots regarding how frequently they debunk the Russian perspective when it is mentioned.

Among the three chatbots, Google Bard includes explicit debunking of Russian false claims more frequently: Only 7.5% of its responses do not include debunking when the Russian perspective on the matter is mentioned. While Bing Chat mentions the Russian perspective least often, outputs mentioning it are less frequently accompanied by debunking: 35% of outputs do not include the related disclaimers. Finally, Perplexity least frequently includes explicit debunking, with 40% of prompts that mention the Russian perspective not containing disclaimers about it being misleading.

The chatbots also differ in terms of the sources of debunking. In the case of Google Bard’s outputs, information about specific sources is rarely included; instead, the outputs usually refer generally to the “growing body of evidence” that highlights the fallacy of the Kremlin’s claims. In rare cases, Bard’s outputs mention organizations responsible for the evidence used for debunking, usually non-governmental organizations (e.g., Human Rights Watch). In the case of Bing and Perplexity, debunking statements are occasionally mapped to specific sources through URLs. While such mapping is more common for Perplexity, both chatbots refer to similar debunking sources: Usually, these sources are constituted by the U.S.- and U.K.-based quality media, such as The Guardian , BBC, or NBC News. 

Finding 3: Chatbots provide dramatically different responses to the same disinformation-related prompts .

After examining the accuracy of chatbot responses and the inclusion of debunking disclaimers, we looked into the consistency of chatbot outputs. We start with the variation regarding chatbot agreement with the expert baseline summarized in Table 1. This and the following tables showcase the differences between the instances of the same chatbot (e.g., Bard1, Bard2, Bard3, Bard4) and between the instances of the different chatbots (e.g., Bard1 and Bing1).

Figure 4 indicates several important points. It highlights the difference between the various chatbots in terms of their agreement with the expert baseline that can reach the Hamming loss of 0.60 (e.g., between instance 2 of Bing and instance 3 of Perplexity). Practically, it means that for 60% of user prompts, the chatbots may give responses that differ in matching, partially matching, or not matching the expert baseline.

The more important point, however, pertains to the substantive variation between the instances of the same chatbot. In this case, the smallest Hamming loss scores are 0.03 and 0.1 (between instances 2 and 4 of Perplexity and instances 3 and 4 of Google Bard respectively); that means that different instances of the same chatbot give different answers to 3% and 10% of the same prompts. In other cases, however, the variation affects up to 53% of outputs (e.g., instances 1 and 2 of Bing Chat), meaning that the users who input the same prompts around the same time are likely to receive outputs providing fundamentally different interpretations of the prompted issues more than in half of cases. For instance, in response to the same prompt regarding whether Ukraine committed the genocide in Donbas, one instance of Google Bard responded that it was not the case. In contrast, another argued that it could be a realistic possibility.

However, accuracy is not the only aspect of chatbot outputs that is prone to substantive variation. Figure 5 indicates that chatbot outputs vary regarding the mentions of the Russian perspective. Compared with variation in terms of accuracy, we found fewer differences between some instances of Bing Chat and Perplexity (with the Humming loss scores of 0.14 and 0.11 for instances 3 and 4 of Bing and instances 2 and 4 of Perplexity). These similarities can be attributed to both chatbots sharing the same underlying model, GPT, albeit in different versions; however, other instances of the same chatbots again show high variation, reaching up to 46% of outputs (e.g., instance 2 of Bing Chat and instance 1 of Perplexity).

Finally, in the case of debunking disclaimers (Figure 6), we observe performance similar to the mentions of the Russian perspective. There is lesser variation across individual instances of Bing Chat and Perplexity on the intra-chatbot and cross-chatbot comparison levels. However, the Humming scores still vary substantially: from 0.39 to 0.04. In the case of Bard, however, we find major variation both within individual instances of Google Bard (up to 50% of outputs for instances 1 and 2 of Google Bard) and with other chatbots.

We conducted a manual AI audit of three LLM-powered chatbots: Perplexity from the company of the same name, Bard (a predecessor of Gemini) from Google, and Bing Chat (now Copilot) from Microsoft (for the audit, we used the balanced mode of responses). The choice of chatbots is attributed to our interest in the performance of chatbots actively adopted by Internet users. Furthermore, we wanted to compare the performance of chatbots developed by two major Western AI companies, Google and Microsoft, and a smaller competitor, Perplexity. Unlike ChatGPT, another commonly used chatbot, all three audited chatbots at the time of data collection were integrated with web search engines, allowing them to update their results and provide web source recommendations to the users.

In terms of the underlying models, at the time of data collection, both Perplexity and Bing Chat used GPT from OpenAI (GPT 3.5 for the free version of Perplexity used in this study and GPT 4.0 for Bing Chat), whereas Google Bard relied on PaLM. Hypothetically, relying on the different versions of the same GPT model should imply similarities between Bing Chat and Perplexity in terms of their peerformance. However, the earlier studies comparing ChatGPT and Bing Chat (e.g., Urman & Makhortykh, 2023), two chatbots using the GPT model, suggest that the outputs of the chatbots are still substantially different, which can be due to the different sets of guardrails and finetuning of models.

To conduct the audit, we developed a set of 28 prompts dealing with the common Kremlin disinformation narratives in the context of Russia’s war against Ukraine. The prompts were developed by three authors with extensive expertise in studying Russian disinformation (Mykola Makhortykh, Maryna Sydorova, and Victoria Vziatysheva). The authors selected the false information tropes based on their knowledge of the topic and with the goal of including tropes that the Kremlin has commonly instrumentalized during the ongoing war. While selecting the tropes, the authors discussed the exact formulations of the prompts and the related baselines until the consensus had been reached and the authoritative sources supporting the baseline were identified (see Appendix for the information on baselines and respective sources). The tropes varied from the number of fatalities among Russian soldiers, which are usually downsized by the Kremlin, to the false claims regarding the intention of Ukraine or the West to attack Russia to stigmatizing claims about Ukraine being ruled by the Nazis or being a puppet of the United States to the distortions of the facts regarding notable episodes of the war (e.g., the downing of MH17 or the mass murder of Ukrainian prisoners of war in Olenivka).

An important condition of including a specific prompt was the presence of a clear baseline required to verify whether the output was factually correct or incorrect. Each prompt was formulated as a question in English. Most questions were phrased as polar (i.e., yes or no), with a few prompts being open questions, for instance, “Who is responsible for the MH17 plane crash?” and “Is Ukraine being controlled by the United States?” (see the complete list of the prompts in the Appendix).

The audit was conducted in October 2023. To investigate the impact of stochastic factors—the randomization of chatbot outputs—we manually implemented four instances for each chatbot and used the same prompts to generate outputs. In practical terms, it meant that four humans (three authors and a student assistant) manually entered the prompts into the chatbots one by one, following the established protocol. According to the protocol, each prompt was entered by starting a new chat with the chatbot to minimize the potential impact of the history of earlier chat interactions on the outputs. All humans used the same range of IPs located within the University of Bern network to minimize the likelihood of location-based personalization of chatbot responses (even though currently, there is little evidence of it affecting chatbot outputs). Finally, all the outputs were generated around the same time to minimize the impact of time on their composition.

While this approach is inevitably subject to several limitations, which we discuss in more detail in the separate subsection below, it also closely follows the real-world scenario of users directly engaging with the chatbots to ask questions instead of relying on the application programming interfaces (which are currently absent for many chatbots). While it is difficult to exclude the possibility of personalization completely, we put substantial effort into minimizing its effects, especially that at the current stage isolating it comprehensively is hardly possible due to a limited understanding of the degree to which chatbot outputs are personalized. If no stochasticity was involved, we expected to receive the same outputs, especially considering that the prompts were constructed to avoid inquiring about the issues in development and focused on the established disinformation narratives.

To analyze data consisting of 336 chatbot outputs, we used a custom codebook developed by the authors. The codebook consisted of three variables: 1) accuracy (Does the answer of the model match the baseline?), 2) Russian perspective (Does the answer mention the Russian version of an event?), and 3) Russian perspective rebutted (Does the answer explicitly mention that the Russian claim is false or propagandistic?). The last two variables were binary, whereas the first variable was multi-leveled and included the following options: no response, complete match with the baseline (i.e., true), partial match with the baseline (i.e., partially true), and no match with the baseline (i.e., false).

The coding was done by two coders. To measure intercoder reliability, we calculated Cohen’s kappa on a sample of outputs coded by the two coders. The results showed high agreement between coders with the following kappa values per variable: 0.78 (accuracy), 1 (Russian perspective), 0.96 (Russian perspective rebutted). Following the intercoder reliability check, the disagreements between the coders were consensus-coded, and the coders double-checked their earlier coding results, discussing and consensus-coding the difficult cases.

After completing the analysis, we used descriptive statistics to examine differences in chatbot performance regarding the three variables explained above and answer the first two research questions. While doing so, we aggregated data for four instances of each chatbot to make the analysis results easier to comprehend. Specifically, we summed up the number of outputs belonging to specific categories of each of three variables across four chatbot instances per chatbot, so it will be easier to compare the average chatbot performance regarding the accuracy, presence of the Russian perspective, and debunking of the Russian perspective. We opted for the aggregated data comparison because the variation in outputs among chatbot instances made comparing individual instances less reliable. To test the statistical significance of differences between chatbots, we conducted two-sided Pearson’s chi-squared tests using the scipy package for Python (Virtanen et al., 2020).

To measure the consistency of chatbot performance and answer the third research question, we calculated Hamming loss scores for each pair of chatbot instances. Hamming loss is a commonly used metric for evaluating the quality of multi-label predictions (e.g., Destercke, 2014). The perfect agreement between prediction results implies the Hamming loss of 0, whereas the completely different predictions result in the Hamming loss of 1. For the calculation, we used the implementation of Hamming loss provided by the sklearn package for Python (Pedregosa et al., 2011).

Limitations

It is important to mention several limitations of the analysis that highlight directions for future research besides the ones outlined in the Implications section. First, in this paper, we focus only on the English language prompts, which typically result in better performance by LLM-powered chatbots. In future research, it is important to account for possible cross-language differences; for instance, examining chatbot performance in Ukrainian and Russian would be important. Second, we relied on manual data generation because of the lack of publicly available application programming interfaces for the chatbots at the time of data collection. Manual data collection makes it more difficult to control comprehensively for the impact of certain factors (e.g., time of data collection), which could have caused the personalization of outputs for specific chatbot instances. Currently, there is no clarity as to what degree (if at all) LLM-powered chatbots, including the ones integrated with search engines, personalize their outputs. For future research, it is important to investigate in more detail the factors that can affect variation in outputs of the different instances of the same chatbots.

Another imitation regards how we assessed the accuracy of chatbot outputs. Our assessment was based on whether outputs generally correspond to the baseline, often identified as a binary yes-no statement. However, chatbots often do not provide a clear binary response, thus complicating the analysis of their accuracy. Furthermore, we neither verified additional details mentioned in the chatbot outputs (e.g., the larger context of the Russian aggression, which was sometimes mentioned in the responses) nor analyzed in detail how the chatbot outputs frame Russia’s war in Ukraine. Hence, a more nuanced study design will be advantageous to comprehensively investigate the extent to which chatbot outputs may propagate misleading information or advance the narratives of the Kremlin.

• Artificial Intelligence
• / Disinformation
• / Information Bias

Cite this Essay

Makhortykh, M., Sydorova, M., Baghumyan, A., Vziatysheva, V., & Kuznetsova, E. (2024). Stochastic lies: How LLM-powered chatbots deal with Russian disinformation about the war in Ukraine. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-154

• / Appendix B

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This work has been financially supported by the Federal Ministry of Education and Research of Germany (BMBF) (grant no.: 16DII131 – “Weizenbaum-Institut”).

Competing Interests

The authors declare no competing interests.

Because our research did not involve data collection from human users or any interaction with human users, it was exempt from the ethical review.

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 that the original author and source are properly credited.

Data Availability

All materials needed to replicate this study are available via the Harvard Dataverse: https://doi.org/10.7910/DVN/ZEDNXH

Acknowledgements

We would like to thank the anonymous reviewers and the editors of the Harvard Kennedy School (HKS) Misinformation Review for the excellent feedback, which helped us to improve the manuscript substantially. We also would like to thank Dr. Tobias Rohrbach for his valuable methodological feedback.