how to write a research paper using ai

Your Writing Assistant for Research

Unlock Your Research Potential with Jenni AI

Are you an academic researcher seeking assistance in your quest to create remarkable research and scientific papers? Jenni AI is here to empower you, not by doing the work for you, but by enhancing your research process and efficiency. Explore how Jenni AI can elevate your academic writing experience and accelerate your journey toward academic excellence.

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Boost Productivity

Save time and effort with AI assistance, allowing you to focus on critical aspects of your research. Craft well-structured, scholarly papers with ease, backed by AI-driven recommendations and real-time feedback.

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Overcome Writer's Block

Get inspiration and generate ideas to break through the barriers of writer's block. Jenni AI generates research prompts tailored to your subject, sparking your creativity and guiding your research.

Unlock Your Full Writing Potential

Jenni AI is designed to boost your academic writing capabilities, not as a shortcut, but as a tool to help you overcome writer's block and enhance your research papers' quality.

 Ensure Accuracy

Properly format citations and references, ensuring your work meets academic standards. Jenni AI offers accurate and hassle-free citation assistance, including APA, MLA, and Chicago styles.

Our Commitment: Academic Honesty

Jenni AI is committed to upholding academic integrity. Our tool is designed to assist, not replace, your effort in research and writing. We strongly discourage any unethical use. We're dedicated to helping you excel in a responsible and ethical manner.

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Prompt Generation

Input your research topic, and Jenni AI generates comprehensive prompts to kickstart your paper.

Research Assistance

Find credible sources, articles, and relevant data with ease through our powerful AI-driven research assistant.

Writing Support

Draft and refine your paper with real-time suggestions for structure, content, and clarity.

Citation & References

Let Jenni AI handle your citations and references in multiple styles, saving you valuable time.

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Frequently asked questions

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Can Jenni AI help me structure and write a comprehensive literature review?

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Choosing the Right Academic Writing Companion

Get ready to make an informed decision and uncover the key reasons why Jenni AI is your ultimate tool for academic excellence.

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COMPETITORS

Enhanced Writing Style

Jenni AI excels in refining your writing style and enhancing sentence structure to meet academic standards with precision.

Competitors may offer basic grammar checking but often fall short in fine-tuning the nuances of writing style.

Academic Writing Process

Jenni AI streamlines the academic writing process, offering real-time assistance in content generation and thorough proofreading.

Competitors may not provide the same level of support, leaving users to navigate the intricacies of academic writing on their own.

Scientific Writing

Jenni AI is tailored for scientific writing, ensuring the clarity and precision needed in research articles and reports.

Competitors may offer generic writing tools that lack the specialized features required for scientific writing.

Original Content and Academic Integrity

Jenni AI's AI algorithms focus on producing original content while preventing plagiarism, ensuring academic integrity.

Competitors may not provide robust plagiarism checks, potentially compromising academic integrity.

Valuable Tool for Technical Writing

Jenni AI extends its versatility to technical writing, aiding in the creation of clear and concise technical documents.

Some competitors may not be as well-suited for technical writing projects.

User-Friendly Interface

Jenni AI offers an intuitive and user-friendly interface, making it easy for both novice and experienced writers to utilize its features effectively.

Some competitors may have steeper learning curves or complex interfaces, which can be time-consuming and frustrating for users.

Seamless Citation Management

Jenni AI simplifies the citation management process, offering suggestions and templates for various citation styles.

Competitors may not provide the same level of support for correct and consistent citations.

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

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

Make no mistake, AI is here to stay!

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

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

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

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

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

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

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

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

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

Here they are, below:

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

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

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

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

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

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

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

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

AI-powered research tools and AI for academic research

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

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

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

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

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

AI for reading peer-reviewed papers easily

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

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

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

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

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

AI for scientific writing and research papers

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

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

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

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

AI academic editing tools

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

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

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

AI tools for grant writing

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

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

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

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

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

Best free AI research tools

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

The best free ones at time of writing are:

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

Wrapping up

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

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

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

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

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

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

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

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

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

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

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

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Using AI for research: A beginner’s guide

Table of Contents

From the invention of the first wheel for moving around faster to Galileo observing the cosmos using a telescope, there has been no shortage of instances where scientists have used technology to do their work more efficiently. And isn’t that the whole point? Since human faculties can be limiting.

Using AI for research is no different, particularly in current times where the research landscape is evolving at an unprecedented rate. New scientific domains are sprouting frequently, millions of papers are being published every year , and there are vast amounts of data needing to be synthesized.

This is where artificial intelligence can assist, augment, and even revolutionize the way we discover, conduct, and write scientific research. Generative AI has proven itself to be more than a simple buzzword to the point where it can provide real useful value to a researcher at any level.

How can you use AI for research?

AI has proven itself to be handy in many professional circumstances. In academic research , you can easily fit it into several stages of your workflow. Here are some of the ways you can use it:

Finding and reviewing relevant papers

If you go about conducting a manual literature review, you’re talking about countless days of dedicated effort in search and reading. On the other hand, AI can significantly reduce the time and effort it takes to conduct a literature review.

There are AI search engines in plenty that comb through vast databases of research papers, identify relevant papers, and even summarize key findings. This can help you speed up paper analysis, find trends or gaps in the literature, and discover a research question faster.

Most of these AI research assistants help you discover new research articles based on a more accurate semantic search. Even if you don’t have the right keyword, you will still be able to find the correct papers.

Comprehending academic papers

AI can make academic papers easier to read and understand by simplifying jargon and complex topics in research papers. They can also summarise long papers into shorter reads so that you save quite some time while going through heaps of scientific articles.

Some AI assistants let you interact with papers, meaning you can essentially have a conversation with the PDF you’re reading. You can enter prompts like a simple question to get an answer or even ask to create a presentation. The AI tool will read the paper and give the output.

Academic AI tools are also alleviating language barriers by allowing users to do the above task in their native languages.

Data collection and analysis

AI can automate data collection processes by quickly gathering information by mining large databases. Even more impressive is when AI starts analyzing this huge data. For humans, it would be unfathomable to go through a mountain of data, uncovering patterns, trends, and correlations. However, for AI tools, it’s a rather easy process with less room for error.

Whether it’s data entry or data analysis, manually working with data can be an uphill task. Through AI, you will be able to arrive at more accurate insights faster, which sets a high-quality foundation for your research.

Better academic writing

Language models can assist in better writing regardless of your comprehension skill.

They can help with accurate citations, along with providing grammar and style suggestions as you are writing your paper or essay. This means you can now automate proofreading and creating citations.

AI also solves the problem of stiff scientific writing. Tools like paraphrasers and co-writers give you a great opportunity to instantly improve your writing skills and convey your thoughts in the way you actually wish.

Seamless team collaborations

AI can significantly enhance group projects where collaboration and communication are of focus. Through project management research tools, you can automate tedious tasks, manage documents better, and facilitate better communication by creating common workspaces online.

If you’re working with people from different linguistic backgrounds, AI can help you translate in real-time while you’re on calls.

Instant plagiarism checks

Maintaining academic integrity is a non-negotiable while submitting papers. AI tools can help detect plagiarism or the presence of AI in your writing. These tools scan your work, compare it to an extensive database of academic and online content, and flag potential instances of plagiarism.

Similarly, AI detectors recognize the pattern that AI writing follows and proceed to highlight any instances of such writing in your prose.

Best AI tools for research workflows

Combine the importance of AI in modern-day research life with this huge wave of generative AI and ChatGPT in the past couple of years, it should come as no surprise that there is a host of extremely helpful AI tools for research.

Here are 5 best AI tools for researchers which can be integrated to your workflow:

SciSpace is an AI platform specifically made for researchers that eases research discovery, reading, and writing. It sits on top of a repository of 270 million+ papers and offers a spectrum of AI tools, including a literature review tool to find relevant information about scientific papers and an AI research assistant called SciSpace Copilot to answer questions about any PDF document. There is also a Copilot Chrome browser extension that can help you understand academic articles on any website.

Litmaps is a handy discovery tool that assists researchers in navigating through scientific literature. It generates interactive literature maps consisting of articles related to a specific journal article or research topic. These maps enable researchers to find appropriate papers, connect the pattern between them, and exchange their knowledge about a particular field of study. The tool is both free and paid

EndNote is a reference managing tool that assists you in sorting your bibliographies and references while writing essays, reports, and journal articles. It allows you to create a personal database of references and files, as well as insert references into a Word document and automatically format them in your preferred citation style.

One of the more widely known free productivity software, Notion lets you jot down notes, arrange thoughts, and handle tasks and projects efficiently. During research, Notion can be an excellent tool when you’re collaborating with teams as your team members can comment on the documents, create dynamic content like tables, graphs, etc., and use its AI assistant to complete their tasks.

Otter.ai is a boon while you’re in meetings or recording audio while you work. The AI tool automatically transcribes everything you’re saying and generates live captions during meetings. You can also connect Otter.ai with popular meeting apps like Zoom or Google Meet.

Best practices while using AI for academic research

While AI is predominantly a boon for researchers, all things have their pros and cons. AI is still a technology in its infancy, as experts call it, and it should be treated as such. So before you become completely dependent on it, here are a few things you should keep in mind while using an AI-powered research tool:

Ensure data quality and bias

Before you start to analyze data using AI, take a moment to consider the quality of your input. Because if you feed low-quality data to a machine, you can't expect a high-quality output. That just isn’t how it works.

AI cannot think for itself in the same way humans do. At best, it can learn from everything it has been fed and predict an output. So, make sure your data is premium, representative, and, most importantly, unbiased. Biased data can lead to skewed results and questionable conclusions.

Adhere to academic ethics

To reiterate again, academic researchers are bound by academic integrity. Plagiarism, AI-assisted writing, and privacy regulations are all of the highest priority while publishing a paper.

Thus, it’s always better to ensure your research complies with ethical standards and you use necessary plagiarism and AI detection tools before submitting your paper. How you write a paper is a reflection of who you are as a professional.

Check for hallucinations

While AI may seem perfect at all times, it has its moments that make you question the validity of the entire technology.

Sometimes, AI systems can generate results that seem plausible but are entirely incorrect or generate complete gibberish. There are also times when an AI might give you the correct answer but fake its sources. This is popularly known as a hallucination.

It’s quite obvious how it can be detrimental to your academic research, thus it’s always better to fact-check AI-generated output. Some AI assistants have real citations in their answers now, which helps build trust.

Maintain human oversight

While AI can automate and streamline many tasks, it can't replace human judgment, context, and expertise. And given how AI can, at times, hallucinate or give false output, it’s always better to have your human judgment review everything.

To leave you with

The academic world has undergone a profound change in the last few years thanks to AI. For some, it’s an invaluable resource from streamlining literature reviews to supercharging data analysis and academic writing. For others, it’s a grey area and presents some real concerns relating to academic integrity and watering down of content.

But the fact remains that AI, in most cases does help researchers around the world become more efficient, thus producing good-quality work in less time. As language models develop more and more, the use of AI for research will become even more prominent.

Frequently Asked Questions

AI tools can be used in research for finding relevant papers, reviewing and comprehending complex papers, data collection and analysis, better academic writing, and plagiarism detection.

Some of the best AI tools for researchers include SciSpace, Litmaps, Endnote, Notion, and Otter.ai.

Yes, you can use AI tools to write a research paper. From streamlining literature reviews to supercharging data analysis and academic writing, AI tools can make research more efficient. However, human intervention is vital to provide factual information to the readers.

AI can be used to identify relevant papers, summarize key findings, automate data collection, improve academic writing, and also detect plagiarism.

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A Full Guide To Using AI For Research: Use Cases, Tips & AI Tools

Interested in using AI for research? These various use cases, tips, and AI tools will help you leverage the power of AI in your research projects.

Apr 7, 2024

Research methods have evolved, thanks to technological advancements. Delving into AI for research can significantly streamline the process. By leveraging AI capabilities, researchers can collect, process, and analyze vast amounts of data in record time. Unlike traditional research methods, AI-powered systems can provide accurate results. Besides, AI allows researchers to tap into a wealth of primary vs secondary sources , enabling them to make well-informed decisions based on credible information. In this blog, we will delve deep into how using AI for research can transform the way research is conducted today.

Table of Contents

Understanding the role ai plays in research, 6 ways you can use ai for research, benefits of using ai for research, 10 best ai tools for academic research, effective tips for researchers using ai for their research, supercharge your researching ability with otio — try otio for free today.

AI has revolutionized the research landscape by enabling more efficient and accurate processes. Utilizing AI tools in research can help save time, find relevant papers, plan and execute experiments, analyze data, and even write and edit manuscripts. AI-powered tools offer researchers a way to manage their entire research workflow by integrating various tasks, ultimately aiding in producing higher-quality research output.

Types of AI Technologies Commonly Used in Research Settings

There are several types of AI technologies that are commonly used in research settings. Natural Language Processing (NLP) plays a significant role in aiding researchers in processing vast amounts of textual data, summarizing papers, and extracting key information. 

Machine learning algorithms are used to predict trends, analyze data, and optimize experiments. Robotics can be utilized to conduct experiments, gather data, and perform other physical tasks. Neural networks can be employed for tasks such as image recognition and data analysis.

AI research and writing partner

Knowledge workers, researchers, and students today suffer from content overload and are left to deal with it using fragmented, complex, and manual tooling. Too many of them 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 the click of a button - this problem is only going to get worse. 

Otio solves this problem by providing one AI-native workspace for researchers. It helps them:

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.

draft outputs using the sources you’ve collected

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

Related Reading

• How To Read Scientific Papers • How Many Sources Should A Research Paper Have • Sources For Research Paper • Google Scholar Search Tips • How To Read A Research Paper • How To Find Sources For A Research Paper • Research Notes • Literature Synthesis

1. Finding and reviewing relevant papers

AI has significantly reduced the time and effort needed for manual literature reviews, saving countless hours. AI tools can comb through vast databases of research papers, identify relevant papers, and summarize key findings. This speeds up paper analysis, identifies trends, gaps in the literature, and helps discover research questions faster.

2. Comprehending academic papers

AI makes academic papers easier to understand by simplifying jargon and complex topics. Interacting with papers through AI assistants allows users to have a conversation with the PDF. This feature can help in language barriers by enabling users to interact with papers in their native languages.

3. Data collection and analysis

AI helps uncover patterns, trends, and correlations in large datasets accurately and quickly. From data entry to analysis, AI tools significantly speed up the process, ensuring high-quality research foundations.

4. Data visualization

AI-assisted optimization tools make data visualization easy. AI tools can help present data in informative ways, such as through images or graphs, making it easier for researchers to identify patterns and gain insights.

5. Enhanced academic writing

AI language models assist in writing by providing grammar and style suggestions, citations, and help in conveying thoughts effectively. Tools like paraphrasers and co-writers aid in improving writing skills instantly, making the writing process more efficient.

6. Seamless team collaborations

AI enhances group projects through automation of tasks, better document management, and improved communication. Project management tools with AI capabilities facilitate better collaboration by creating common online workspaces and translation in real-time.

7. Plagiarism checks

AI tools help detect plagiarism and artificial intelligence in writing. By scanning documents and comparing content to extensive databases, AI tools flag potential instances of plagiarism . AI detectors can recognize patterns of AI writing to highlight such instances in prose.

Otio's AI-Powered Workspace

Knowledge workers, researchers, and students often struggle with content overload and fragmented tools. Otio provides an AI-native workspace to streamline research workflows. Otio helps collect data from various sources, extract key takeaways, and create draft outputs faster. Researchers can benefit from AI-generated notes, source-grounded Q&A chats, and AI-assisted writing. 

Accelerating data analysis and processing

AI tools are capable of processing vast amounts of data at a speed that far surpasses human capabilities. This acceleration allows researchers to swiftly identify patterns, trends, and anomalies within data sets, enabling them to draw conclusions and insights more quickly than ever before. 

With this enhanced data processing speed, researchers can conduct in-depth analysis and produce results within a fraction of the time it would take using traditional methods. This not only saves time but also allows researchers to explore data in ways that were previously unattainable.

Enhancing accuracy and efficiency of research tasks

AI boosts the accuracy and efficiency of research tasks by eliminating human error and biases. AI algorithms can run analyses with high precision, reducing the risk of inaccuracies or inconsistencies in the data. 

Researchers can rely on AI to process large volumes of data consistently and meticulously, ensuring the reliability of their findings. This accuracy and efficiency also extend to tasks such as literature reviews, where AI tools can quickly scan and summarize vast amounts of text, saving researchers valuable time and effort.

Enabling predictive modeling and data-driven insights

AI technology is adept at creating predictive models from vast datasets, offering researchers the ability to forecast trends, behaviors, and outcomes with much higher accuracy than traditional methods. 

By leveraging machine learning algorithms, researchers can uncover hidden insights and patterns within their data that were previously undetectable. The ability to predict future outcomes based on historical data allows researchers to make more informed decisions and develop strategies based on data-driven insights.

Facilitating automation of repetitive tasks

AI simplifies the research process by automating repetitive tasks that would otherwise be time-consuming and monotonous. For instance, AI tools can automate the collection and cleaning of data, freeing up researchers to focus on analysis and interpretation. 

Automation also extends to tasks like citation management and reference formatting, streamlining the writing process and reducing the risk of errors. By automating these repetitive tasks, researchers can dedicate more time to exploring their research questions and generating innovative ideas.

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

draft outputs using the sources you’ve collected. Otio helps you to go from reading list to first draft faster. Along with this, Otio also helps you write research papers/essays faster. Here are our top features that are loved by researchers: 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

• How To Summarize A Research Article • Reliable Sources For Research • How To Tell If An Article Is Peer Reviewed • Literature Search • Best Databases For Research • Summarize Research Paper Ai • How To Use Chat Gpt For Research • How To Search For Research Articles

1. Otio - Your AI Research and Writing Partner

Otio is an AI-native workspace that simplifies the research process for knowledge workers, researchers, and students by providing a single platform for collecting data from various sources, extracting key takeaways, and creating draft outputs. The AI-generated notes on all bookmarks, the ability to chat with individual links or entire knowledge bases, and AI-assisted writing are among the top features praised by researchers. 

Otio helps you move from a reading list to a first draft quickly and even assists in writing research papers or essays.

2. Semantic Scholar - Detailed Information at Your Fingertips

Semantic Scholar is an AI-powered tool that helps researchers by offering detailed information in context, highlighting key points, and providing super-short summaries of scientific papers. This tool enhances the research process by providing a more efficient way to extract valuable information from academic articles.

3. SciSpace - Streamlining Your Research Process

SciSpace is a valuable tool that aids researchers in finding relevant research papers and gaining insights into them. This tool can make it easier to locate specific papers within the scientific research field and obtain useful information for academic purposes.

4. Genei.io - Efficient Paper Summarization and Exploration

Genei.io is an AI-powered summarization and research tool that simplifies the process of finding and exploring academic papers. The efficient features of this tool can help researchers save time and effort when conducting literature reviews or other research-related tasks.

5. Connected Papers - Uncover Hidden Research Gems

Connected Papers is a powerful tool designed to help researchers find and explore academic papers. By offering insights into research articles and facilitating the discovery of relevant sources, this tool can help researchers uncover valuable information in their field of study.

6. Scite - Understanding Research Impact and Credibility

Scite is a valuable tool that enables users to see how research has been cited, providing valuable information on the impact and credibility of scientific articles. By offering insights into the citation history of research articles, Scite can help researchers evaluate the reliability and relevance of academic sources.

7. Zotero - Efficient Resource Retrieval for Research

Zotero is a reference management tool that aids researchers in retrieving resources using DOIs effectively. By making it easier to find and manage academic sources for research purposes, Zotero can help streamline the research process and enhance productivity.

8. IBM Watson Discovery - Extracting Insights From Unstructured Data

IBM Watson Discovery is an AI-powered platform that enables researchers to extract insights from large volumes of unstructured data, including scholarly articles, patents, and reports. By using natural language processing (NLP) and machine learning algorithms , this tool can help researchers analyze and interpret textual data more efficiently.

9. Google Scholar - Accessing a Wealth of Scholarly Literature

Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across a variety of disciplines. By helping researchers find academic articles, books, conference papers, and theses, Google Scholar can facilitate the discovery of valuable information and enhance the research process.

10. Cortex - Discovering and Analyzing Scientific Literature

Cortex is an AI-driven platform that helps researchers discover, analyze, and visualize scientific literature. By using natural language processing (NLP) and machine learning algorithms, this tool can extract insights from academic papers and identify trends in research fields. Cortex can help researchers stay up-to-date with the latest developments in their field and make informed decisions based on the analysis of scientific literature.

Researchers can significantly benefit from using AI-powered tools to enhance their research process and streamline their time. It is essential for researchers not to rely solely on AI to replace the critical thinking required for the research. Instead, AI should be used as a tool to optimize the research process while maintaining the researcher's intellectual input. Here are some tips for researchers on how they can efficiently use AI in their research projects to boost productivity and optimize research outcomes. 

Fact-Check AI-Generated Content: Verifying Information for Accurate Results 

While using AI for research tasks, it's crucial to fact-check content generated by AI tools. Users should not take the AI-generated information as 'the truth' but rather cross-verify the results through reliable sources. AI tools can assist in data analysis and generation, but it is the researcher's responsibility to ensure the accuracy and reliability of the information. 

Avoid Depending on AI for Writing Academic Documents: AI as a Helper, Not Replacement

When using AI tools for research, it's advised not to rely entirely on these tools to write academic articles, theses, or grant applications. Researchers should view AI as assistive technology that can help organize data, conduct analysis, and provide insights, rather than generating full pieces of academic writing. The critical thinking and analysis that researchers bring to their work are irreplaceable by AI. 

Utilize AI for Managing and Citing References: Streamlining the Citation Process with AI Tools

For referencing and citation needs, researchers should not rely on AI tools for generating references. Instead, AI tools can be used to manage and cite references effectively. AI-powered citation tools can help researchers organize their citations, format them according to specific guidelines, and ensure that reference lists are accurate and complete. By following these tips, researchers can effectively integrate AI into their research process, improving efficiency, and optimizing outcomes. AI tools should complement researchers' abilities, providing support and assistance without replacing the human intellect and critical thinking essential for high-quality research.

Otio is a dynamic tool designed to help knowledge workers, researchers, and students manage their content overload more efficiently. With a rise in easily accessible content creation, it’s becoming increasingly challenging for people to streamline their research process. 

Otio hopes to simplify this process by offering an AI-native workspace for researchers. 

1. Collect Diverse Data Sources

Otio enables users to gather data from a wide range of sources, including bookmarks, tweets, extensive books, and YouTube videos. By centralizing this information, Otio streamlines the initial research phase for users.

2. Extract Key Insights

The platform can generate detailed AI-powered notes and Q&A chat based on the collected data. This feature helps users quickly grasp the key takeaways from their research materials.

3. Fast-Track Drafting

Otio empowers users to create draft outputs using their collected sources. By helping researchers transition from their reading lists to initial drafts more efficiently, Otio optimizes the writing process.

Key Features Loved by Researchers

Ai-generated notes.

Otio offers AI-generated notes for all bookmarks, including YouTube videos, articles, and PDFs. This feature enhances the note-taking process and ensures the user has detailed insights from their research materials.

Interactive Chat Interface

Users can engage with individual links or entire knowledge bases via Otio's chat function. This feature allows for seamless communication, similar to engaging with ChatGPT, enhancing the collaborative aspect of research.

AI-Assisted Writing

Otio supports AI-assisted writing, which can be a game-changer for researchers aiming to speed up their writing process. By leveraging AI capabilities, researchers can optimize their writing efficiency.

Otio is an all-encompassing solution for knowledge workers, researchers, and students seeking to streamline their research and writing processes. The AI features embedded within Otio make it a valuable tool for automating tasks, generating insights, and accelerating the writing process. If you're looking to enhance your research workflow, Otio could be your ideal AI research and writing partner . 

Try Otio for free today and experience the future of research tools.

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How AI can help you write your research paper

Putting together a research paper can be a daunting task, often involving hours of research, managing information, and crafting a well-structured document. However, the advent of AI-powered tools makes the process more manageable than ever before. Even better than some research tips, explore how AI-powered tools can change the way you build research papers, making the entire process smoother, more precise, and less time-consuming.

Use AI for research

One of the most laborious aspects of writing a research paper is gathering information and conducting thorough research; AI-powered tools can help streamline this process significantly.

Use Copilot in Edge to find relevant research papers, articles, and academic sources with ease. By simply inputting your paper parameters, research topic, or keywords, you can quickly access a vast database of academic resources, saving you hours of scouring the internet. Copilot can also swiftly provide definitions, explanations, and relevant context for unfamiliar terms or passages on the page, eliminating the need to switch between tabs or search engines. Moreover, Copilot can summarize extensive web content and suggest related articles or papers to deepen your understanding. By integrating Copilot into your research workflow, you can enhance productivity and ensure access to up-to-date and pertinent information. 

Organize your paper with AI

Once you've collected the necessary information, you need to organize and structure your research paper effectively. Copilot, accessible right in the sidebar, can help build a clear, well-structured outline for your paper based specifically on your topic and web sources. By inputting key points and subtopics, Copilot can generate a structured outline to serve as a roadmap for your argument. This ensures your research paper is logically organized, making it easier for readers to comprehend.

Come across a concept or fact on the web that you want to use? Something spark an idea you want to get down and revisit? Use the Compose  in the Microsoft Edge sidebar to write up a summary of the idea, or where it fits into your larger argument. Ask for a citation while you’re at it—more on that a little later.

As a bonus, Microsoft Edge’s AI-powered tools check for grammar and spelling errors and provide suggestions to improve the clarity of your writing. If you like writing chunks of your paper as you research, this is ideal. They can help refine your writing style and ensure your ideas are effectively communicated.

Finding sources using AI

Looking for primary or academic sources? Ask Copilot in Edge. With a topic, keywords, and questions, the AI-powered tool can help hunt down possible sources fast. Copilot can provide links and even summaries of the sources. To be sure the sources are credible, always review them for yourself, of course, and make the best decision about what is relevant and persuasive for your paper.

Citing sources using AI

Citing sources and managing references is a crucial aspect of research paper writing—and one of the most dreaded. Fortunately, AI-powered citation and reference management tools can simplify the tedious citation process. As you track sources and collect information across the web, the AI-powered Microsoft Edge sidebar can help generate citations for your sources. Simply ask for Copilot to cite a source using a specific style—typically APA, MLA, or Chicago—and it will find the best citation generator for your paper. The sidebar tools can also serve as a sort of extra set of eyes, checking your footnotes and bibliography for errors or inconsistencies.

Ethical use of AI-powered tools in school

The ethics of using AI for research papers raise important questions about transparency, integrity, and the role of human creativity and critical thinking in academia. While AI-powered tools undeniably offer efficiency and assistance in the research process, it's crucial for researchers to maintain transparency. Proper citation and acknowledgment of AI-generated content or assistance is essential to uphold academic integrity and avoid plagiarism.

Ultimately, the responsible use of AI in research should complement human ingenuity rather than replace it, emphasizing collaboration between technology and researchers to advance knowledge ethically and morally.

Embark on you next research paper with AI’s help

AI-powered tools  like those built into Microsoft Edge have the ability to revolutionize the process of developing research papers. From automating research to helping with organization, citation management, even language translation, these tools offer a variety of benefits to researchers.

When you embark on your next research paper, consider integrating AI-powered tools into your workflow to save time, enhance accuracy, and improve the overall quality of your work. Copilot in Edge is an easy and convenient AI portal for researchers and writers. Try Microsoft Edge  today to tap into the power of AI and improve your research papers today.

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10 Great AI Tools You Can Use to Speed Up and Assist Your Research and Paper Writing

Reviewed by David Krug David Krug is a seasoned expert with 20 years in educational technology (EdTech). His career spans the pivotal years of technology integration in education, where he has played a key role in advancing student-centric learning solutions. David's expertise lies in marrying technological innovation with pedagogical effectiveness, making him a valuable asset in transforming educational experiences. As an advisor for enrollment startups, David provides strategic guidance, helping these companies navigate the complexities of the education sector. His insights are crucial in developing impactful and sustainable enrollment strategies.

Updated: January 12, 2024 , Reading time: 16 minutes

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College students write, on average, between 10 and 15 essays every semester, with English and writing courses requiring the most intensive writing assignments. Most college majors also write either an intensive senior thesis or a capstone project as a culminating requirement, and the college majors with the most writing assignments include English and English literature, communications, and journalism.  

The bottom line is that college students must possess effective and efficient writing skills! But with co-curricular and extracurricular activities also vying for attention, writing with alacrity and quality can be a challenge.

Additional Resources:

• Cutting Edge Graduate Schools Leading Us Into The Future
• How to Choose Your Graduate Thesis Topic
• What is a Thesis Defense?
• How to Choose Your PhD Dissertation Topic

Fortunately, artificial intelligence (AI) tools are available that can facilitate both the pre-writing research process and the actual writing process itself! These AI tools facilitate data gathering, organization, and analysis, provide outlines for research papers and other academic writing assignments, and minimize errors in grammar, spelling and sentence construction, among other features.

Indeed, these AI tools have revolutionized the way college students – and graduate and postgraduate students, researchers and scientists, for that matter – approach their writing-related work. 

However, caution and adherence to ethical standards must be exercised when using AI tools for academic writing! While there are benefits, AI tools have their significant limitations, too. For one thing, AI tools cannot replace your writing and research skills because these are tools – nothing more but tools in facilitating your research and writing work.

If you have a heavy reliance on AI tools, you can become unmotivated in writing original work, become less skilled in writing, or lose your unique voice (i.e., AI tools have a repetitive quality) 

For another thing, AI tools can be unreliable in their facts about people, places and events, their citations provided, and even in their recommendations for words, grammar and context. You must always verify the information, including the citations and attributions generated by AI tools (i.e., cross-checking facts from reliable sources).

Google Scholar

Launched by Google in 2004, Google Scholar is a staple among students, researchers and academics who want reliable scholarly sources from a wide range of materials. The free web search engine indexes books, academic theses, articles, conference papers, court papers, and patents, among other materials, from diverse disciplines. Students can also access their universities’ library databases for resources and materials and, thus, facilitate the research process. 

Google users assert that Google Scholar’s search functions are similar to its parent’s user-friendly design. Just type the keywords or keyphrases into a homepage field, refine the search by author name, publication name and date, and click on the relevant links. The metrics section contains the diverse category rankings, from humanities, literature and arts to health and medical sciences, and provides more specific search results in different languages (e.g., English, Chinese, and French). 

Students agree that access to multidisciplinary materials makes Google Scholar a useful AI tool for writing assignments in both their general education and concentration courses. Keep in mind, however, that access to the full text of the resources and materials isn’t guaranteed, but Google Scholar can provide active links to the complete document. Note that the full-text versions may or may not be free, but viable alternatives are provided. 

As with many AI tools, Google Scholar’s search results can include low-quality publications, meaning information verification must still be made. There’s also the risk of underrepresentation of foreign language publications – but it won’t be an issue if your works focus on English-language publications – and incomplete citations. Google Scholar also doesn’t have extensive advanced search options, meaning it’s best to use it along with other AI tools.

Scite uses natural language processing and artificial intelligence in Smart Citations generation, meaning it can determine the way citations are used (e.g., neutral, supporting, or contrasting). In this manner, students can evaluate the credibility and reliability of scholarly articles, research papers, and other academic materials through analysis of their citations received. The Smart Citations database consists of extensive collections of files from the likes of University Press, Cambridge and Wiley, as well as scientific preprint sources like bioRxiv, asXiv, and medRxiv. 

Students can use Scite to understand the broader context of scholarly articles, research papers, and other academic materials for which citations have been made. This is possible since Scite can determine whether specific citations provide evidence for (i.e., supporting) or against (i.e., contrasting) a specific claim or if these only mention it (i.e., neutral). 

When you use Scite properly, you can spend less time on the literature review and critical analysis aspects of the research process. These include critically engaging with the literature, reading the cited references, understanding their context, and finding relevant and reliable literature related to your topic. 

You will also be able to quickly identify and clearly explain the gaps in current literature that your research paper will address. You can also discuss the debates about the contrasting findings and the current state of the field. Your research paper may also discuss the ways that research on your particular topic can be improved.

Scite’s useful features include a reference check, advanced search and custom board. There’s even a browser extension and a Zotero plugin. Check out Scite’s Assistant language model, too, a chatbot with useful features for students and researchers alike.

The beauty of using AI tools is in the effective and efficient automation of many of the aspects of the research process! This is true of Scholarcy, a popular AI tool among students, researchers and academics, because it automates the reading, summarizing and extracting aspects of information gathering. The AI-powered tool saves time, energy and effort on the part of its users, particularly when providing summaries of large files or documents quickly. 

The AI tool also makes quick assessments and evaluations of the importance of documents, whether these are entire reports, articles, research papers or specific chapters in a book. Summary flashcards containing organized, structured and concise summaries transform extensive data into an easily digestible format.

The AI tool also recognizes tables and figures from the materials being analyzed. You will then have a better understanding of the main concepts, ideas or information in the materials or a better appreciation of the context of the information. 

The citation extraction features allow for reliable identification and organization of the key references and sources used in the materials being analyzed. You can save time in creating the citations and bibliographies for your research paper and other academic papers using Scholarcy. There’s also an automation feature for the literature review process, meaning more time can be spent on writing your academic paper. 

Scholarly is also compatible with many of the commonly used document formats used in academic research. Again, the ease of organizing, analyzing and using your summaries is part of its popular appeal. 

But Scholarcy also has its issues, such as concerns about its accuracy, its limited scope, and its security and privacy, as well as its subscription cost.

Students and scholars alike use Trinka as their primary grammar checker and language correction AI tool when writing their academic and technical papers. With its extensive tools – more than 3,000 – for grammar, tone and style, you can write better outputs with little to no errors while still maintaining your voice (i.e., your unique style of writing). Your academic and technical papers will also be less likely to contain errors, although editing them as many times as necessary is still a must – it’s only an AI tool, after all. 

The comprehensive grammar checks don’t only check for grammatical errors but also spelling, consistency and style errors, a must when writing papers for presentation or publication. Trinka also provides advanced writing tips, including enhancements in tone and style that elevate your papers’ overall quality while still being true to your goal. You will be provided with actionable feedback that makes sense when you think more about it, not to mention that it’s an insight you can use in your future works. 

What we love the most about Trinka is that it’s specifically designed as an AI tool for formal writing purposes, such as your senior’s thesis or capstone project in college, even your master’s thesis or doctoral dissertation in your graduate studies. Even the simplest mistakes in these academic papers can negatively impact your grades or your professional reputation. 

Trinka also has a sophisticated plagiarism checker feature (i.e., iThenticate text similarity detection algorithm), an auto-editing feature for quick revisions of MS Word documents, and suggestions for publication formatting conventions (e.g., APA and AMA). Students also love the publication readiness checker, citation checker, and personal dictionary.

For students and scholars with tight schedules, the opportunity to save even 1.5 hours every week on research-related work is welcome! This is what the developers of Elicit, an AI research assistant that uses language models in the automation of workflows involved in the research process, promise – and deliver in many ways. The company that offers the AI tool, also known as Elicit, is a for-profit research foundation. 

Note that the current Elicit version primarily automates literature review. You will ask a question, and Elicit will find relevant information (e.g., scholarly journals, research papers, and conference proceedings), organize the information into summaries, and present them in tables. You may customize the tables according to your desired information metrics, such as the outcomes, interventions and study type, resulting in easier tracking and monitoring of your sources. 

The relevant information comes from academic literature from across a wide range of disciplines, meaning students can ask questions about the datasets used in studying logical reasoning or the effects of creatine on the body. Note, however, that the papers presented in the search results may or may not match the keywords provided. 

Aside from finding academic papers on diverse topics, Elicit can also gather, analyze and organize multiple materials, including your own documents (e.g., uploaded PDFs). You can also brainstorm for topics, identify and define key search terms, and determine your research direction. Elicit is also notable for providing just-in-time updates, a useful feature considering the ever-evolving body of knowledge in any field. 

But Elicit has its share of limitations, too, such as limited access to full-text documents (i.e., paywall) and the risk of misrepresenting or misinterpreting information, as is the case with many AI tools. Again, always verify the information, exercise caution, and use your professional judgment.

Research Rabbit

Launched in 2021, Research Rabbit is a free, web-based platform that facilitates the literature review process in academic writing (e.g., senior’s thesis, master’s thesis or doctoral dissertation). The literature review can be problematic because of the sheer number of data that must be identified, sorted and organized into reliable and relevant information. Research Rabbit lessens the time, energy and effort spent on these tasks by providing useful features in searching for information in articles, books and other published materials, organizing them into a systematic format, and saving them for future reference. 

Since it’s a free AI tool, you can use it for your academic writing regardless of your academic discipline, affiliation and direction. Research Rabbit isn’t a library resource, either. 

The AI tool is fairly straightforward to use, too. You sign up for the AI tool and start exploring its user-friendly user interface. You must create and name your collection (i.e., click on the New Collection or the +Collection button), add your seed papers (i.e., click on the Add Papers button that will open a search bar), and type in the relevant keywords related to your academic work. 

Click the Search button, scan the list of sources provided in the search results, and add the relevant sources to your collection. You can write comments about these sources, too. Research Rabbit provides summaries of the sources and a diagram demonstrating the connections between these sources and their authors, as well as access to the sources’ PDFs. You may also change the format of the graph for your convenience. 

Zotero is an AI-powered, open-source research management tool that acts as your personal research assistant – and for free, too! This is among the few AI tools specifically designed for researchers and scholars with useful features for the collection, organization, citation and annotation of source documents, as well as sharing of research documents. You can customize your collections of source and research documents, too, aside from benefiting from the automatic extractions of the sources’ metadata. 

Setting up your personal account is easy. Just download the desktop application on your personal computer – it’s compatible with Windows, Mac and Linux – and install its mobile-friendly Zotero Bookmarklet. You can then sync your Zotero library on all your computers, followed by installing the word processor plugin and the web browser connector. This will save your citations from your source websites and databases. 

Your Zotero account provides a fairly impressive 300MB of storage and an unlimited number of groups that you can create and join. With Zotero installed, you can directly insert your sources into your academic paper in various ways, thanks to the word processor plugin, such as footnotes, in-text citations and bibliographies. 

You can use Zotero with MS Word, Google Docs, and LibreOffice Writer, in addition to its expansive 10,000+ citation styles that allow for customized formatting and styling. We particularly appreciate the “cite as you write” convenience that Zotero brings to the table.  

Note that Zotero works well with PDF and non-PDF content, as well as with an extensive array of catalogs, including MOBIUS, WU Classic, and WorldCar. 

Zotero’s lack of institutional customer support service is an issue, but users can get extensive information on the Zotero website about troubleshooting common issues, access to the Zotero Forums, and instructional screencasts. If your college has Zotero in its system, you can ask for tutorials, if any.

The Consensus app is an AI search engine powered by GPT-4 that’s useful in the extraction and distillation of data from published scientific research. The result: Users receive answers based on scientific evidence to a wide range of queries. Indeed, it’s among the few AI tools that source information exclusively from published sources, including peer-reviewed research! 

As an AI search engine, Consensus uses natural language processing, machine learning, and blockchain technology in the identification, analysis and evaluation of the authenticity of content on the web. Then, AI is used in reading the content and extracting the relevant answers related to the questions being posed. While there are no right or wrong questions, asking the “right questions” will result in answers backed by a sufficient quantity of quality sources. 

Keep in mind that your questions must be scientific, such as questions in biology, physiology and sociology. Your questions can either be open-ended or yes/no questions, and you can also ask for the answers to be expanded. With scientifically verified results based on previously published scholarly works – the website claims 400 million-plus scholarly works are its sources – you can save time and energy on research. 

There’s also a consensus meter that highlights the consensus and divergence among authors of scholarly works, a useful tool in yes/no questions. The ChatGPT 4 summary runs ten results, at least, but will only display the relevant answers, meaning you will benefit from the more efficient search.

Tableau is a popular Salesforce-powered data visualization tool among researchers, academics and students at the college and graduate levels. This is also considered an excellent reference management software with a drag-and-drop interface that results in effective and efficient identification, exploration and organization, as well as analysis of data, outliers and patterns contained in research sources. Users also appreciate the user-friendly and interactive dashboard that strengthens their positive experiences with the AI tool. 

Tableau has many features, too, which cater to different needs and wants:

• Tableau Cloud is a cloud-based platform that provides analytics features for effective and efficient integrated data management.
• Tableau Prep is useful for the fast, easy and efficient editing, organizing and merging of research data for in-depth analysis. 
• Tableau Data Management has features useful in carrying out the data and analytics lifecycle from start to end, including scaling data automation and self-service analytics. 
• Tableau Mobile allows users to explore the content contained in the Tableau Cloud site or the Tableau Server from their mobile gadgets, both online and offline. The mobile-optimized dashboard layouts allow users to view, filter and distill their desired information with the touch of a button. 

With its intelligent data preparation feature, you can also access data and prepare it for analysis regardless of its location and condition. 

ChatPDF is an AI tool with immense potential for students, researchers and academics in their formal writing, from essays to research papers. Users upload their PDF documents on the AI interface and then interact with it, thanks to its generative AI model. With its natural language processing feature, it can analyze a PDF document and provide immediate answers to questions related to its contents. The result is less time spent on reading an entire PDF document, a particularly useful feature with lengthy or voluminous PDF documents. 

The answers provided aren’t just quick in coming – these are concise yet true to the contents in the PDF document, aside from being context-specific responses. The information is summarized in an easily digestible format that makes it easier to incorporate in an academic paper. PDF documents can be virtually anything, too, such as scholarly articles and research papers, conference presentations, financial reports, and legal contracts. 

Users can also challenge the summaries provided by ChatPDF, an interactive engagement that can deepen their understanding of the topic and contribute to their ability to form their own opinions. You can also highlight specific areas that require further research. The summaries and annotations possible with ChatPDF are of particular importance in making reliable literature reviews, too. 

But beware that ChatPDF is similar to ChatGPT in that it has a high risk of plagiarism and repetition. You must still exercise your critical thinking skills and verify information when in doubt. 

AI tools can support the writing-related work of college students, researchers and scientists, but these aren’t likely to completely replace the originality, creativity and quality of work that humans can generate. You must treat them for what they are – writing aids only!

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Research with Generative AI

Resources for scholars and researchers

Generative AI (GenAI) technologies offer new opportunities to advance research and scholarship. This resource page aims to provide Harvard researchers and scholars with basic guidance, information on available resources, and contacts. The content will be regularly updated as these technologies continue to evolve. Your feedback is welcome.

Leading the way

Harvard’s researchers are making strides not only on generative AI, but the larger world of artificial intelligence and its applications. Learn more about key efforts.

The Kempner Institute

The Kempner Institute is dedicated to revealing the foundations of intelligence in both natural and artificial contexts, and to leveraging these findings to develop groundbreaking technologies.

Harvard Data Science Initiative

The Harvard Data Science Initiative is dedicated to understanding the many dimensions of data science and propelling it forward.

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Generative AI is only part of the fascinating world of artificial intelligence. Explore Harvard’s groundbreaking and cross-disciplinary academic work in AI.

funding opportunity

GenAI Research Program/ Summer Funding for Harvard College Students 2024

The Office of the Vice Provost for Research, in partnership with the Office of Undergraduate Research and Fellowships, is pleased to offer an opportunity for collaborative research projects related to Generative AI between Harvard faculty and undergraduate students over the summer of 2024.

Learn more and apply

Frequently asked questions

Can i use generative ai to write and/or develop research papers.

Academic publishers have a range of policies on the use of AI in research papers. In some cases, publishers may prohibit the use of AI for certain aspects of paper development. You should review the specific policies of the target publisher to determine what is permitted.

Here is a sampling of policies available online:

• JAMA and the JAMA Network
• Springer Nature

How should AI-generated content be cited in research papers?

Guidance will likely develop as AI systems evolve, but some leading style guides have offered recommendations:

• The Chicago Manual of Style
• MLA Style Guide

Should I disclose the use of generative AI in a research paper?

Yes. Most academic publishers require researchers using AI tools to document this use in the methods or acknowledgements sections of their papers. You should review the specific guidelines of the target publisher to determine what is required.

Can I use AI in writing grant applications?

You should review the specific policies of potential funders to determine if the use of AI is permitted. For its part, the National Institutes of Health (NIH) advises caution : “If you use an AI tool to help write your application, you also do so at your own risk,” as these tools may inadvertently introduce issues associated with research misconduct, such as plagiarism or fabrication.

Can I use AI in the peer review process?

Many funders have not yet published policies on the use of AI in the peer review process. However, the National Institutes of Health (NIH) has prohibited such use “for analyzing and formulating peer review critiques for grant applications and R&D contract proposals.” You should carefully review the specific policies of funders to determine their stance on the use of AI

Are there AI safety concerns or potential risks I should be aware of?

Yes. Some of the primary safety issues and risks include the following:

• Bias and discrimination: The potential for AI systems to exhibit unfair or discriminatory behavior.
• Misinformation, impersonation, and manipulation: The risk of AI systems disseminating false or misleading information, or being used to deceive or manipulate individuals.
• Research and IP compliance: The necessity for AI systems to adhere to legal and ethical guidelines when utilizing proprietary information or conducting research.
• Security vulnerabilities: The susceptibility of AI systems to hacking or unauthorized access.
• Unpredictability: The difficulty in predicting the behavior or outcomes of AI systems.
• Overreliance: The risk of relying excessively on AI systems without considering their limitations or potential errors.

See Initial guidelines for the use of Generative AI tools at Harvard for more information.

• Initial guidelines for the use of Generative AI tools at Harvard

Generative AI tools

• Explore Tools Available to the Harvard Community
• System Prompt Library
• Request API Access
• Request a Vendor Risk Assessment
• Questions? Contact HUIT

Copyright and intellectual property

• Copyright and Fair Use: A Guide for the Harvard Community
• Copyright Advisory Program
• Intellectual Property Policy
• Protecting Intellectual Property

Data security and privacy

• Harvard Information Security and Data Privacy
• Data Security Levels – Research Data Examples
• Privacy Policies and Guidelines

Research support

• University Research Computing and Data (RCD) Services
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Organizing Your Social Sciences Research Paper

Generative ai and writing.

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
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• Scholarly vs. Popular Publications
• Qualitative Methods
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• Footnotes or Endnotes?
• Further Readings
• USC Libraries Tutorials and Other Guides
• Bibliography

Research Writing and Generative AI Large Language Models

A rapidly evolving phenomenon impacting higher education is the availability of generative artificial intelligence systems [such as Chat Generative Pre-trained Transformer or ChatGPT]. These systems have been developed from scanning text from millions of books, web sites, and other sources to enable algorithms within the system to learn patterns in how words and sentences are constructed. This allows the platforms to respond to a broad range of questions and prompts, generate stories, compose essays, create lists, and more. Generative AI systems are not actually thinking or understanding like a human, but they are good at mimicking written text based on what it has learned from the sources of input data used to build and enhance its artificial intelligence algorithms, protocols, and standards.

As such, generative AI systems [a.k.a., “Large Language Models”] have emerged , depending on one’s perspective, as either a threat or an opportunity in how faculty create or modify class assignments and how students approach the task of writing a college-level research paper. We are in the early stages of understanding how LLMs may impact learning outcomes associated with information literacy, i.e., fluency in effectively applying the skills needed to effectively identify, gather, organize, critically evaluate, interpret, and report information. However, before this is fully understood, Large Language Models w ill continue to improve and become more sophisticated, as will academic integrity detection programs used to identify AI generated text in student papers.

When assigned to write a research paper, it is up to your professor if using ChatGTP is permitted or not. Some professors embrace using these systems as part of an in-class writing exercise to help understand their limitations, while others will warn against its use because of their current defects and biases. That said, the future of information seeking using LLMs means that the intellectual spaces associated with research and writing will likely collapse into a single online environment in which students will be able to perform in-depth searches for information connected to the Libraries' many electronic resources.

As LLMs quickly become more sophisticated, here are some potential ways generative artificial intelligence programs could facilitate organizing and writing your social sciences research paper:

• Explore a Topic – develop a research problem related to the questions you have about a general subject of inquiry.
• Formulate Ideas – obtain background information and explore ways to place the research problem within specific contexts .
• Zero in on Specific Research Questions and Related Sub-questions – create a query-based framework for how to investigate the research problem.
• Locate Sources to Answer those Questions – begin the initial search for sources concerning your research questions.
• Obtain Summaries of Sources – build a synopsis of the sources to help determine their relevance to the research questions underpinning the problem.
• Outline and Structure an Argument – present information that assists in formulating an argument or an explanation for a stated position.
• Draft and Iterate on a Final Essay – create a final essay based on a process of repeating the action of text generation on the results of each prior action [i.e., ask follow up questions to build on or clarify initial results].

Despite their power to create text, generative AI systems are far from perfect and their ability to “answer” questions can be misleading, deceiving, or outright false. Described below are some current problems adapted from an essay written by Bernard Marr at Forbes Magazine and reiterated by researchers studying LLMs and writing. These issues focus on problems with using ChatGPT, but they are applicable to any current Large Language Model program .

• Not Connected to the Internet . Although the generative AI systems may appear to possess a significant amount of information, most LLM’s are currently not mining the Internet for that information [note that this is changing quickly. For example, an AI chatbot feature is now embedded into Microsoft’s Bing search engine, but you'll probably need to pay for this feature in the future]. Without a connection to the Internet, LLMs cannot provide real-time information about a topic. As a result, the scope of research is limited and any new developments in a particular field of study will not be included in the responses. In addition, the LLMs can only accept input in text format. Therefore, other forms of knowledge such as videos, web sites, audio recordings, or images, are excluded as part of the inquiry prompts.
• The Time-consuming Consequences of AI Generated Hallucinations . If proofreading AI generated text results in discovering nonsensical information or an invalid list of scholarly sources [e.g., the title of a book is not in the library catalog or found anywhere online], you obviously must correct these errors before handing in your paper. The challenge is that you have to replace nonsensical or false statements with accurate information and you must support any AI generated declarative statements [e.g., "Integrated reading strategies are widely beneficial for children in middle school"] with citations to valid academic research that supports this argument . This requires reviewing the literature to locate real sources and real information, which is time consuming and challenging if you didn't actually compose the text. And, of course, if your professor asks you to show what page in a book or journal article you got the information from to support a generated statement of fact, well, that's a problem. Given this, ChatGPT and other systems should be viewed as a help tool and never a shortcut to actually doing the work of investigating a research problem.
• Trouble Generating Long-form, Structured Content . ChatGPT and other systems are inadequate at producing long-form content that follows a particular structure, format, or narrative flow. The models are capable of creating coherent and grammatically correct text and, as a result, they are currently best suited for generating shorter pieces of content like summaries of topics, bullet point lists, or brief explanations. However, they are poor at creating a comprehensive, coherent, and well-structured college-level research paper.
• Limitations in Handling Multiple Tasks . Generative AI systems perform best when given a single task or objective to focus on. If you ask LLMs to perform multiple tasks at the same time [e.g., a question that includes multiple sub-questions], the models struggle to prioritize them, which will lead to a decrease in the accuracy and reliability of the results.
• Biased Responses . This is important to understand. While ChatGPT and other systems are trained on a large set of text data, that data has not been widely shared so that it can be reviewed and critically analyzed. You can ask the systems what sources they are using, but any responses can not be independently verified. Therefore, it is not possible to identify any hidden biases or prejudices that exist within the data [i.e., it doesn't cite its sources]. This means the LLM may generate responses that are biased, discriminatory, or inappropriate in certain contexts .
• Accuracy Problems or Grammatical Issues . The sensitivity to typographical errors, grammatical errors, and misspellings is currently very limited in LLMs. The models may produce responses that are technically correct, but they may not be entirely accurate in terms of context or relevance. This limitation can be particularly challenging when processing complex or specialized information where accuracy and precision are essential. Given this, never take the information that is generated at face value; always proofread and verify the results!

As they currently exist, ChatGPT and other Large Language Models truly are artificial in their intelligence. They cannot express thoughts, feelings, or other affective constructs that help a reader intimately engage with the author's written words; the output contains text, but the systems are incapable of producing creative expressions or thoughts, such as, conveying the idea of willful deception and other narrative devices that you might find in a poem or song lyric. Although creative devices, such as metaphors, idioms, imagery or subtleties in narrative rhythm, style, or voice, are rarely used in academic writing, it does illustrate that personalizing the way you present your research [e.g., sharing a personal story relating to the significance of the topic or being asked to write a reflective paper ] cannot be generated artificially.

Ethical Considerations

In the end, the ethical choice of whether to use ChatGTP or similar platforms to help write your research paper is up to you; it’s an introspective negotiation between you and your conscience. As noted by Bjork (2023) and others, though, it is important to keep in mind the overarching ethical problems related to the use of LLMs. These include:

• LLMs Do Not Understand the Meaning of Words . Without meaning as a guide, these systems use algorithms that rely on formulating context clues, stylistic structures, writing forms, linguistic patterns, and word frequency in determining how to respond to queries. This functionality means that, by default, LLMs perpetuate dominant modes of writing and language use while minimizing or hiding less common ones. As a result,...
• LLMs Prioritize Standard American English . White English-speaking men have dominated most writing-intensive sectors of the knowledge economy, such as, journalism, law, politics, medicine, academia, and perhaps most importantly, computer programming. As a result, writers and speakers of African American, Indigenous English, and other sociolinguistic dialects that use forms of language with its own grammar, lexicon, slang, and history of resistance within the dominant culture, are penalized and shamed for writing as they speak. The default functionality and outputs of LLMs, therefore, can privilege forms of English writing developed primarily by the dominant culture.
• LLMs Do Not Protect User Privacy . ChatGPT and other platforms record and retain the entire content of your conversations with the systems. This means any information you enter, including personal information or, for example, any documents you ask the systems to revise is retained and cannot be removed. Although the American Data Privacy and Protection Act was being considered within the 117th Congress, there is no federal privacy law that regulates how these for-profit companies can store, use, or possibly sell information entered into their platforms. Given this, it is highly recommended that personal information should never be included in any queries.

NOTE:   If your professor allows you to use generative AI programs or you decide on your own to use an LLM for a writing assignment, then this fact should be cited in your research paper, just as any other source of information used to write your paper should be acknowledged. Why? Because unlike grammar or citation tools, such as Grammarly or Citation Machine that correct text you've already written, generative AI programs are creating new content that is not in your own words. Currently, the American Psychological Association (APA), Modern Language Association (MLA) and the Chicago Manual of Style provide recommendations on how to cite generated text.

ANOTHER NOTE: LLMs have significant deficiencies that still require attention to thorough proofreading and source verification, an ability to discern quality information from misleading, false, irrelevant, or even made up information, a capacity to interpret and critically analyze what you have found, and the skills required to extrapolate meaning from the research your have conducted. For help with any or all of these elements of college-level research and writing, you should still contact a librarian for help.

YET ANOTHER NOTE: Researchers are finding early evidence that suggests over-reliance on ChatGPT and other LLM platforms for even the simplest writing task may, over time, undermine confidence in a student's own writing ability. Just like getting better at giving a class presentation or working on a group project, good writing is an acquired skill that can only be improved upon through the act of doing; the more you write, the more comfortable and confident you become expressing your own ideas, opinions, and judgements applied to the problem you have researched. Substituting LLMs with your own voice can inhibit your growth as a writer, so give yourself room to think and write creatively and with confidence by accepting LLMs as a tool rather than a definitive source of text.

For more information about Generative AI platforms and guidance on their ethical use in an academic setting, review the USC Libraries' Using Generative AI in Research guide for students and faculty. For an introduction to the limitations and potential pitfalls generative AI text generators applied to law, GO HERE .

Introduction to ChatGPT for Library Professionals. Mike Jones and Curtis Fletcher. USC Libraries, Library Forum, May 18, 2023; Aikins, Ross and Albert Kuo. “What Students Said About the Spring of ChatGPT.” Inside Higher Education , September 3, 2023; Baugh, John. “Linguistic Profiling across International Geopolitical Landscapes.” 152 Dædalus (Summer 2023): 167-177; ChatGPT. Library, Wesleyan University; Bjork, Collin. "ChatGPT Threatens Language Diversity." The Conversation , February 9, 2023; Understanding AI Writing Tools and their Uses for Teaching and Learning at UC Berkeley . Center for Teaching & Learning, University of California, Berkeley; Ellis, Amanda R., and Emily Slade. "A New Era of Learning: Considerations for ChatGPT as a Tool to Enhance Statistics and Data Science Education." Journal of Statistics and Data Science Education 31 (2023): 1-10; Ray, Partha Pratim. “ChatGPT: A Comprehensive Review on Background, Applications, Key Challenges, Bias, Ethics, Limitations and Future Scope.” Internet of Things and Cyber-Physical Systems (2023); Uzun, Levent. "ChatGPT and Academic Integrity Concerns: Detecting Artificial Intelligence Generated Content." Language Education and Technology 3, no. 1 (2023); Lund, Brady D. Et al. “ChatGPT and a New Academic Reality: Artificial Intelligence Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing.” Journal of the Association for Information Science and Technology 74 (February 2023): 570–581; Rasul, Tareq et al. "The Role of ChatGPT in Higher Education: Benefits, Challenges, and Future Research Directions.” Journal of Applied Learning and Teaching 6 (2023); Rudolph, Jürgen, Samson Tan, and Shannon Tan. "ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education?" Journal of Applied Learning and Teaching 6, no. 1 (2023): 342-362; Marr, Bernard. “The Top 10 Limitations Of ChatGPT.” Forbes (March 3, 2023): https://www.forbes.com/sites/bernardmarr/2023/03/03/the-top-10-limitations-of-chatgpt/?sh=41ae78e8f355; Thinking about ChatGPT? Academic Integrity at UBC, Office of the Provost and Vice-President Academic, University of British Columbia.

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AI Research Paper Generator

Transform your research journey with groundbreaking ai technology, effortlessly crafting academic papers that meet professional standards..

Streamlining Your Academic Writing Process Using EssayGPT

EssayGPT's AI research paper generator simplifies the process of crafting well-structured, comprehensive research papers. Here is how to do it in simple steps:

• 1. Get started by entering the central theme of your research in the 'Essay Topic' box.
• 2. Add up to five specific keywords related to your research topic to create relevant and focused content.
• 3. Enter your desired format including APA, MLA, or Harvard for accurate citation and formatting.
• 4. Use the 'Outline Suggestions' and 'Essay Title' boxes to tailor the structure and title to your specific needs.
• 5. Pick your target audience, tone of voice, and language. Then, hit the 'Generate' button to create your paper.

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Choosing EssayGPT's AI Research Paper Generator: A Cut Above the Rest

When it comes to crafting a research paper, the choice of tools can be a game-changer. With cutting-edge features, EssayGPT's AI research paper generator stands out as a superior choice for researchers seeking an efficient, versatile, and high-quality AI research paper writer.

This AI research paper generator from EssayGPT distinguishes itself from other AI writers for numerous compelling reasons such as:

Advanced AI Capabilities: At the heart of EssayGPT's AI research paper generator lies the integration of both GPT-3.5 and GPT-4 technologies. This dual AI power ensures not only advanced capabilities in generating research content but also guarantees versatility and depth in analysis, making it an unparalleled AI paper writer.

Full Customizability: Unlike standard research paper writers, EssayGPT's AI research paper generator provides complete control over the creation process. Whether it's adjusting the style, format, or specific content requirements, EssayGPT's AI research paper generator caters to all your unique academic needs.

Professional Citations and Reference Format: Catering to diverse academic standards, EssayGPT's AI paper generator offers a variety of professional paper reference formats. From APA to MLA or custom formats, the tool ensures your research paper meets the highest academic criteria.

Multi-Language Support: Breaking language barriers, EssayGPT's research paper writer extends its support to over 30 languages. This feature not only broadens the tool's accessibility but also makes it an invaluable asset for international research communities.

Advanced Context-Aware Technology: Understanding the importance of context in academic writing, EssayGPT's AI research paper generator employs advanced context-aware technology. This ensures that each paper generated is not only relevant but also resonates with the intended research topic, making it more than just an ordinary AI paper generator.

How Can You Benefit From EssayGPT's AI Research Paper Generator?

Embrace the future of research with EssayGPT's AI research paper generator. This tool is not just an advanced AI paper writer; it's a revolution in academic writing. Designed to cater to a diverse range of research needs, it offers an unparalleled blend of efficiency, customization, and quality, ensuring your research stands out.

Let's explore how it can significantly benefit users in the academic and professional fields:

Innovative Idea Cultivation: The AI research paper generator serves as a springboard for your research ideas. It helps conceptualize diverse topics, offering unique perspectives and thought-provoking angles, essential for groundbreaking research papers.

Time-Saving and Cost-Effective Solution: Reduce the hours spent on research and writing significantly. This free tool streamlines the writing process, allowing more focus on analysis and less on structuring, making efficient use of your valuable time and resources.

Simplifying the Writing Journey: Experience a transformed writing process with our AI paper writer. From outlining to drafting, the tool organizes and structures your thoughts, turning complex ideas into well-articulated research papers with ease.

Educational Enhancement: Beyond just generating papers, the AI research paper generator is a learning tool. It exposes users to varied writing styles and structures, enhancing their understanding of academic writing norms and encouraging skill development in research methodology.

EssayGPT AI Research Paper Generator’s Innovations in AI Paper Writing

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Research paper summarizer, ai essay writer, essay checker, chatgpt essay writer, essay extender, essay introduction generator, essay paraphraser, essay rewriter, essay outline generator, essay topic generator, dive into a world of inspiration.

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1. How does the EssayGPT AI research paper generator ensure data accuracy in the generated papers?

The EssayGPT AI research paper generator utilizes an advanced AI algorithm to fetch relevant and credible information. It employs sophisticated contextual understanding and keyword analysis to maintain accuracy. However, this tool is designed to augment rather than replace comprehensive manual research.

2. How does the EssayGPT AI research paper generator address plagiarism concerns?

The tool employs machine learning algorithms to synthesize information, ensuring each output is unique and original. Additionally, EssayGPT recommends users utilize its plagiarism checker, for thorough plagiarism verification.

3. Can the EssayGPT AI research paper generator handle complex academic disciplines?

Yes, the AI research paper generator is equipped with algorithms capable of handling a wide range of subjects, including advanced and specialized academic disciplines. It understands the complexity of various subjects and structures the research paper accordingly. Users are always advised to review and edit the generated content for precision.

4. What should I do if I find issues with the paper generated by EssayGPT's tool?

If you encounter any issues with the generated paper, you can always modify your inputs and regenerate them for better results. For specific concerns or improvement suggestions, you can always contact our support .

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How to Write a Research Paper (+ Free AI Research Paper Writer)

Table of contents

Meredith Sell

Over the years, I've managed to vastly improve how I write research papers.

The three major game-changers for me, in terms of quality of the finished piece, have been:

• Following the research paper checklist (see below)
• Developing the thesis before starting to write
• And, more recently, using AI to improve my research paper draft

Let's break down each of these elements and produce the kind of research papers that get cited in magazines.

FREE AI research paper writer > FREE AI research paper writer >

What is a research paper, and how is it written differently?

Research papers are longer and more in-depth than essays. They require extensive research and evidence-based arguments. Research papers also typically have a more formal structure and require citations and references.When academics want to find a balanced and comprehensive view on a given topic, they usually seek a research paper.

Like most writing assignments, a research paper can be broken down into simple steps. Research papers follow the same basic writing process as explanatory or persuasive essays — but instead of making an argument or drawing greater meaning from the topic, the research paper is primarily concerned with concrete facts that may be analyzed, examined, or interpreted to better understand the paper’s central topic.

This is good news if you enjoy research: you’ll be doing a lot of it. The ultimate quality of your paper depends on you conducting thorough, complete research — and relying on reputable sources.

How to Properly Write a Research Paper Using AI

1. make a checklist based on the assignment description, and fill it out with ai.

Your professor has likely specified some criteria for your research paper:

• Length (in pages or words)
• Type of topic (the War of 1812, ancient Greece, agriculture, etc.)
• Elements that must be included, such as analysis, discussion, and comparison.
• Types of sources you must draw from (academic papers, encyclopedias, etc.)
• Source attribution style
• Formatting style

Go through the assignment description and create a checklist of those criteria. You can use this checklist throughout the research and writing process as well:

AI can really help you get some traction with your research paper in the preperation stage. This includes two main steps:

• Brainstorming paper topic idea
• Outlining based on your topic, basing the prompt on the assignment

2. Choose a topic you’re curious about, or use AI to help you with that

A sure way to write a boring research paper is to pick a topic you have no interest in, like summer temperatures in the desert or the life cycle of a flea. (Though someone’s probably interested in those things.)

Instead, follow your curiosity.

If your paper is for a writing class, you may have a lot of freedom to choose what you write about, so tap into your interests. Are you intrigued by the history of roller skating or the invention of the soccer cleat? Or how teen social dynamics have changed with evolving technology (think: home phones → online instant messaging → flip phones → smartphones)?

If you’re writing for a class in a subject like history, art, or science, you’ll probably have more restrictions on what you can write about — like a time period or type of art or science — but you can still use your curiosity to pick an interesting topic.

If you’re having a tough time, try brainstorming a list of things you’ve wondered about. Ask “ what’s up with… ” and see what comes to mind.

For example:

What’s up with traffic circles and why are they supposedly better for traffic patterns than a light or four-way stop?

What’s up with country music sounding more and more like hip-hop?

What’s up with people who have gluten allergies being able to eat bread in Europe but not the US?

Once you have a list, choose the topic you find most interesting (and appropriate for the assignment).

If your mind draws a blank, you can utilize AI to help you choose a topic. Let's say your course is about mid century art. You can go to a tool like Wotdtune and ask it to give you ideas for creative mid century art essays. See example below.

3. Develop your thesis (and guide your research) by asking a research question

Even though a research paper may not necessarily take a side on a topic, it still needs a thesis, aka a central idea or focus that drives the piece from beginning to end. 

We wrote a whole guide on writing thesis statements , so here we’ll just give you this tip:

Use a research question to develop your thesis

A research question is a variation on the “What’s up with…” questions from the last tip — but it will zoom in more specifically on the aspect of your topic that you’re investigating.

Why were the Irish so dependent on potatoes?

Did any women in ancient Greece enjoy relative freedom and autonomy?

You may already know the answer to these questions, or you may not. Either way, they give you a place to start in your research. Once you have your question, set out to:

• Find the initial answer.
• Gather more context (the who, what, when, where, why, how) around that answer.
• Revise your research question and turn it into your thesis.

This process helps tighten your focus from a broad topic that could fill books to a specific angle that can be meaningfully explored in the few pages of your paper.

Instead of the potato famine , write about why England was to blame for the potato famine’s devastating effects on the Irish.

Instead of ancient Greece or women in ancient Greece , write about how Spartan women’s lives differed from the lives of women in Athens.

4. Skim sources and use AI to perform research for your paper

Your research question can help you quickly determine whether information is relevant to your paper. As you gather initial sources, skim them — and then use your research question to decide whether to keep or discard the source. 

Does the source cover information relevant to my research question?

Yes: Keep to read later.

No: Discard and move on to the next source.

This approach will save you precious research time. You won’t waste limited hours reading sources that don’t have a single helpful fact.

If skimming is hard for you (as a deep reader, I get it), Wordtune can help. Paste the link to your online source, upload a scanned PDF, or copy the text, and the tool will scan and summarize for you. You can always come back later and closely read the most useful sources.

5. Make note of the most interesting facts you find

Along with taking detailed notes of your research (complete with all the source info you need to make proper citations), highlight the most interesting facts you come across. You could stick these in a section together or mark them in a way that makes them stand out.

Why should you do this?

Because later on, one of these fascinating factoids could have a direct connection to your thesis — and make a great hook for the start of your paper. Instead of digging through all of your notes to try to remember what that interesting tidbit was, you’ll be able to find it easily.

6. Organize your research

There are plenty of ways to organize your notes, but I suggest breaking them up into subtopics and categories.

• Subtopic: A topic related to your main topic or thesis that needs to be explained and understood by readers in order to understand your main topic or thesis. For example: Land ownership in Ireland under British rule.
• Category: An overarching concept that several subtopics fall under. For example: British restrictions on the Irish.

To start, I would focus on the subtopics and then group them into categories.

As you organize, use the formatting tools in your word processor to tag headings and subheadings. For example, all categories would be an H2 (Heading 2), while all subtopics would be an H3 (Heading 3). 

Tagging your categories and subtopics this way will help you develop your outline. Just organize your categories and subtopics in a logical order, and you’ll have a skeleton of an outline ready to go.

7. Write with your research document open

No one can remember everything they found while researching — you’ll need to reference your research document throughout the writing process. No question there.

But you can make this easier (and keep your writing process efficient) by:

Keeping your research document open and in clear view.

I like to put my draft document and my research document side by side on my screen, so I can see them both at the same time. 

Another approach would be to paste the information you need directly into your draft document — in the order you’ll need it. (Your outline will help you know what you need.)

8. Steal the TK trick from journalists

In the middle of drafting your paper, you find that you’re missing a fact. 

You neglected to write down how many Irish people starved due to the potato famine.

You don’t know what age Spartan women were able to own property.

Instead of derailing your writing and searching for that information, write the sentence you want to write and stick a “TK” where the missing fact should go.

“TK” stands for “to come” (don't ask us why) and is a placeholder used by journalists to mark missing information they’ll fill in later. Using TK allows you to keep writing without getting off track every time you discover your research didn’t cover everything.

A whopping TK Irish people starved, thanks to the combination of famine and British oppression.

At age TK , Spartan girls became women who were able to own property, a right that their sisters in Athens did not enjoy.

9. Revise, explain, paraphrase with AI as your research/writing assistant

Using the right researching tools can get you a lot way.

If you’re ever at a loss for words — writing clunky, clumsy sentences, struggling to explain a concept, or having a hard time paraphrasing a source — Wordtune can serve as your AI sidekick.  

Simply highlight the sentence in question and browse Wordtune’s suggestion for a better wording.

You can also use Wordtune Spices to come up with examples and counter arguments for whatever you're writing about or even find stats and facts, complete with source citations

Wordtune doesn’t do all of the writing for you, but it can help you sharpen your ideas on the sentence level, so you can hand in a research paper with good writing that’s still very much your own.

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Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor

Tiffany i leung.

1 JMIR Publications, Inc, Toronto, ON, Canada

2 Department of Internal Medicine (adjunct), Southern Illinois University School of Medicine, Springfield, IL, United States

Taiane de Azevedo Cardoso

Amaryllis mavragani, gunther eysenbach.

3 University of Victoria, Victoria, BC, Canada

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Anthropic Terms of Service, version 3.0, updated July 8, 2023.

GPTZero Terms of Use, updated January 22, 2023.

The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications’ editorial policies on these issues. We further detail JMIR Publications’ approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.

Introduction

Technology tools are useful for making the scientific writing process more timely and effective. Many advances have been made in terms of the tools available to help conduct more sophisticated statistical analysis, manage references, and check grammar. Among these advances, large language model (LLMs) are neural networks trained on large corpora of textual information that can be fine-tuned to respond to natural language queries in a conversational fashion. In late 2022, OpenAI released ChatGPT, an artificial intelligence (AI) chatbot [ 1 ] that uses an LLM, which has become enormously popular and a focal point for regulatory debate in a matter of months. Since then, countless LLMs have been developed and launched for research, commercial, and other applications.

The ethics of generative AI use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community [ 2 , 3 ]. More generally, there are already broader calls for the regulation of AI, and LLMs in particular, in general public use [ 4 , 5 ]. This is because generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. For instance, LLMs may produce errors and misleading information, especially when dealing with technical topics that they may have had limited data to train on. In the technical report released by OpenAI, it is acknowledged that Generative Pre-trained Transformer (GPT)–4 can produce biased and unreliable content [ 6 ]. Such biased output can result from inherent biases in the data on which they were trained. A recent study published in the Journal of Medical Internet Research showed that ChatGPT was able to generate a highly convincing, fraudulent scientific manuscript article in approximately 1 hour [ 7 ]. The authors used tools to detect AI-generated text (AI Detector and AI Text Classifier), and the results were inconclusive, indicating that these tools were unable to determine that the manuscript was generated by ChatGPT. Finally, the authors were able to detect mistakes in the generated article, specifically in the references, as ChatGPT generated fictitious citations. These findings reinforce the importance of having well-established regulations around the use of ChatGPT in the scientific field.

For authors of academic manuscripts, key issues of concern include the need to fact-check AI-generated content of any form (including but not limited to textual information or graphics); assign accountability for AI-generated information; and disclose transparently the use of generative AI in producing any scholarly or scientific work, especially when it impacts the meaning and content of the information submitted for potential publication [ 8 ]. For peer reviewers, additional issues pertain to the typical processing of manuscripts, wherein humans traditionally have generated peer review reports and issued editorial decisions on revising, rejecting, or accepting manuscripts. Currently, it is possible to prompt generative AI to facilitate these processes when given specific inputs and prompts as well. For editors, receiving AI-generated material in manuscripts (from authors) or in peer review reports (from peer reviewers) also warrant additional considerations.

In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications’ editorial policies on these issues, with the goal of ensuring the integrity of the science published and the publishing process. We further detail JMIR Publications’ approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.

For Authors

In scientific publishing, there is already historical precedent that the transparency of authorship is essential to the integrity of scientific publication [ 9 ]. Regarding AI, general consensus already states that AI cannot be a listed coauthor on a manuscript because of the inability for the AI to be accountable for the content written [ 2 , 10 - 13 ]. The lack of accountability and ability to give consent to be published as a coauthor would be consistent with not listing an AI tool as a coauthor [ 14 ]. According to Committee on Publication Ethics (COPE) guidance, “AI tools cannot meet the requirements for authorship as they cannot take responsibility for the submitted work. As non-legal entities, they cannot assert the presence or absence of conflicts of interest nor manage copyright and license agreements” [ 2 ]. The World Associate of Medical Editors (WAME) states in their Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publication that “Chatbots cannot be authors” [ 11 ]. One examination of ChatGPT (the free version of GPT-3) against the Contributor Roles Taxonomy (CRediT) authorship criteria [ 15 ] noted that the chatbot meets only 3 of 14 criteria for authorship [ 16 ]. Unfortunately, before such widespread publisher policies and recommendations became the norm, some manuscripts and preprints have already been published that identified ChatGPT as a coauthor [ 13 ].

At JMIR Publications, early guidance in our knowledge base of editorial policies explained that authors must appropriately include a description of the use of generative AI in the conduct or reporting of scientific work; otherwise, if this information is not a part of the study design (eg, in the Methods section of a manuscript), then providing acknowledgment of the use of generative AI in writing or creating text, figures, or other content for scientific publication is required [ 17 - 19 ]. We welcome authors to submit relevant work to the flagship journal of JMIR Publications, the Journal of Medical Internet Research , which now has a section on generative language models (including ChatGPT), where it may be appropriate to submit work that uses such technology as a core component of the work ( Table 1 ). If an author does not use AI to generate any portions of a submitted manuscript, it would be appropriate for the author also to provide a pertinent attestation in their cover letter on submission.

Author’s responsibilities when using generative artificial intelligence (AI) in preparing a manuscript.

Such acknowledgements must be fully transparent, precise, and complete throughout the submission, editorial, and production processes and will be disclosed upon the publication of a manuscript, if accepted for publication after the disclosure has been provided [ 19 ]. In addition, we strongly recommend authors to supply their transcripts, including complete prompts and responses, in supplementary files (whether or not it is published) as exemplified in Eysenbach [ 20 ], as this serves as additional information for the peer reviewers or editor to consider in their evaluation of the manuscript.

Authors must also be cautious of the use of generative AI because of its predispositions to hallucination information and references [ 20 - 22 ]. Because generative AI cannot be accountable for the outputs and possible hallucinations that they generate in response to a prompt, authors are accountable for fact- and reference-checking any references suggested by a generative AI tool. Authors must also be cautious of the potential for unintentional plagiarism (because the AI may not be able to properly source or cite literature) [ 23 ] or overt AI plagiarism (the authors passing off or taking credit for the production of statements that were generated by AI). Either form of plagiarism is deemed not acceptable and would be examined carefully in accordance with COPE guidance [ 24 ]. Authors may wish to adhere to the WAME recommendation that they “specify what they have done to mitigate the risk of plagiarism, provide a balanced view, and ensure the accuracy of all their references” [ 11 ]. Furthermore, instances of suspected or potential scientific misconduct or violations of publication ethics principles, regardless of the involvement or use of generative AI, would be investigated in accordance with JMIR Publications policies, which adhere to COPE guidance.

For Peer Reviewers

For peer reviewers, JMIR Publications adheres to expectations similar to that for authors: specifically, peer reviewers are accountable for the content of AI-generated comments submitted in a peer review. Consequently, peer reviewers are strongly advised to still ensure that the quality and content of the peer review meet the recommended standards described elsewhere in JMIR Publications policies [ 25 ]. However, peer reviewers must remain cautious about the risks of such use, including but not limited to the perpetuation of bias and nonneutral language in AI use (eg, gender, racial, political, or other biases based on individual characteristics) [ 26 , 27 ] and information leakage or breaches of confidentiality [ 27 , 28 ] ( Table 2 ). The latter point on the confidentiality of manuscript information warrants a more extended clarification: when authors agree to open peer review of their JMIR Publications manuscript (ie, on JMIR Preprints [ 29 ]), information leakage is of lesser concern because authors have already consented to an open peer review process, and their manuscript is publicly viewable. JMIR Publications encourages open peer review [ 30 ]. However, in some instances, authors wish to maintain a traditional, closed peer review process; in such cases, peer reviewers may risk information leakage by engaging generative AI in assisting them in the process of peer review report generation.

Peer reviewer’s responsibilities when using generative artificial intelligence (AI) in peer review.

In addition to accountability and confidentiality, transparency is essential to ensure the integrity of the peer review process. Agencies such as the US National Institutes of Health (NIH) have issued clear guidance that the use of AI in assisting a review with the grant peer review process is prohibited due to a breach of their confidentiality and nondisclosure agreements [ 32 ]. Some publishers have opted to ban generative AI use or restrict use to in-house or licensed technologies [ 33 , 34 ]. The WAME states that “peer reviewers should specify, to authors and each other, any use of chatbots in the evaluation of the manuscript and generation of reviews” [ 11 ].

At JMIR Publications, we adhere to this guidance of transparency and disclosure; we do not endorse a ban on generative AI in peer review, which can be counterproductive in various ways [ 14 , 35 ]. Peer reviewers are expected to disclose and describe their use of generative AI ( Table 2 ). As JMIR Publications follows single-blind peer review with unblinding only upon publication, the publisher may include a comment (Editorial Notice) at their discretion, which would accompany the publication history of a manuscript regarding a peer reviewer’s disclosure of AI use during the peer review process. Here, we further elaborate on some of the detailed considerations a peer reviewer must account for when considering generative AI use to support their personal peer review process.

Importantly, when peer reviewers use generative AI to support their peer review, they are accountable to ensuring the confidentiality of the peer review process. Detailed and careful review of the terms of use of any generative AI is strongly advised, if not required. Furthermore, if the peer reviewer has any doubts about potential information leakage after a careful review of the terms of use of a generative AI tool, then they should not engage in its use for this task. For example, in the free version of Open AI’s ChatGPT, their March 14, 2023, Terms of Use ( Figure 1 and Multimedia Appendix 1 ) do not exclude the potential for secondary use or reuse of provided information (“Input”), although the use of their application programming interface (API) suggests that they would exclude the reuse of input: “We do not use Content that you provide to or receive from our API to develop or improve our Services. We may use Content from Services other than our API to help develop and improve our Services” [ 36 ]. Because there is potential for the input to be reused, JMIR Publications would not permit the use of the free version of ChatGPT for assisting with peer review comment generation.

(A) Screenshot of 3(c) from OpenAI’s ChatGPT Terms of Use ( Multimedia Appendix 1 ). (B) Screenshot of 6(a) from Anthropic’s Claude Terms of Service ( Multimedia Appendix 2 ).

In another example, Anthropic’s Claude also has clearly stated language in their July 8, 2023, Terms of Service ( Figure 1 and Multimedia Appendix 2 ): “You represent and warrant that you have all rights, and have provided any notices and obtained any consents that are necessary for us to process any Prompts you submit to the Services in accordance with our Terms. You also represent and warrant that your submission of Prompts to us will not violate our Terms...including intellectual property laws and any privacy or data protection laws governing personal information contained in your Prompts” [ 37 ]. Because peer reviewers do not have “all rights” or have not “obtained any consents” with regard to a manuscript they may review, JMIR Publications would not permit the use of the free version of Claude for assisting with peer review comment generation.

Peer reviewers for JMIR Publications journals are advised to carefully review the content of the Peer Reviewer Hub for guidance [ 25 ], including guidance on writing a high-quality peer review [ 31 ]. Instances of suspected or potential peer review manipulation, fraud, scientific misconduct, or violations of publication ethics principles during the peer review process would be investigated in accordance with JMIR Publications policies, which adhere to COPE guidance.

For Editors

AI is already in use by some publishers, as an attempt to optimize the editorial workflow. For instance, some publishers have publicly available tools where the authors can add the title, keywords, and abstract of their manuscript, and the AI tool will list the journals that this work is more suitable for. This approach could be time-saving for both the editors and the authors.

Similar to peer reviewers and authors, editors evaluating and issuing decisions about manuscripts are accountable for the content of their decisions and the final decision on the manuscript, whether it is accepted or rejected ( Table 3 ). This includes whether the editor may choose to use generative AI to assist in the summarization of peer review reports or the generation of text for an editorial decision [ 11 , 14 ]. The transparency and maintenance of confidentiality again remain essential, in precisely the same ways as noted for peer reviewers: the editor is accountable for ensuring the confidentiality of the peer review process where it is required (ie, when authors choose not to engage in open peer review).

Editor’s responsibilities when using generative artificial intelligence (AI) in peer review.

When editors evaluate peer reviews of a manuscript that they are assigned to, the editor should follow JMIR Publications policies in evaluating the quality, validity, relevance, and professional language use of a peer review. In a recommendation from the WAME, similar to peer reviewers, editors are also accountable for the generated content, the transparency of the disclosure of use, and maintaining confidentiality during the peer review process [ 11 ]. Routinely, plagiarism is a serious concern in scientific publishing, and existing tools are able to identify writing that is plagiarized from existing published literature. AI plagiarism occurs when a person generates extensive material using AI and claims it as their own work [ 7 , 11 , 38 , 39 ]. Plagiarism detection tools now must encompass AI plagiarism as well [ 38 , 40 ]. To avoid AI plagiarism, authors must disclose the use of generative AI as detailed above. Peer reviews may electively opt to use plagiarism detection tools when performing a peer review and would be required to adhere to appropriate disclosures as previously detailed. Editors (or the publisher) may use tools to detect whether a manuscript presents content written by generative AI, although all users of any AI plagiarism detection tools must again adhere to the principles of transparency and confidentiality. For example, although GPTZero may seem to be a promising option, there is a risk of information leakage or loss of confidentiality, based upon a review of its terms of use [ 41 ] ( Multimedia Appendix 3 ). If an editor identifies issues with research integrity regarding any of the above guidance for authors or peer reviewers, then these would be investigated according to JMIR Publications policies.

Closing Comments

The accountability of parties using generative AI, transparency regarding complete disclosure, and the maintenance of confidentiality are fundamental in maintaining the integrity of the scientific record and are key components of JMIR Publications’ editorial policies. Because of the rapidly evolving nature of AI technologies, related policies, regulations [ 42 ], investigations [ 43 ], and best practices [ 44 , 45 ], JMIR Publications looks forward to continuing to lead and evolve as an innovator in scientific publishing.

Acknowledgments

This manuscript was produced as a result of discussion among JMIR Publications staff and managers.

Abbreviations

Multimedia Appendix 1

Multimedia appendix 2, multimedia appendix 3.

Authors' Contributions: TIL and TdAC contributed to writing the original draft. TIL, TdAC, AM, and GE contributed to conceptualization, writing, review, and editing of the manuscript. TIL contributed to project administration. GE contributed to supervision.

Conflicts of Interest: TIL is the scientific editorial director at JMIR Publications. TdAC and AM are scientific editors at JMIR Publications. GE is the founder, chief executive officer, and executive editor of JMIR Publications, receives a salary and owns equity.

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How to Research and Write Using Generative AI Tools

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Instructor: Dave Birss

You’ve probably already heard about ChatGPT, but did you know it can make you better at your job? Join instructor Dave Birss for a crash course in generative AI and learn how to get started with prompt engineering for ChatGPT and other AI chatbots to upskill as a researcher and a writer.

Dave shows you how to create effective prompts that deliver high-quality, task-relevant results. Get an overview of some of the key considerations of working with generative AI with hands-on, practical strategies to improve your research and writing. Find out how to summarize complex information, view subjects from multiple perspectives, build user personas and strategic models, analyze writing style, outline ideas, and generate new content. By the end of this course, you’ll be ready to leverage the power of ChatGPT and other chatbots to deliver more consistent writing outcomes every time.

Note: This course was created by Dave Birss. We are pleased to host this training in our library.

Get science-backed answers as you write with Paperpal's Research feature

How to Write an Abstract in Research Papers (with Examples)

An abstract in research papers is a keyword-rich summary usually not exceeding 200-350 words. It can be considered the “face” of research papers because it creates an initial impression on the readers. While searching databases (such as PubMed) for research papers, a title is usually the first selection criterion for readers. If the title matches their search criteria, then the readers read the abstract, which sets the tone of the paper. Titles and abstracts are often the only freely available parts of research papers on journal websites. The pdf versions of full articles need to be purchased. Journal reviewers are often provided with only the title and abstract before they agree to review the complete paper. [ 1]  

Abstracts in research papers provide readers with a quick insight into what the paper is about to help them decide whether they want to read it further or not. Abstracts are the main selling points of articles and therefore should be carefully drafted, accurately highlighting the important aspects. [ 2]  

This article will help you identify the important components and provide tips on how to write an abstract in research papers effectively

What is an Abstract?  

An abstract in research papers can be defined as a synopsis of the paper. It should be clear, direct, self-contained, specific, unbiased, and concise. These summaries are published along with the complete research paper and are also submitted to conferences for consideration for presentation.  

Abstracts are of four types and journals can follow any of these formats: [ 2]  

• Structured  
• Unstructured  
• Descriptive  
• Informative  

Structured abstracts are used by most journals because they are more organized and have clear sections, usually including introduction/background; objective; design, settings, and participants (or materials and methods); outcomes and measures; results; and conclusion. These headings may differ based on the journal or the type of paper. Clinical trial abstracts should include the essential items mentioned in the CONSORT (Consolidated Standards Of Reporting Trials) guidelines.  

Figure 1. Structured abstract example [3] 

Unstructured abstracts are common in social science, humanities, and physical science journals. They usually have one paragraph and no specific structure or subheadings. These abstracts are commonly used for research papers that don’t report original work and therefore have a more flexible and narrative style.  

Figure 2. Unstructured abstract example [3] 

Descriptive abstracts are short (75–150 words) and provide an outline with only the most important points of research papers. They are used for shorter articles such as case reports, reviews, and opinions where space is at a premium, and rarely for original investigations. These abstracts don’t present the results but mainly list the topics covered.  

Here’s a sample abstract . [ 4]  

“Design of a Radio-Based System for Distribution Automation”  

A new survey by the Maryland Public Utilities Commission suggests that utilities have not effectively explained to consumers the benefits of smart meters. The two-year study of 86,000 consumers concludes that the long-term benefits of smart meters will not be realized until consumers understand the benefits of shifting some of their power usage to off-peak hours in response to the data they receive from their meters. The study presents recommendations for utilities and municipal governments to improve customer understanding of how to use the smart meters effectively.  

Keywords: smart meters, distribution systems, load, customer attitudes, power consumption, utilities  

Informative abstracts (structured or unstructured) give a complete detailed summary, including the main results, of the research paper and may or may not have subsections.   

Figure 3. Informative abstract example [5] 

Purpose of Abstracts in Research    

Abstracts in research have two main purposes—selection and indexing. [ 6,7]  

• Selection : Abstracts allow interested readers to quickly decide the relevance of a paper to gauge if they should read it completely.   
• Indexing : Most academic journal databases accessed through libraries enable you to search abstracts, allowing for quick retrieval of relevant articles and avoiding unnecessary search results. Therefore, abstracts must necessarily include the keywords that researchers may use to search for articles.  

Thus, a well-written, keyword-rich abstract can p ique readers’ interest and curiosity and help them decide whether they want to read the complete paper. It can also direct readers to articles of potential clinical and research interest during an online search.  

Contents of Abstracts in Research  

Abstracts in research papers summarize the main points of an article and are broadly categorized into four or five sections. Here are some details on how to write an abstract .   

Introduction/Background and/or Objectives  

This section should provide the following information:  

• What is already known about the subject?  
• What is not known about the subject or what does the study aim to investigate?  

The hypothesis or research question and objectives should be mentioned here. The Background sets the context for the rest of the paper and its length should be short so that the word count could be saved for the Results or other information directly pertaining to the study. The objective should be written in present or past simple tense.  

Examples:  

The antidepressant efficacy of desvenlafaxine (DV) has been established in 8-week, randomized controlled trials. The present study examined the continued efficacy of DV across 6 months of maintenance treatment . [ 1]  

Objective: To describe gastric and breast cancer risk estimates for individuals with CDH1 variants.  

Design, Setting, and Participants (or Materials and Methods)  

This section should provide information on the processes used and should be written in past simple tense because the process is already completed.  

A few important questions to be answered include:  

• What was the research design and setting?  
• What was the sample size and how were the participants sampled?  
• What treatments did the participants receive?  
• What were the data collection and data analysis dates?  
• What was the primary outcome measure?  

Hazard ratios (HRs) were estimated for each cancer type and used to calculate cumulative risks and risks per decade of life up to age 80 years.  

This section, written in either present or past simple tense, should be the longest and should describe the main findings of the study. Here’s an example of how descriptive the sentences should be:  

Avoid: Response rates differed significantly between diabetic and nondiabetic patients.  

Better: The response rate was higher in nondiabetic than in diabetic patients (49% vs 30%, respectively; P<0.01).  

This section should include the following information:  

• Total number of patients (included, excluded [exclusion criteria])  
• Primary and secondary outcomes, expressed in words, and supported by numerical data  
• Data on adverse outcomes  

Example: [ 8]  

In total, 10.9% of students were reported to have favorable study skills. The minimum score was found for preparation for examination domain. Also, a significantly positive correlation was observed between students’ study skills and their Grade Point Average (GPA) of previous term (P=0.001, r=0.269) and satisfaction with study skills (P=0.001, r=0.493).  

Conclusions  

Here, authors should mention the importance of their findings and also the practical and theoretical implications, which would benefit readers referring to this paper for their own research. Present simple tense should be used here.  

Examples: [ 1,8]  

The 9.3% prevalence of bipolar spectrum disorders in students at an arts university is substantially higher than general population estimates. These findings strengthen the oft-expressed hypothesis linking creativity with affective psychopathology.  

The findings indicated that students’ study skills need to be improved. Given the significant relationship between study skills and GPA, as an index of academic achievement, and satisfaction, it is necessary to promote the students’ study skills. These skills are suggested to be reinforced, with more emphasis on weaker domains.  

When to Write an Abstract  

In addition to knowing how to write an abstract , you should also know when to write an abstract . It’s best to write abstracts once the paper is completed because this would make it easier for authors to extract relevant parts from every section.  

Abstracts are usually required for: [ 7]    

• submitting articles to journals  
• applying for research grants   
• writing book proposals  
• completing and submitting dissertations  
• submitting proposals for conference papers  

Mostly, the author of the entire work writes the abstract (the first author, in works with multiple authors). However, there are professional abstracting services that hire writers to draft abstracts of other people’s work.   

How to Write an Abstract (Step-by-Step Process)  

Here are some key steps on how to write an abstract in research papers: [ 9]  

• Write the abstract after you’ve finished writing your paper.  
• Select the major objectives/hypotheses and conclusions from your Introduction and Conclusion sections.  
• Select key sentences from your Methods section.  
• Identify the major results from the Results section.  
• Paraphrase or re-write the sentences selected in steps 2, 3, and 4 in your own words into one or two paragraphs in the following sequence: Introduction/Objective, Methods, Results, and Conclusions. The headings may differ among journals, but the content remains the same.  
• Ensure that this draft does not contain: a.   new information that is not present in the paper b.   undefined abbreviations c.   a discussion of previous literature or reference citations d.   unnecessary details about the methods used  
• Remove all extra information and connect your sentences to ensure that the information flows well, preferably in the following order: purpose; basic study design, methodology and techniques used; major findings; summary of your interpretations, conclusions, and implications. Use section headings for structured abstracts.  
• Ensure consistency between the information presented in the abstract and the paper.  
• Check to see if the final abstract meets the guidelines of the target journal (word limit, type of abstract, recommended subheadings, etc.) and if all the required information has been included.  

Choosing Keywords for Abstracts  

Keywords [ 2] are the important and repeatedly used words and phrases in research papers and can help indexers and search engines find papers relevant to your requirements. Easy retrieval would help in reaching a wider audience and eventually gain more citations. In the fields of medicine and health, keywords should preferably be chosen from the Medical Subject Headings (MeSH) list of the US National Library of Medicine because they are used for indexing. These keywords need to be different from the words in the main title (automatically used for indexing) but can be variants of the terms/phrases used in the title, abstract, and the main text. Keywords should represent the content of your manuscript and be specific to your subject area.  

Basic tips for authors [ 10,11]  

• Read through your paper and highlight key terms or phrases that are most relevant and frequently used in your field, to ensure familiarity.  
• Several journals provide instructions about the length (eg, 3 words in a keyword) and maximum number of keywords allowed and other related rules. Create a list of keywords based on these instructions and include specific phrases containing 2 to 4 words. A longer string of words would yield generic results irrelevant to your field.  
• Use abbreviations, acronyms, and initializations if these would be more familiar.  
• Search with your keywords to ensure the results fit with your article and assess how helpful they would be to readers.  
• Narrow down your keywords to about five to ten, to ensure accuracy.  
• Finalize your list based on the maximum number allowed.  

  Few examples: [ 12]  

Important Tips for Writing an Abstract  

Here are a few tips on how to write an abstract to ensure that your abstract is complete, concise, and accurate. [ 1,2]  

• Write the abstract last.  
• Follow journal-specific formatting guidelines or Instructions to Authors strictly to ensure acceptance for publication.  
• Proofread the final draft meticulously to avoid grammatical or typographical errors.  
• Ensure that the terms or data mentioned in the abstract are consistent with the main text.  
• Include appropriate keywords at the end.

Do not include:  

• New information  
• Text citations to references  
• Citations to tables and figures  
• Generic statements  
• Abbreviations unless necessary, like a trial or study name  

Key Takeaways    

Here’s a quick snapshot of all the important aspects of how to write an abstract . [2]

• An abstract in research is a summary of the paper and describes only the main aspects. Typically, abstracts are about 200-350 words long.  
• Abstracts are of four types—structured, unstructured, descriptive, and informative.  
• Abstracts should be simple, clear, concise, independent, and unbiased (present both favorable and adverse outcomes).  
• They should adhere to the prescribed journal format, including word limits, section headings, number of keywords, fonts used, etc.  
• The terminology should be consistent with the main text.   
• Although the section heading names may differ for journals, every abstract should include a background and objective, analysis methods, primary results, and conclusions.  
• Nonstandard abbreviations, references, and URLs shouldn’t be included.  
• Only relevant and specific keywords should be used to ensure focused searches and higher citation frequency.  
• Abstracts should be written last after completing the main paper.  

Frequently Asked Questions   

Q1. Do all journals have different guidelines for abstracts?  

A1. Yes, all journals have their own specific guidelines for writing abstracts; a few examples are given in the following table. [ 6,13,14,15]  

Q2. What are the common mistakes to avoid when writing an abstract?  

A2. Listed below are a few mistakes that authors may make inadvertently while writing abstracts.  

• Copying sentences from the paper verbatim  

An abstract is a summary, which should be created by paraphrasing your own work or writing in your own words. Extracting sentences from every section and combining them into one paragraph cannot be considered summarizing.  

• Not adhering to the formatting guidelines  

Journals have special instructions for writing abstracts, such as word limits and section headings. These should be followed strictly to avoid rejections.  

• Not including the right amount of details in every section  

Both too little and too much information could discourage readers. For instance, if the Background has very little information, the readers may not get sufficient context to appreciate your research. Similarly, incomplete information in the Methods and a text-heavy Results section without supporting numerical data may affect the credibility of your research.  

• Including citations, standard abbreviations, and detailed measurements  

Typically, abstracts shouldn’t include these elements—citations, URLs, and abbreviations. Only nonstandard abbreviations are allowed or those that would be more familiar to readers than the expansions.  

• Including new information  

Abstracts should strictly include only the same information mentioned in the main text. Any new information should first be added to the text and then to the abstract only if necessary or if permitted by the word limit.  

• Not including keywords  

Keywords are essential for indexing and searching and should be included to increase the frequency of retrieval and citation.  

Q3. What is the difference between abstracts in research papers and conference abstracts? [16]  

A3. The table summarizes the main differences between research and conference abstracts.  

  Thus, abstracts are essential “trailers” that can market your research to a wide audience. The better and more complete the abstract the more are the chances of your paper being read and cited. By following our checklist and ensuring that all key elements are included, you can create a well-structured abstract that summarizes your paper accurately.  

References  

• Andrade C. How to write a good abstract for a scientific paper or conference presentation. Indian J Psychiatry . 2011; 53(2):172-175. Accessed June 14, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136027/  
• Tullu MS. Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key. 2019; 13(Suppl 1): S12-S17. Accessed June 14, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398294/  
• Zawia J. Writing an Academic Paper? Get to know Abstracts vs. Structured Abstracts. Medium. Published October 16, 2023. Accessed June 16, 2024. https://medium.com/@jamala.zawia/writing-an-academic-paper-get-to-know-abstracts-vs-structured-abstracts-11ed86888367  
• Markel M and Selber S. Technical Communication, 12 th edition. 2018; pp. 482. Bedford/St Martin’s.  
• Abstracts. Arkansas State University. Accessed June 17, 2024. https://www.astate.edu/a/global-initiatives/online/a-state-online-services/online-writing-center/resources/How%20to%20Write%20an%20Abstract1.pdf  
• AMA Manual of Style. 11 th edition. Oxford University Press.  
• Writing an Abstract. The University of Melbourne. Accessed June 16, 2024. https://services.unimelb.edu.au/__data/assets/pdf_file/0007/471274/Writing_an_Abstract_Update_051112.pdf  
• 10 Good Abstract Examples that will Kickstart Your Brain. Kibin Essay Writing Blog. Published April 5, 2017. Accessed June 17, 2024. https://www.kibin.com/essay-writing-blog/10-good-abstract-examples/  
• A 10-step guide to make your research paper abstract more effective. Editage Insights. Published October 16, 2013. Accessed June 17, 2024. https://www.editage.com/insights/a-10-step-guide-to-make-your-research-paper-abstract-more-effective  
• Using keywords to write your title and abstract. Taylor & Francis Author Services. Accessed June 15, 2024. https://authorservices.taylorandfrancis.com/publishing-your-research/writing-your-paper/using-keywords-to-write-title-and-abstract/  
• How to choose and use keywords in research papers. Paperpal by Editage blog. Published March 10, 2023. Accessed June 17, 2024. https://paperpal.com/blog/researcher-resources/phd-pointers/how-to-choose-and-use-keywords-in-research-papers  
• Title, abstract and keywords. Springer. Accessed June 16, 2024. https://www.springer.com/it/authors-editors/authorandreviewertutorials/writing-a-journal-manuscript/title-abstract-and-keywords/10285522  
• Abstract and keywords guide. APA Style, 7 th edition. Accessed June 18, 2024. https://apastyle.apa.org/instructional-aids/abstract-keywords-guide.pdf  
• Abstract guidelines. American Society for Microbiology. Accessed June 18, 2024. https://asm.org/events/asm-microbe/present/abstract-guidelines  
• Guidelines for conference abstracts. The Lancet. Accessed June 16, 2024. https://www.thelancet.com/pb/assets/raw/Lancet/pdfs/Abstract_Guidelines_2013.pdf  
• Is a conference abstract the same as a paper abstract? Global Conference Alliance, Inc. Accessed June 18, 2024. https://globalconference.ca/is-a-conference-abstract-the-same-as-a-paper-abstract/  

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• 26 June 2024

How I’m using AI tools to help universities maximize research impacts

• Dashun Wang 0

Dashun Wang is a professor at the Kellogg School of Management and McCormick School of Engineering, and the founding director of the Center for Science of Science and Innovation at Northwestern University in Evanston, Illinois.

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From the Internet to CRISPR–Cas9 gene editing, many seeds of progress were planted initially in the ivory tower of academia. Could research be doing even more for society? I argue that it could — if universities used artificial intelligence (AI) tools to maximize the impact of their scientists’ outputs.

Each year, millions of grant proposals, preprints and research papers are produced, along with patents, clinical trials and drug approvals. Massive data sets storing details of these outputs can be scoured by AI algorithms to better understand how science and technology progress and to identify gaps and bottlenecks that hinder breakthroughs. Over the past few years, my colleague and close collaborator Ben Jones, my team and I have been working with large US universities to maximize their research impacts. We’ve already learnt a lot.

Revealed: the ten research papers that policy documents cite most

For example, during our pilot project at Northwestern University in Evanston, Illinois, we worked with one of its researchers in biology. She has published hundreds of papers and acquired tens of millions of dollars in research funding. By tracing her papers and grants and how her research has been used, we discovered an intriguing fact.

The researcher had never engaged with the university’s technology transfer office (TTO), yet her research had been used extensively by private companies worldwide — many of their patents cited her work. My collaborator Alicia Löffler, then head of the TTO, talked to the researcher. It turned out that she was unaware of those market impacts. Within one week of that conversation, the researcher filed her first invention disclosure with the university.

This episode raised several questions. How many scientists are in similar positions? Can researchers with untapped innovation potential be identified? And can the obstacles that hinder technological progress be addressed? To find out, Ben, Alicia and I, and our team, have expanded studies to other universities. Our preliminary work suggests that people in such positions are common.

Has your research influenced policy? Use this free tool to check

For one, the researcher is a woman. When we compared how often male and female faculty members patented their work, we found a disparity. Male faculty members typically patented their research two to ten times more often than did their female counterparts, although this rate varied by university and discipline. But when we measured the extent to which the two groups’ scientific publications were cited by patents, we found no statistically significant difference. In other words, female scientists’ work is just as close to the technological frontier.

Numerous factors can contribute to this gender gap , such as unequal access to education and mentorship, funding disparities, prevailing norms and stereotypes and structural barriers in patenting and commercialization processes. A better understanding of these challenges would help to broaden the pool of innovators.

Similarly, we see a large difference between tenure-track and tenured faculty members: tenured researchers patent their work at a higher rate. But one doesn’t magically become more innovative the moment tenure is granted. The causes of this gap are probably distinct from those of the gender one, and might include promotion incentives and what counts towards tenure. But both discrepancies point to untapped opportunities for innovation.

Want to speed up scientific progress? First understand how science policy works

Thus, data and AI tools can help institutions to identify people and ideas that are overlooked, both in a research institution and globally. But universities must take care. They have many roles and responsibilities — from educating future leaders to advancing fundamental knowledge — that must not be eclipsed by efforts to promote practical applications. Some people might argue that scientists don’t need to commercialize their ideas themselves, because industry can pick up the ball. Or there might be unintended consequences. Emphasizing what is useful could come at the expense of curiosity-driven research or result in flocking to what seem to be the hottest and most fruitful ideas today rather than to those that will help the world most in future.

But the role of science is changing. Many of today’s issues, from pandemics to climate change, are closely linked with scientific progress. The dichotomy of basic versus applied research is becoming inadequate. For example, advances along the science–society interface, such as discoveries that aid marketable applications ( M. Ahmadpoor and B. F. Jones Science 357 , 583–587; 2017 ) or social-science insights that guide policymaking ( Y. Yin et al. Nature Hum. Behav. 6 , 1344–1350; 2022 ), are highly impactful, as evidenced by high citation rates. By engaging more with use-inspired research, scientists can produce insights that both advance basic understanding and address societal needs.

Encouraging developments are under way. In 2022, the US National Science Foundation created the Directorate for Technology, Innovation and Partnerships to support use-inspired research and translate discoveries into real-world applications. Its Assessing and Predicting Technology Outcomes programme will fund innovative projects — including our work, which we plan to expand to more than 20 universities — to understand how investments in science and technology can best accelerate progress. Other nations, university leaders and policymakers must seize this opportunity, too. I think of science as ‘the little engine that could’. If research and development could be made even 5% more efficient, the returns could be immense.

Nature 630 , 794 (2024)

doi: https://doi.org/10.1038/d41586-024-02081-6

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Competing Interests

D.W. receives consulting fees from one of the universities he works with.

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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

• Read it carefully, looking for anything confusing you might need to clarify with your professor.
• Identify the assignment goal, deadline, length specifications, formatting, and submission method.
• Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

• A paper following the chronology of World War II would not be original or specific enough.
• A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

• Is there anything people seem to overlook in the sources you research?
• Are there any heated debates you can address?
• Do you have a unique take on your topic?
• Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

• Maintaining forward momentum — write now, perfect later.
• Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
• Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

• topic sentences against the thesis statement;
• topic sentences against each other, for similarities and logical ordering;
• and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

• Offer new arguments or essential information
• Take up any more space than necessary
• Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

• Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
• Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
• Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
• If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

• Confirm that your paper completes every task specified in your assignment sheet.
• Check for logical organization and flow of paragraphs.
• Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

• each sentence helps support the topic sentence.
• no unnecessary or irrelevant information is present.
• all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

Scribbr’s professional editors can help with the revision process with our award-winning proofreading services.

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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Researchers Build AI That Builds AI

January 25, 2022

Olivia Fields for Quanta Magazine

Introduction

Artificial intelligence is largely a numbers game. When deep neural networks, a form of AI that learns to discern patterns in data, began surpassing traditional algorithms 10 years ago, it was because we finally had enough data and processing power to make full use of them.

Today’s neural networks are even hungrier for data and power. Training them requires carefully tuning the values of millions or even billions of parameters that characterize these networks, representing the strengths of the connections between artificial neurons. The goal is to find nearly ideal values for them, a process known as optimization, but training the networks to reach this point isn’t easy. “Training could take days, weeks or even months,” said Petar Veličković , a staff research scientist at DeepMind in London.

That may soon change. Boris Knyazev of the University of Guelph in Ontario and his colleagues have designed and trained a “hypernetwork” — a kind of overlord of other neural networks — that could speed up the training process. Given a new, untrained deep neural network designed for some task, the hypernetwork predicts the parameters for the new network in fractions of a second, and in theory could make training unnecessary. Because the hypernetwork learns the extremely complex patterns in the designs of deep neural networks, the work may also have deeper theoretical implications.

For now, the hypernetwork performs surprisingly well in certain settings, but there’s still room for it to grow — which is only natural given the magnitude of the problem. If they can solve it, “this will be pretty impactful across the board for machine learning,” said Veličković.

Getting Hyper

Currently, the best methods for training and optimizing deep neural networks are variations of a technique called stochastic gradient descent (SGD). Training involves minimizing the errors the network makes on a given task, such as image recognition. An SGD algorithm churns through lots of labeled data to adjust the network’s parameters and reduce the errors, or loss. Gradient descent is the iterative process of climbing down from high values of the loss function to some minimum value, which represents good enough (or sometimes even the best possible) parameter values.

But this technique only works once you have a network to optimize. To build the initial neural network, typically made up of multiple layers of artificial neurons that lead from an input to an output, engineers must rely on intuitions and rules of thumb. These architectures can vary in terms of the number of layers of neurons, the number of neurons per layer, and so on.

One can, in theory, start with lots of architectures, then optimize each one and pick the best. “But training [takes] a pretty nontrivial amount of time,” said Mengye Ren , now a visiting researcher at Google Brain. It’d be impossible to train and test every candidate network architecture. “[It doesn’t] scale very well, especially if you consider millions of possible designs.”

So in 2018, Ren, along with his former University of Toronto colleague Chris Zhang and their adviser Raquel Urtasun, tried a different approach . They designed what they called a graph hypernetwork (GHN) to find the best deep neural network architecture to solve some task, given a set of candidate architectures.

The name outlines their approach. “Graph” refers to the idea that the architecture of a deep neural network can be thought of as a mathematical graph — a collection of points, or nodes, connected by lines, or edges. Here the nodes represent computational units (usually, an entire layer of a neural network), and edges represent the way these units are interconnected.

Here’s how it works. A graph hypernetwork starts with any architecture that needs optimizing (let’s call it the candidate). It then does its best to predict the ideal parameters for the candidate. The team then sets the parameters of an actual neural network to the predicted values and tests it on a given task. Ren’s team showed that this method could be used to rank candidate architectures and select the top performer.

When Knyazev and his colleagues came upon the graph hypernetwork idea, they realized they could build upon it. In their new paper , the team shows how to use GHNs not just to find the best architecture from some set of samples, but also to predict the parameters for the best network such that it performs well in an absolute sense. And in situations where the best is not good enough, the network can be trained further using gradient descent.

“It’s a very solid paper. [It] contains a lot more experimentation than what we did,” Ren said of the new work. “They work very hard on pushing up the absolute performance, which is great to see.”

Training the Trainer

Knyazev and his team call their hypernetwork GHN-2, and it improves upon two important aspects of the graph hypernetwork built by Ren and colleagues.

First, they relied on Ren’s technique of depicting the architecture of a neural network as a graph. Each node in the graph encodes information about a subset of neurons that do some specific type of computation. The edges of the graph depict how information flows from node to node, from input to output.

The second idea they drew on was the method of training the hypernetwork to make predictions for new candidate architectures. This requires two other neural networks. The first enables computations on the original candidate graph, resulting in updates to information associated with each node, and the second takes the updated nodes as input and predicts the parameters for the corresponding computational units of the candidate neural network. These two networks also have their own parameters, which must be optimized before the hypernetwork can correctly predict parameter values.

To do this, you need training data — in this case, a random sample of possible artificial neural network (ANN) architectures. For each architecture in the sample, you start with a graph, and then you use the graph hypernetwork to predict parameters and initialize the candidate ANN with the predicted parameters. The ANN then carries out some specific task, such as recognizing an image. You calculate the loss made by the ANN and then — instead of updating the parameters of the ANN to make a better prediction — you update the parameters of the hypernetwork that made the prediction in the first place. This enables the hypernetwork to do better the next time around. Now, iterate over every image in some labeled training data set of images and every ANN in the random sample of architectures, reducing the loss at each step, until it can do no better. At some point, you end up with a trained hypernetwork.

Knyazev’s team took these ideas and wrote their own software from scratch, since Ren’s team didn’t publicize their source code. Then Knyazev and colleagues improved upon it. For starters, they identified 15 types of nodes that can be mixed and matched to construct almost any modern deep neural network. They also made several advances to improve the prediction accuracy.

Most significantly, to ensure that GHN-2 learns to predict parameters for a wide range of target neural network architectures, Knyazev and colleagues created a unique data set of 1 million possible architectures. “To train our model, we created random architectures [that are] as diverse as possible,” said Knyazev.

As a result, GHN-2’s predictive prowess is more likely to generalize well to unseen target architectures. “They can, for example, account for all the typical state-of-the-art architectures that people use,” said Thomas Kipf , a research scientist at Google Research’s Brain Team in Amsterdam. “That is one big contribution.”

Impressive Results

The real test, of course, was in putting GHN-2 to work. Once Knyazev and his team trained it to predict parameters for a given task, such as classifying images in a particular data set, they tested its ability to predict parameters for any random candidate architecture. This new candidate could have similar properties to the million architectures in the training data set, or it could be different — somewhat of an outlier. In the former case, the target architecture is said to be in distribution; in the latter, it’s out of distribution. Deep neural networks often fail when making predictions for the latter, so testing GHN-2 on such data was important.

Armed with a fully trained GHN-2, the team predicted parameters for 500 previously unseen random target network architectures. Then these 500 networks, their parameters set to the predicted values, were pitted against the same networks trained using stochastic gradient descent. The new hypernetwork often held its own against thousands of iterations of SGD, and at times did even better, though some results were more mixed.

For a data set of images known as CIFAR-10, GHN-2’s average accuracy on in-distribution architectures was 66.9%, which approached the 69.2% average accuracy achieved by networks trained using 2,500 iterations of SGD. For out-of-distribution architectures, GHN-2 did surprisingly well, achieving about 60% accuracy. In particular, it achieved a respectable 58.6% accuracy for a specific well-known deep neural network architecture called ResNet-50. “Generalization to ResNet-50 is surprisingly good, given that ResNet-50 is about 20 times larger than our average training architecture,” said Knyazev, speaking at NeurIPS 2021 , the field’s flagship meeting.

GHN-2 didn’t fare quite as well with ImageNet, a considerably larger data set: On average, it was only about 27.2% accurate. Still, this compares favorably with the average accuracy of 25.6% for the same networks trained using 5,000 steps of SGD. (Of course, if you continue using SGD, you can eventually — at considerable cost — end up with 95% accuracy.) Most crucially, GHN-2 made its ImageNet predictions in less than a second, whereas using SGD to obtain the same performance as the predicted parameters took, on average, 10,000 times longer on their graphical processing unit (the current workhorse of deep neural network training).

“The results are definitely super impressive,” Veličković said. “They basically cut down the energy costs significantly.”

And when GHN-2 finds the best neural network for a task from a sampling of architectures, and that best option is not good enough, at least the winner is now partially trained and can be optimized further. Instead of unleashing SGD on a network initialized with random values for its parameters, one can use GHN-2’s predictions as the starting point. “Essentially we imitate pre-training,” said Knyazev.

Beyond GHN-2

Despite these successes, Knyazev thinks the machine learning community will at first resist using graph hypernetworks. He likens it to the resistance faced by deep neural networks before 2012. Back then, machine learning practitioners preferred hand-designed algorithms rather than the mysterious deep nets. But that changed when massive deep nets trained on huge amounts of data began outperforming traditional algorithms. “This can go the same way.”

In the meantime, Knyazev sees lots of opportunities for improvement. For instance, GHN-2 can only be trained to predict parameters to solve a given task, such as classifying either CIFAR-10 or ImageNet images, but not at the same time. In the future, he imagines training graph hypernetworks on a greater diversity of architectures and on different types of tasks (image recognition, speech recognition and natural language processing, for instance). Then the prediction can be conditioned on both the target architecture and the specific task at hand.

And if these hypernetworks do take off, the design and development of novel deep neural networks will no longer be restricted to companies with deep pockets and access to big data. Anyone could get in on the act. Knyazev is well aware of this potential to “democratize deep learning,” calling it a long-term vision.

However, Veličković highlights a potentially big problem if hypernetworks like GHN-2 ever do become the standard method for optimizing neural networks. With graph hypernetworks, he said, “you have a neural network — essentially a black box — predicting the parameters of another neural network. So when it makes a mistake, you have no way of explaining [it].”

Of course, this is already largely the case for neural networks. “I wouldn’t call it a weakness,” said Veličković. “I would call it a warning sign.”

Kipf, however sees a silver lining. “Something [else] got me most excited about it.” GHN-2 showcases the ability of graph neural networks to find patterns in complicated data.

Normally, deep neural networks find patterns in images or text or audio signals, which are fairly structured types of information. GHN-2 finds patterns in the graphs of completely random neural network architectures. “That’s very complicated data.”

And yet, GHN-2 can generalize — meaning it can make reasonable predictions of parameters for unseen and even out-of-distribution network architectures. “This work shows us a lot of patterns are somehow similar in different architectures, and a model can learn how to transfer knowledge from one architecture to a different one,” said Kipf. “That’s something that could inspire some new theory for neural networks.”

If that’s the case, it could lead to a new, greater understanding of those black boxes.

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• LLMs now write lots of science. Good

Easier and more lucid writing will make science faster and better

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M ANY PEople are busily experimenting with chatbots in the hope that generative artificial intelligence ( AI ) can improve their daily lives. Scientists, brainy as they are, are several steps ahead. As we report , 10% or more of abstracts for papers in scientific journals now appear to be written at least in part by large language models. In fields such as computer science that figure rises to 20%. Among Chinese computer scientists, it is a third.

Some see this enthusiastic adoption as a mistake. They fear that vast quantities of poor-quality papers will introduce biases, boost plagiarism and jam the machinery of scientific publication. Some journals, including the Science family, are imposing onerous disclosure requirements on the use of llm s. Such attempts are futile and misguided. llm s cannot easily be policed. Even if they could be, many scientists find that their use brings real benefits.

Research scientists are not just devoted to laboratory work or thinking big thoughts. They face great demands on their time, from writing papers and teaching to filling out endless grant applications. llm s help by speeding up the writing of papers, thereby freeing up time for scientists to develop new ideas, collaborate or check for mistakes in their work.

The technology can also help level a playing-field that is tilted towards native English speakers, because many of the prestigious journals are in their tongue. llm s can help those who do not speak the language well to translate and edit their text. Thanks to LLM s, scientists everywhere should be able to disseminate their findings more easily, and be judged by the brilliance of their ideas and ingeniousness of their research, rather than their skill in avoiding dangling modifiers.

As with any technology, there are worries. Because llm s make it easier to produce professional-sounding text, they will make it easier to generate bogus scientific papers. Science received 10,444 submissions last year, of which 83% were rejected before peer review. Some of these are bound to have been ai -generated fantasies.

llm s could also export, through their words, the cultural environment in which they were trained. Their lack of imagination may spur inadvertent plagiarism, in which they directly copy past work by humans. “Hallucinations” that are obviously wrong to experts, but very believable to everyone else, could also make their way into the text. And most worrying of all, writing can be an integral part of the research process, by helping researchers clarify and formulate their own ideas. An excessive reliance on llm s could therefore make science poorer.

Trying to restrict the use of LLM s is not the way to deal with these problems. In the future they are rapidly going to become more prevalent and more powerful. They are already embedded in word processors and other software, and will soon be as common as spell-checkers. Researchers tell surveys that they see the benefits of generative ai not just for writing papers but for coding and doing administrative tasks. And crucially, their use cannot easily be detected. Although journals can impose all the burdensome disclosure requirements they like, it would not help, because they cannot tell when their rules have been broken. Journals such as Science should abandon detailed disclosures for the use of llm s as a writing tool, beyond a simple acknowledgment.

Science already has many defences against fabrication and plagiarism. In a world where the cost of producing words falls to nothing, these must become stronger still. Peer review, for instance, will become even more important in a gen- ai world. It must be beefed up accordingly, perhaps by paying reviewers for the time they sacrifice to scrutinise papers. There should also be more incentives for researchers to replicate experiments. Hiring and promotion committees at universities should ensure that scientists are rewarded based on the quality of their work and the quantity of new insights they generate. Curb the potential for misuse, and scientists have plenty to gain from their llm amanuenses. ■

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This article appeared in the Leaders section of the print edition under the headline “Can you make this clearer?”

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