how to write a research paper with 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.

Loved by over 1 million academics

Academia's Trusted Companion

Join our academic community and elevate your research journey alongside fellow scholars with Jenni AI.

Effortlessly Ignite Your Research Ideas

Unlock your potential with these standout features

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.

Get started

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.

How it Works

Sign up for free.

To get started, sign up for a free account on Jenni AI's platform.

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.

What Our Users Say

Discover how Jenni AI has made a difference in the lives of academics just like you

· Aug 26

I thought AI writing was useless. Then I found Jenni AI, the AI-powered assistant for academic writing. It turned out to be much more advanced than I ever could have imagined. Jenni AI = ChatGPT x 10.

Charlie Cuddy

@sonofgorkhali

· 23 Aug

Love this use of AI to assist with, not replace, writing! Keep crushing it @Davidjpark96 💪

Waqar Younas, PhD

@waqaryofficial

· 6 Apr

4/9 Jenni AI's Outline Builder is a game-changer for organizing your thoughts and structuring your content. Create detailed outlines effortlessly, ensuring your writing is clear and coherent. #OutlineBuilder #WritingTools #JenniAI

I started with Jenni-who & Jenni-what. But now I can't write without Jenni. I love Jenni AI and am amazed to see how far Jenni has come. Kudos to http://Jenni.AI team.

· 28 Jul

Jenni is perfect for writing research docs, SOPs, study projects presentations 👌🏽

Stéphane Prud'homme

http://jenni.ai is awesome and super useful! thanks to @Davidjpark96 and @whoisjenniai fyi @Phd_jeu @DoctoralStories @WriteThatPhD

Frequently asked questions

How much does jenni ai cost, how can jenni ai assist me in writing complex academic papers, can jenni ai handle different types of academic papers, such as essays, research papers, and dissertationss jenni ai maintain the originality of my work, how does artificial intelligence enhance my academic writing with jenni ai.

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

Will using Jenni AI improve my overall writing skills?

Can Jenni AI assist with crafting a thesis statement?

What sets Jenni AI apart as an AI-powered writing tool?

Can I trust Jenni AI to help me maintain academic integrity in my work?

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.

Feature Featire

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.

Ready to Revolutionize Your Research Writing?

Sign up for a free Jenni AI account today. Unlock your research potential and experience the difference for yourself. Your journey to academic excellence starts here.

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

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

Make no mistake, AI is here to stay!

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

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

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

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

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

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

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

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

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

Here they are, below:

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

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

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

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

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

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

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

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

AI-powered research tools and AI for academic research

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

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

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

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

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

AI for reading peer-reviewed papers easily

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

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

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

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

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

AI for scientific writing and research papers

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

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

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

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

AI academic editing tools

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

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

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

AI tools for grant writing

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

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

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

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

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

Best free AI research tools

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

The best free ones at time of writing are:

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

Wrapping up

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

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

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

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

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

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

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

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

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

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

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

Thank you for visiting Academia Insider.

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

2024 © Academia Insider

View the latest institution tables

View the latest country/territory tables

AI writing tools promise faster manuscripts for researchers

Automation brings plagiarism risks, and software still needs human input for analysis and narrative.

Credit: gmast3r/Getty Images

17 August 2021

gmast3r/Getty Images

Writing tools powered by artificial intelligence (AI) have the potential to reduce manuscript preparation time to a few days, or hours. Deep-learning technologies that run chatbots, spellchecks and auto-generated tweets are being used in a growing number of products pitched at students and academics.

Grammarly, for example, claims to “inspect your writing carefully to improve clarity, word choice and more”, offering free and fee-based services.

But are these tools up to the task? In an experiment through education information site EduRef, a group of recent graduates, undergraduates and self-described undergraduate-level writers were given the same assignments as GPT-3, an AI language program developed by OpenAI, a research company co-founded by Elon Musk.

The assignments were evaluated by instructors who did not know who (or what) had written them. GPT-3 performed in line with the humans, according to EduRef, and received “more or less the same feedback”.

The program was praised for writing excellent openings and transitions, but was criticised for using vague, blunt and awkward language, and for failing to craft a strong narrative. It wrote shallow, less descriptive papers than the students, according to EduRef’s write-up on its website, but it took between three and 20 minutes to write a paper, compared to three days for the students.

Hilde van Zeeland is chief applied linguist at start-up company Writefull, which offers AI-based language editing, and is part of London-based Digital Science (see disclosure, below). She says AI tools are already powerful enough to improve a writer’s sentence flow and structure, and will continue to improve.

The company recently analysed more than 250,000 abstracts to identify the most commonly used phrases in each of four different parts of the abstract.

They found, for example, that the words ‘aim of this study’ occurred most frequently in part 1 of the abstract (where study aim and background are described) and the phrase ‘95% confidence interval’ occurred most often in part 4 (which deals with meaning of results, contribution, future research). Users can choose phrases and connecting sentences to use in their own papers.

“With this database of phrases, when a user struggles with the right word to use for each section, our software will be able to provide good alternatives,” says van Zeeland.

The website tells users not to worry about plagiarism, reassuring them that certain phrases won’t be flagged by a plagiarism checker because they are short and commonly used.

Writefull is running a separate experiment that involves feeding an abstract into an AI tool, which generates a paper title based on the input. This function can enhance title readability, draw readership to the abstract and make the article more visible to search engines, according to van Zeeland.

AI tools can do more than check a writer’s grammar and spelling and suggest frequently used phrases, says computer scientist, Guillaume Cabanac, from the University of Toulouse in France.

An analysis of journal-published computer-science papers by Cabanac and his colleagues, posted as a preprint on arXiv in July and yet to be peer-reviewed, found swathes of articles containing tortured phrases and nonsensical text. For example, the phrase ‘colossal information’ was used instead of ‘big data’.

The authors suspect that these papers were the result of using automated translations or software that rewrites existing text to disguise plagiarism, according to a report in Nature .

Their study prompted warnings from research integrity experts that a new type of fabricated paper was entering the scientific literature, which will be hard to spot as AI tools learn to use more sophisticated language.

Considerations for using AI writing tools in academic work

Shu Chian Tay, a science communicator at the National University of Singapore , fears that AI writing tools could exacerbate inequality between labs with different access to resources or openness to trying new technologies.

Shu Chian Tay

“Consider a hypothetical situation where AI science-writing is possible and two labs have made the same research breakthrough at the same time,” says Tay.

“One lab stuck to human writing, which took weeks, while the other made use of AI writing tools. The latter, who is likely to publish earlier would get most, if not all, of the scientific recognition and lucrative intellectual property rights.”

Will AI replace human writing?

Tay notes that although AI writing tools are quite powerful, they have not yet reached a point where they can write a scientific manuscript from scratch. “We still need researchers to analyse data, present them as figures, and develop a coherent story before feeding all this information to the AI machine,” she says.

Ryan Morrison, professor of English as a second language at George Brown College in Toronto, Canada, says that although the adoption of AI in academic writing is inevitable, it is unlikely to replace human writing in the near future.

Ryan Morrison

“Just as super-computer calculators can process large sets of numbers fast, AI writing tools outperform humans in completing tasks like spell checks and text generation at a much faster rate,” says Morrison. “But the creativity of AI is ultimately limited by the input materials and the discretion of the human curator.”

Software such as Turnitin is also incorporating AI technology to enhance its ability to detect plagiarism. This could potentially be used to identify documents written using AI tools.

Be transparent about using AI writing tools in academic work

Michael Mindzak, assistant professor in the department of educational studies at Brock University in Ontario, Canada, cautions that the academic community has not yet agreed on how to manage potential problems related to AI writing tools, such as plagiarism and authorship credits.

Michael Mindzak

Most institutions are yet to formulate policies on the use of these tools by students and staff. This can be problematic when committee members making decisions on tenure and promotion have different views about the appropriateness of using them, says Mindzak.

“My advice is to be transparent,” says Mindzak. “Declare and add a disclaimer if you have used AI-assisted writing. As more people in the community do this, it may become a norm, just like how the ‘conflict of interest’ section has been added to academic papers in response to a rise in company-funded research and start-ups from academic labs.”

*Digital Science is a subsidiary of Holtzbrinck Publishing Group, which owns 53% of Springer Nature, publisher of the Nature Index. Nature Index is editorially independent of its publisher.

November 21, 2023

Share this page

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.

Products featured in this article

Microsoft Edge

More articles

How to keep your flow and focus with Microsoft Edge

Increase productivity and focus on the task at hand with built-in Microsoft Edge features.

How to stay organized with Microsoft Edge

Learn about built-in features to keep your browser organized and efficient.

Why Microsoft Edge is the best browser for students

Microsoft Edge is the best browser for students thanks to its helpful features and add-ons.

• USC Libraries
• Research Guides

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
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• 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 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.

• << Previous: Further Readings
• Next: Acknowledgments >>
• Last Updated: Apr 29, 2024 1:49 PM
• URL: https://libguides.usc.edu/writingguide

Generative Artificial Intelligence

• What is Generative AI?
• When to Use AI for Research
• How to Use AI for Research
• Evaluating Generative AI Tools
• Citing Generative AI Tools
• Examining an AI Chatbot Hallucination

Accuracy of AI

This image was created with Dall-e 2  using the prompt: "a robot hallucinating"

Generative AI is it is only as good as the sources it pulls from and it can hallucinate information.

Treat your results from AI with caution.

Using AI for Research

To use AI effectively for research one of the first things you should ask is what are you trying to accomplish and be intentional about the type of tool you use. You may even want to consider if AI is the best sort of tool for what you are looking for. 

There are lots of different AI tools. See [different page for a list of different AI research assistants and what they do].

Prompt Engineering for a Research Question

This section will focus on writing prompts for chat based AI tools like ChatGPT, GoogleBard, or Microsoft Bing's chatbot. 

Key Terminology

Prompt Engineering   is the intentional design of prompts given to artificial intelligence tools so that the response it gives you are helpful.

Prompt Patterns  are different structures you can use to engineer AI prompts

Prompt Pattern: Persona

How to create the prompt.

• Act as a certain persona (e.g. "imagine you are an anthropologist"
• You can give additional context, such as what the persona is trying to accomplish

Here's an example of how to apply this in your research

Prompt: Imagine you are an anthropologist. What are keywords and terms you would likely use if you were writing a paper about the formation of social hierarchies in societies.

This prompt will give a list of different search terms you can try to find relevant papers on your topic

See how ChatGPT responded to a persona pattern prompt: https://chat.openai.com/share/8fa6b79a-0a30-4775-bc3f-96f7f8304c8d   Compare this to a similar question but asked with the persona being a not specific about the persona who is doing the research: https://chat.openai.com/share/4917eed8-75fc-4e89-81a4-9db328639af1

Prompt Pattern: Simulated Interaction

Instead of asking ChatGPT questions, have it ask questions to you

• Explain to ChatGPT what the interaction will look like (I will prompt you, then I want you to ask me a question based on that prompt).
• You can do this as one-off questions or more in-depth.

Prompt: I want to write a psychology paper about memory but I am not sure what direction to go with my specific topic. Help me come up with a starting research question by asking me questions about what aspects of memory I am interested in. When you have enough information to write a research topic, stop asking questions and write the research question. Ask me one question at a time and wait for my response.

This prompt will make ChatGPT act as a conversation partner to help you think through decisions about narrowing down your research topic.

See how ChatGPT responded to a simulated interaction prompt:  https://chat.openai.com/share/ffe72f48-799f-427e-b9dd-bce5ecea6996  

Prompt Pattern: Schedule Maker

Tell ChatGPT what you are trying to accomplish and ask it to create a schedule to keep you on task

Prompt: I have three hours tonight I can work on research for my paper on social media's impact on presidential elections. By the end of those three hours I want to have found two peer-reviewed sources and created an annotated bibliography of those sources. Can you create a schedule of what I should do between 7pm and 10 pm to accomplish those things on time?

This prompt will give you a schedule of what to work on during a set amount of time and also breaks down research into concrete steps

See how ChatGPT responded to a prompt to create a schedule; https://chat.openai.com/share/3b867579-d62a-4736-b0d7-de26284cd061

• << Previous: When to Use AI for Research
• Next: Evaluating Generative AI Tools >>
• Last Updated: Apr 5, 2024 10:58 AM
• URL: https://davidson.libguides.com/generative-ai

Mailing Address : Davidson College - E.H. Little Library, 209 Ridge Road, Box 5000, Davidson, NC 28035

• Interesting
• Scholarships
• UGC-CARE Journals

How to Use Google Bard AI to Write a Scientific Research Paper

Google Bard AI is a powerful tool that can be used to accelerate scientific research. This article will discuss how to use Google Bard AI to write a scientific research paper, including tips on brainstorming ideas, conducting literature reviews, writing your paper, and editing your paper.

As technology advances, the role of Artificial Intelligence (AI) in the research field is becoming more and more prominent. One of the latest developments in this regard is Google’s latest AI-powered language model, Google Bard. Google Bard is an AI tool that is designed to help researchers write scientific research papers more efficiently and effectively.

It uses advanced algorithms to analyze research data and generate a coherent research paper that follows the standard scientific writing style. In this article, ilovephd will discuss how to use Google Bard AI to write a scientific research paper .

Understanding the Basics of Google Bard AI:

Google Bard is an AI tool developed by Google that uses advanced Natural Language Processing (NLP) algorithms to analyze research data and generate a research paper.

It is a deep learning-based AI model that has been trained on a large corpus of scientific research papers to understand the structure, language, and tone of scientific writing.

Google Bard AI is designed to automate the tedious and time-consuming task of writing a scientific research paper by analyzing research data, identifying key findings, and presenting them in a coherent and understandable manner.

Steps to Use Google Bard AI to Write a Scientific Research Paper:

1. Gather Data and Create an Outline:

The first step in using Google Bard AI to write a scientific research paper is to gather all the necessary data and create an outline. The data should include all the relevant research materials, including research articles, scientific papers, and other relevant sources.

Once you have gathered all the necessary data, create an outline that includes the main sections of your paper, such as the introduction, methodology, results, discussion, and conclusion. The outline should also include the key points you want to make in each section.

2. Input Data into Google Bard AI:

The next step is to input the data into Google Bard AI. To do this, go to the Google Bard website and sign in with your Google account.

Once you have logged in, click on the “Create New Document” button and select the option to create a scientific research paper. This will open a new document where you can input your research data.

3. Customize the Settings:

Before you start writing, you can customize the settings of Google Bard AI according to your preferences. For example, you can choose the language, tone, and style of the writing. You can also set the level of complexity and the length of the paper.

4. Start Writing:

Once you have customized the settings, you can start writing. Google Bard AI will analyze your research data and start generating a research paper. You can write your paper in sections, or you can write it all at once. As you write, Google Bard AI will suggest changes and improvements to your writing. You can accept or reject these suggestions according to your preferences.

5. Edit and Proofread:

Once you have finished writing, you should edit and proofread your paper carefully. Google Bard AI is not perfect, and there may be errors or inconsistencies in the writing. You should check the grammar, spelling, and punctuation carefully and make any necessary corrections.

6. Submit your Paper:

Once you are satisfied with your paper, you can submit it for review. You can download your paper as a Word document or a PDF file and submit it to a scientific journal or conference.

The Benefits and Limitations of Using Google Bard AI for Scientific Research

Google Bard is a large language model, also known as a conversational AI or chatbot trained to be informative and comprehensive.

It is trained on a massive amount of text data and is able to communicate and generate human-like text in response to a wide range of prompts and questions.

For example, Google Bard can provide summaries of factual topics or create stories.

Benefits of Using Google Bard AI to Write a Scientific Research Paper:

• Saves Time:

One of the biggest benefits of using Google Bard AI to write a scientific research paper is that it saves time. Writing a research paper can be a tedious and time-consuming task, especially if you are not familiar with the scientific writing style. With Google Bard AI, you can automate the writing process and generate a research paper quickly and efficiently.

• Improves Writing Quality:

Another benefit of using Google Bard AI is that it improves the quality of your writing. Google Bard AI is

trained on a large corpus of scientific research papers, so it is familiar with the standard scientific writing style. This means that the AI can help you write more coherently and effectively by suggesting changes to your writing.

• Helps with Data Analysis:

Google Bard AI also helps with data analysis. The AI can analyze research data and identify key findings that can be included in your research paper. This saves time and effort in manually analyzing data and ensures that your research is comprehensive and accurate.

• Provides Suggestions and Improvements:

Google Bard AI provides suggestions and improvements to your writing. This can be helpful in improving the quality of your research paper and ensuring that it meets the standards of the scientific community. You can accept or reject these suggestions based on your preferences.

5. Customizable Settings:

Google Bard AI also has customizable settings that allow you to adjust the language, tone, and style of your writing. This allows you to tailor your writing to your audience and ensure that your research paper is effective in communicating your findings.

6. Accelerating the pace of research:  

Bard can be used to automate tasks that are typically time-consuming and labor-intensive, such as data collection, analysis, and writing. This can free up researchers to focus on more creative and strategic aspects of their work.

7. Improving the quality of research:  

Bard can be used to help researchers identify and correct errors in their work. It can also be used to provide researchers with access to a wider range of information, which can help them to develop more comprehensive and informed research proposals and findings.

8. Making research more accessible:  

Bard can be used to make scientific research more accessible to a wider range of people. This can be done by providing a platform for researchers to share their work with others and by making it easier for people to learn about scientific research.

Challenges of Using Google Bard AI to Write a Scientific Research Paper:

• Limited Vocabulary:

One of the challenges of using Google Bard AI is that it has a limited vocabulary. This means that the AI may not be able to understand or accurately represent complex scientific concepts or terminology. It is important to review the writing generated by the AI carefully and make any necessary corrections or additions.

• May Not Capture Your Writing Style:

Google Bard AI is designed to mimic the standard scientific writing style. However, it may not capture your individual writing style or tone. It is important to review the writing generated by the AI and make any necessary adjustments to ensure that it accurately represents your research and writing style.

3. Limited Control over the Writing Process:

When using Google Bard AI, you have limited control over the writing process. While the AI can generate a research paper quickly and efficiently, it may not always capture the nuances or complexities of your research. It is important to review the writing generated by the AI carefully and make any necessary corrections or additions.

4. Bard is still under development:  

Bard is still under development, so it is not always perfect. It can sometimes make mistakes, such as providing inaccurate or incomplete information. It is important for researchers to be aware of Bard’s limitations and to use it responsibly.

5. Bard is not a replacement for human researchers:  

Bard is a powerful tool, but it is not a replacement for human researchers. It is important for researchers to use Bard in conjunction with their own knowledge and expertise.

6. Bard can be biased:  

Bard is trained on a massive amount of text data, which may contain biases. It is important for researchers to be aware of these biases and to take steps to mitigate them.

Overall, Google Bard is a powerful tool that can be used to accelerate and improve the quality of scientific research. However, it is important to be aware of its limitations and to use it responsibly.

By analyzing research data and generating a coherent research paper, Google Bard AI saves time and improves the quality of writing. While there are some challenges to using Google Bard AI, such as limited vocabulary and control over the writing process, the benefits of using the AI far outweigh the challenges.

Researchers should consider using Google Bard AI to streamline the writing process and improve the quality of their research papers.

Also Read: How to Use ChatGpt to Write a Scientific Research Paper?

• brainstorming ideas
• conducting literature reviews
• editing your paper
• getting feedback
• Google Bard AI
• scientific research paper
• writing tips
• writing your paper

How does GPTZero Work? AI Detector for ChatGPT / Gemini / Copilot / Meta AI

How to effectively use meta ai for phd research, most asked phd viva-voce questions and answers, most popular, tips to prepare phd viva-voce presentation slides, birac call for proposals seeks innovative biotech products, 10 ai software tools to outlining a research paper, 480 ugc-care list of journals – science – 2024, ugc-care list of journals – arts and humanities – 2024, choosing a phd supervisor 9 key factors to consider, 10 ideas to get 10x more google scholar citations, best for you, 14 best free plagiarism checkers online – 2024, what is phd, popular posts, top scopus indexed computer science & engineering journals for fast publication – 2024, top scopus indexed journals in aviation and aerospace engineering, popular category.

• POSTDOC 317
• Interesting 259
• Journals 234
• Fellowship 127
• Research Methodology 102
• All Scopus Indexed Journals 91

iLovePhD is a research education website to know updated research-related information. It helps researchers to find top journals for publishing research articles and get an easy manual for research tools. The main aim of this website is to help Ph.D. scholars who are working in various domains to get more valuable ideas to carry out their research. Learn the current groundbreaking research activities around the world, love the process of getting a Ph.D.

Contact us: [email protected]

Google News

Copyright © 2024 iLovePhD. All rights reserved

• Artificial intelligence

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.

Share This Article:

9 Tips to Improve Your Job Application

7 Practical Solutions to Make AI Sound More Human: A Writer’s Guide

What’s a Semicolon? + When to Use It (With Examples)

Looking for fresh content, thank you your submission has been received.

How to Write and Review an Artificial Intelligence Paper

• First Online: 22 April 2022

Cite this chapter

• Thomas Weikert 5 &
• Tim Leiner 6  

Part of the book series: Contemporary Medical Imaging ((CMI))

1629 Accesses

The purpose of this chapter is to provide medical imaging professionals with the tools to write a research article in the field of artificial intelligence. At the same time, this can help readers to assess the quality of a publication. To this end, the chapter discusses 12 key considerations in detail, ranging from defining a research objective to public sharing of software code. Furthermore, a checklist of 25 items based on the standard structure of a research articles is derived from these considerations to provide writers and readers with an easily applicable tool.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Zheng X. TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2016.

Google Scholar  

Balki I, Amirabadi A, Levman J, Martel AL, Emersic Z, Meden B, Garcia-Pedrero A, Ramirez SC, Kong D, Moody AR, Tyrrell PN. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Canadian Associat Radiolog J. 2019;70(4):344–53). Canadian Medical Association. https://doi.org/10.1016/j.carj.2019.06.002 .

Article   Google Scholar  

Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, Halpern EF, Hess CP, Schiebler ML, Weiss CR. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers—from the radiology editorial board. Radiology. 2020;294(3):487–9. https://doi.org/10.1148/radiol.2019192515 .

Article   PubMed   Google Scholar  

Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HCW, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF, STARD Group. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology. 2015;277(3):826–32. https://doi.org/10.1148/radiol.2015151516 .

Chan A-W, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, Hróbjartsson A, Mann H, Dickersin K, Berlin JA, Doré CJ, Parulekar WR, Summerskill WSM, Groves T, Schulz KF, Sox HC, Rockhold FW, Rennie D, Moher D. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158(3):200–7. https://doi.org/10.7326/0003-4819-158-3-201302050-00583 .

Article   PubMed   PubMed Central   Google Scholar  

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350(jan07 4):g7594. https://doi.org/10.1136/bmj.g7594 .

FDA. Proposed regulatory framework for modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. 2019.

Mongan J, Moy L, Kahn JEJ. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology: Artificial Intelligence. 2020:2. https://doi.org/10.1148/ryai.2020200029 .

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Köpf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, et al. PyTorch: an imperative style. High-Performance Deep Learning Library. 2019; http://arxiv.org/abs/1912.01703 .

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(Oct):2825–30.

Download references

Author information

Authors and affiliations.

Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland

Thomas Weikert

Department of Radiology, Mayo Clinic, Rochester, MN, USA

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Thomas Weikert .

Editor information

Editors and affiliations.

Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA

Carlo N. De Cecco

Marly van Assen

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Weikert, T., Leiner, T. (2022). How to Write and Review an Artificial Intelligence Paper. In: De Cecco, C.N., van Assen, M., Leiner, T. (eds) Artificial Intelligence in Cardiothoracic Imaging. Contemporary Medical Imaging. Humana, Cham. https://doi.org/10.1007/978-3-030-92087-6_53

Download citation

DOI : https://doi.org/10.1007/978-3-030-92087-6_53

Published : 22 April 2022

Publisher Name : Humana, Cham

Print ISBN : 978-3-030-92086-9

Online ISBN : 978-3-030-92087-6

eBook Packages : Medicine Medicine (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

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

4 Ways Paperpal Encourages Responsible Writing with AI

With the growing popularity of AI tools among researchers and students alike, concerns about responsible writing with AI and the authenticity of journal and essay submissions have been on the rise. Academics constantly face the challenge of producing quality content while amping up their productivity and number of publications.  

The ethical use of AI starts with the right mindset, and Paperpal helps you align on this with the unique guardrails we’ve incorporated into its feature design.  

In 2023, we introduced a range of generative AI features on Paperpal. These were designed to assist you at various stages of the writing process while ensuring that you – the author – remains in full control of your writing. 

We were particularly cognizant of the attitudes around the use of generative AI for research and the risks of AI hallucination and plagiarism. While we were keen on incorporating generative AI in the writing process, it was important to ensure that it boosts productivity while not completely taking over your writing process. Here’s how we ensure Paperpal encourages responsible writing with AI: 

• Generative AI Tailored for Academic Writing  

General-purpose generative AI tools like ChatGPT provide generic outputs that may lack academic context. This undermines their usefulness in academic writing, where context and accuracy are paramount to communicating research effectively. Paperpal fills this very gap as it has been trained on academic content to cater to the specific needs of scholarly writing. This ensures that Paperpal ‘gets’ your research and offers suggestions that are relevant to your niche, while also helping you up your productivity game and do away with the need of endless iterations with generic AI tools.  

• Emphasis Remains on Human Authorship    

To encourage responsible writing with AI, our generative AI features were designed to assist human authorship by steering clear of producing full text. Paperpal will support your academic writing at every stage – researching, outlining, editing, and polishing. The AI academic tool will also provide outlines and advisory snippets, using which you can continue writing and take the manuscript to completion. However, research writing has to be led by you, the researcher – that’s a key guiding principle for us at Paperpal.  

• Focus on ‘Human-in-the-Loop’ to Ensure Ethical Use of AI    

Paperpal provides language or paraphrasing suggestions to speed up your polishing process. However, the text is intentionally processed only a few paragraphs at a time, requiring you to review the tool’s suggestions section-by-section, add required citations, and ensure authentic, responsible writing with AI. Paperpal also nudges you to review all AI-generated content and recommends you never use this verbatim in your work.  

• Regular Coaching on Responsible Writing with AI  

At Paperpal, we routinely post educative pieces about the ethical use of AI, and also host webinars on these key aspects. Paperpal’s product messaging also advocates strongly for responsible writing with AI, which reflects our core ethos for its inception and development. 

Final Tips on Responsible Writing with AI  

To summarize, Paperpal is focused on nurturing and guiding authors towards responsible writing with AI and ensuring that you are following the guidelines recommended for the ethical use of AI. Here are our final recommendations when using generative AI tools for your writing: 

• Ensure you know your chosen journal or institute’s guidelines on generative AI tools and follow these when using AI tools to enhance your research journey. 
• Avoid using AI-generated text as is, make sure you review for inaccuracies or changes in meaning and incorporate only the required elements. 
• Remember to always check and add citations and references where necessary to avoid any risk of potential plagiarism.  

Bid adieu to worries about the ethical use of AI and explore the efficiency that AI can bring to your academic writing process. By ensuring responsible writing with AI, you can transform your work and write better, faster, and ethically. Try Paperpal today and experience the difference for yourself! 

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• The Do’s & Don’ts of Using Generative AI Tools Ethically in Academia
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• Plagiarism Prevention: Why You Need a Plagiarism Check (Even When You Think You Don’t)!
• Is It Ethical to Use AI-Generated Abstracts Without Altering It?

What are Scholarly Sources and Where Can You Find Them? 

You may also like, what are scholarly sources and where can you..., how to write a hypothesis types and examples , what is academic writing: tips for students, paperpal’s new ai research finder empowers authors to..., what is hedging in academic writing  , how to use ai to enhance your college..., how to use paperpal to generate emails &..., ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., do plagiarism checkers detect ai content.

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

Automate your literature review with AI

Table of Contents

Traditional methods of literature review can be susceptible to errors . Whether it’s overcoming human bias ">human bias or sifting through an incredibly large amount of scientific research being published today. Not to forget all the papers that have already been published in the past 100 years. Putting both together makes a heap of information that is humanly impossible to sift through. At least do so in an efficient way.

Thanks to artificial intelligence, long and tedious literature reviews are becoming quick and comprehensive. No longer do researchers have to spend endless hours combing through stacks of books and journals.

In this blog post, we'll dive deep into the world of automating your literature review with AI, exploring what a literature review is, why it's so crucial, and how you can harness AI tools to make the process more effective.

What is a literature review?

A literature review is essentially the foundation of a scientific research project, providing a comprehensive overview of existing knowledge on a specific topic. It gives an overview of your chosen topic and summarizes key findings, theories, and methodologies from various sources.

This critical analysis not only showcases the current state of understanding but also identifies gaps and trends in the scientific literature. In addition, it also shows your understanding of your field and can help provide credibility to your research paper .

Types of literature review

There are several types of literature reviews but for the most part, you will come across five versions. These are:

1. Narrative review: A narrative review provides a comprehensive overview of a topic, usually without a strict methodology for selection.

2. Systematic review: Systematic reviews are a strategic synthesis of a topic. This type of review follows a strict plan to identify, evaluate, and critique all relevant research on a topic to minimize bias.

3. Meta-analysis: It is a type of systematic review that uses research data from multiple articles to draw quantitative conclusions about a specific phenomenon.

4. Scoping review: As the name suggests, the purpose of a scoping review is to study a field, highlight the gaps in it, and underline the need for the following research paper.

5. Critical review: A critical literature review assesses and critiques the strengths and weaknesses of existing literature, challenging established ideas and theories.

Benefits of using literature review AI tools?

Using literature review AI tools can be a complete game changer in your research. They can make the literature review process smarter and hassle-free. Here are some practical benefits:

AI tools for literature review can skim through tons of research papers and find the most relevant one for your topic in no time, thus saving you hours of manual searching.

Comprehensive insights

No matter how complex the topic is or how long the research papers are, AI tools can find key insights like methodology, datasets, limitations, etc, by simply scanning the abstracts or PDF documents.

Eliminate bias

AI doesn't have favorites. Based on the data it’s fed, it evaluates research papers objectively and reduces as much bias in your literature review as possible.

Faster research questions

AI tools present loads of research papers in the same place. Some AI tools let you create visual maps and connections, thus helping you identify gaps in existing literature and arriving at your research question faster.

Consistency

AI tools ensure your review is consistently structured and formatted . They can also check for proper grammar and citation style, which is crucial for scholarly writing.

Multilingual support

There are heaps of non-native English-speaking researchers who can struggle with understanding scientific jargon in English. AI tools with multilingual support can help such academicians conduct their literature review in their own language.

How to write a literature review with AI

Now that we understand the benefits of a literature review using artificial intelligence, let's explore how you can automate the process. Literature reviews with AI-powered tools can save you countless hours and allow a more comprehensive and systematic approach. Here's one process you can follow:

Choose the right AI tool

Several AI search engines like Google Scholar, SciSpace, Semantic Scholar help you find the most relevant papers semantically. Or in other words even without the right keywords. These tools understand the context of your search query and deliver the results.

Find relevant research papers

Once you input your research question or keywords into a search engine like Google Scholar, Semantic Scholar, or SciSpace, it scours millions of papers worth of databases to find relevant articles. After that, you can narrow your search results to a certain time period, journals, number of citations, and other parameters for more accuracy.

Analyze the search results

Now that you have your list of relevant academic papers, the next step would be reviewing these results. A lot of AI-powered tools for literature review will often provide summaries along with the paper. Some sophisticated tools also help you gather key points from multiple papers at once and let you ask questions regarding that topic. This way, you can get an understanding of the topic and further have a better understanding of your field.

Organize your collection

Whether you’re writing a literature review or your paper, you will need to keep track of your references. Using AI tools, you can efficiently organize your findings, store them in reference managers, and instantly generate citations automatically, saving you the hassle of manually formatting references.

Write the literature review

Now that you’ve done your groundwork, you can start writing your literature review. Although you should be doing this yourself, you can use tools like paraphrasers, grammar checkers, and co-writers to help you refine your academic writing and get your point across with more clarity.

Best AI Tools for Literature Review

Since generative AI and ChatGPT came into the picture, there are heaps of AI tools for literature review available out there. Some of the most comprehensive ones are:

SciSpace is a valuable tool to have in your arsenal. It has a repository of 270M+ papers and makes it easy to find research articles. You can also extract key information to compare and contrast multiple papers at the same time. Then, go on to converse with individual papers using Copilot, your AI research assistant.

Love using SciSpace tools? Enjoy discounts! Use SR40 (40% off yearly) and SR20 (20% off monthly). Claim yours here 👉 SciSpace Premium

Research Rabbit

Research Rabbit is a research discovery tool that helps you find new, connected papers using a visual graph. You can essentially create maps around metadata, which helps you not only explore similar papers but also connections between them.

Iris AI is a specialized tool that understands the context of your research question, lets you apply smart filters, and finds relevant papers. Further, you can also extract summaries and other data from papers.

If you already don’t know about ChatGPT , you must be living under a rock. ChatGPT is a chatbot that creates text based on a prompt using natural language processing (NLP). You can use it to write the first draft of your literature review, refine your writing, format it properly, write a research presentation, and many more things.

Things to keep in mind when using literature review AI tools

While AI-powered tools can significantly streamline the literature review process, there are a few things you should keep in mind while employing them:

Quality control

Always review the results generated by AI tools. AI is powerful but not infallible. Ensure that you do further analysis by yourself and determine that the selected research articles are indeed relevant to your research.

Ethical considerations

Be aware of ethical concerns, such as plagiarism and AI writing. Use of AI is still frowned upon so make sure you do a thorough check for originality of your work, which is vital for maintaining academic integrity.

Stay updated

The world of AI is ever-evolving. Stay updated on the latest advancements in AI tools for literature review to make the most of your research.

In conclusion

Artificial intelligence is a game-changer for researchers, especially when it comes to literature reviews. It not only saves time but also enhances the quality and comprehensiveness of your work. With the right AI tool and a clear research question in hand, you can build an excellent literature review.

A few more good reads for you!

Types of Essays in Academic Writing

How to Write a Conclusion for a Research Paper

You might also like

Introducing SciSpace’s Citation Booster To Increase Research Visibility

How To Write An Argumentative Essay

Beyond Google Scholar: Why SciSpace is the best alternative

Computer Science Leadership Blog , Leadership Blogs

AI-Powered Digital Writing Tools

Dr. Alexander Tuzhilin, Dean, Computer Science

Published: April 30, 2024

The past two years have yielded unprecedented growth of artificial intelligence (AI) across all industries. A recent McKinsey report on AI highlights a significant leap in its business adoption, reflecting how swiftly companies have embraced the technology to transform operations and foster innovation. This surge is driven by advancements in AI algorithms, data volume, and computational power, suggesting a deepening reliance on the technology for strategic advantage. 

Content creation tools have burgeoned, demonstrating a meteoric rise not only in their sophistication but also in their application. AI’s capability to generate meaningful, contextually appropriate text has revolutionized many aspects of writing, from drafting articles and emails to creating complex reports.

Generative AI tools, such as OpenAI’s ChatGPT , Google’s Gemini , Microsoft’s CoPilot , and Anthropic’s Claude , signify a transformative development in written content creation. Generative AI models are trained on large and diverse datasets that they use to learn patterns, structures, and nuances of content. These models, built upon neural networks, dissect training data to understand and replicate the complexity of natural language.

As a result, these tools can produce content that mirrors the training material, allowing them to create coherent written content, articles, product descriptions, and social media posts, as well as images, audio, and videos.

The use of generative AI in content creation affords significant advantages, leading to enhanced efficiency. The automation of routine tasks by generative AI facilitates content creation on a scale previously unattainable with traditional methods, allowing it to satisfy the demands of robust content strategies. AI’s speed and accuracy also significantly improve the output’s quality.

Along with its writing capabilities, generative AI also excels as an idea generator and a devil’s advocate. Its capacity to assimilate vast arrays of data and synthesize them into coherent narratives not only sparks creative ideation but also nurtures a constructive dialogue within the creative process. By adopting the mantle of a devil’s advocate, generative AI challenges established assumptions, thereby offering a multi-faceted perspective that is invaluable in scholarly and scientific pursuits. The integration of generative AI into cognitive workflows not only augments productivity but also elevates the quality and depth of intellectual inquiry.

Summarization of complex or lengthy datasets is another area where AI excels. ChatGPT is one tool that can generate a high-quality summary, as can Genei and Sharly , while Notta can convert audio files to text and then generate summaries, transcripts, meeting notes, and reports.

AI tools are also being used by enterprise marketing teams to enable marketing content creation. Jasper.ai creates custom and dynamic templated content and can adapt to a company’s brand voice. By integrating with a company’s knowledge base, Jasper.ai aids in generating cohesive and on-brand content at scale.

AI tools are also handling social media content development . HubSpot’s AI content writer specializes in copy for blogs, websites, and social media posts, while Copy.ai and Flick can both write new content and repurpose existing text into different formats. Meanwhile, Ocoya also writes captions and hashtags, while SEO.ai focuses on creating content that is search-engine optimized.

How are AI writing tools being incorporated in educational settings? They have been incorporated into educational settings to aid teachers in developing curricula, lesson plans, and discussion prompts. The George Lucas Educational Foundation highlights Curipod , Eduaide.ai , and Quizizz as especially useful for educators focused on creating classroom materials adapted to their students’ learning styles.

Additionally, several AI tools specialize in academic writing , including Studybay AI , MyStylus , and LumenWriter . These tools provide students with grammar and style suggestions, research assistance, advanced language generation, and help with citations. 

Writers using AI tools must be aware of the ethics surrounding AI to ensure appropriate use of the technology. A recent article in ScienceDirect provides guidelines on using AI writing tools in academia, although its hints can be applied to any industry. 

Plagiarism is a major concern connected to AI writing, and there are several tools available to help guard against it, including GPTRadar and Originality.ai . Popular tool Grammarly also has a plagiarism checker that runs alongside its citation generator and grammar/style features. 

Although the proliferation of AI tools has increased efficiency and creativity for users in all industries, it still requires significant human oversight. AI-generated content can be inaccurate or even made up – a phenomenon termed “hallucinations.” Instead, as this article from Toolkits recommends, AI should be used to do routine tasks, while the more creative work is handled by humans.

Bernard Marr from Forbes agrees. “I believe there will always be a need for humans in the content creation process. After all, we’re storytellers and creators by nature. But those of us who learn to use generative tools to boost our creative potential will have a distinct advantage over those who don’t as we develop new ways of creatively expressing our thoughts and ideas.”

In conclusion, it can be safely said that in the rapidly evolving landscape of digital content creation, AI has become an indispensable ally. AI writing tools are here to stay.

Related Articles

World’s #1 Research Paper Generator

Over 5,000 research papers generated daily

Have an AI Research and write your Paper in just 5 words

See it for yourself: get your free research paper started with just 5 words, how smodin makes research paper writing easy, instantly find sources for any sentence.

Our AI research tool in the research paper editor interface makes it easy to find a source or fact check any piece of text on the web. It will find you the most relevant or related piece of information and the source it came from. You can quickly add that reference to your document references with just a click of a button. We also provide other modes for research such as “find support statistics”, “find supporting arguments”, “find useful information”, and other research methods to make finding the information you need a breeze. Make research paper writing and research easy with our AI research assistant.

Easily Cite References

Our research paper generator makes citing references in MLA and APA styles for web sources and references an easy task. The research paper writer works by first identifying the primary elements in each source, such as the author, title, publication date, and URL, and then organizing them in the correct format required by the chosen citation style. This ensures that the references are accurate, complete, and consistent. The product provides helpful tools to generate citations and bibliographies in the appropriate style, making it easier for you to document your sources and avoid plagiarism. Whether you’re a student or a professional writer, our research paper generator saves you time and effort in the citation process, allowing you to focus on the content of your work.

Free AI Research Paper Generator & Writer - Say Goodbye to Writer's Block!

Are you struggling with writer's block? Even more so when it comes to your research papers. Do you want to write a paper that excels, but can't seem to find the inspiration to do so? Say goodbye to writer's block with Smodin’s Free AI Research Paper Generator & Writer!

Smodin’s AI-powered tool generates high-quality research papers by analyzing millions of papers and using advanced algorithms to create unique content. All you need to do is input your topic, and Smodin’s Research Paper generator will provide you with a well-written paper in no time.

Why Use Smodin Free AI Research Paper Generator & Writer?

Writing a research paper can be a complicated task, even more so when you have limited time and resources. A research paper generator can help you streamline the process, by quickly finding and organizing relevant sources. With Smodin's research paper generator, you can produce high-quality papers in minutes, giving you more time to focus on analysis and writing

Benefits of Smodin’s Free Research Paper Generator

• Save Time: Smodin AI-powered generator saves you time by providing you with a well-written paper that you can edit and submit.
• Quality Content: Smodin uses advanced algorithms to analyze millions of papers to ensure that the content is of the highest quality.
• Easy to Use: Smodin is easy to use, even if you're not familiar with the topic. It is perfect for students, researchers, and professionals who want to create high-quality content.

How to Write a Research Paper?

All you need is an abstract or a title, and Smodin’s AI-powered software will quickly find sources for any topic or subject you need. With Smodin, you can easily produce multiple sections, including the introduction, discussion, and conclusion, saving you valuable time and effort.

Who can write a Research Paper?

Everyone can! Smodin's research paper generator is perfect for students, researchers, and anyone else who needs to produce high-quality research papers quickly and efficiently. Whether you're struggling with writer's block or simply don't have the time to conduct extensive research, Smodin can help you achieve your goals.

Tips for Using Smodin's Research Paper Generator

With our user-friendly interface and advanced AI algorithms, you can trust Smodin's paper writer to deliver accurate and reliable results every time. While Smodin's research paper generator is designed to be easy to use, there are a few tips you can follow to get the most out of Smodinl. First, be sure to input a clear and concise abstract or title to ensure accurate results. Second, review and edit the generated paper to ensure it meets your specific requirements and style. And finally, use the generated paper as a starting point for your research and writing, or to continue generating text.

The Future of Research Paper Writing

As technology continues to advance, the future of research paper writing is likely to become increasingly automated. With tools like Smodin's research paper generator, researchers and students can save time and effort while producing high-quality work. Whether you're looking to streamline your research process or simply need a starting point for your next paper, Smodin's paper generator is a valuable resource for anyone interested in academic writing.

So why wait? Try Smodin's free AI research paper generator and paper writer today and experience the power of cutting-edge technology for yourself. With Smodin, you can produce high-quality research papers in minutes, saving you time and effort while ensuring your work is of the highest caliber.

© 2024 Smodin LLC

5 AI tools for students: Use AI to help you study, summarize content, and edit papers

Ace your classes with an AI assist.

• Quizlet Q-Chat
• Google Socratic

I wish these AI tools for students were around when I was in school. Sure, AI tools can't do your homework, write your papers, or take your exams, but they can make your life a lot easier.

With AI, long book chapters can be summarized into quick, easy-to-study bullet points, classes can be recorded and transcribed so you can be laser-focused, and weirdly worded paragraphs can be revised with AI-generated text — and that's just scratching the surface of how AI can help you as a student.

The number of AI-powered services available for students can be overwhelming, so we've rounded up the 5 best AI tools for students.

Best AI tool for editing and summarizing text: Grammarly

Grammarly is easily one of the best AI tools for students because of the wide variety of tasks it can help with. 

The most obvious way Grammarly can help you is with text generation and revision suggestions for emails, cover letters, resumes, and even school assignments. The critical warning is that you should never use AI to completely write a paper from scratch, this is a homework assistant, not something doing your homework for you. Grammarly's AI features are best used as supplemental tools that can help you get started with a tough paragraph, find a new way to say something, or edit your papers after they're written.

A few lesser-known AI features Grammarly offers include summarizing big blocks of text, generating ideas for projects, adjusting your writing tone, and providing helpful writing prompts. You can even use Grammarly to help caption your Instagram posts when you're done with homework.

Grammarly's free account lets you generate text with 100 AI prompts every month, but the premium $12/month (billed annually) option can rewrite full sentences, adjust writing tone, and generate text with 1,000 AI prompts monthly.

Stay in the know with Laptop Mag

Get our in-depth reviews, helpful tips, great deals, and the biggest news stories delivered to your inbox.

Best AI tool for intuitive studying: Quizlet Q-Chat

Most students are already familiar with Quizlet and its virtual sets of flashcards to help you master subjects. But to take your studying game to the next level, you need to check out its AI-powered Q-Chat tutor .

There are multiple ways Q-Chat can help you test your knowledge, including AI-generated quizzes, lessons, conversations, and fun games like two truths and a lie. You can also use Q-Chat to help you learn a new language, but Duolingo is a better AI tool for that purpose.

Quizlet lets you try out Q-Chat conversations with a free account, but to use its full features, you'll need a Quizlet Plus account for $7.99/month or $35.99/year. 

Best AI tool for recording and summarizing classes: Otter.ai

When you try to take notes and listen to your professor at the same time, you can sometimes miss important information. With Otter.ai , you can record the class, get transcripts and summaries, and put all your attention into listening to your teacher.

Otter is an incredibly helpful AI tool for students with ADHD or anyone else who finds it difficult to multitask in class and pay attention. However, it's worth noting that you should get permission from your teacher before recording them with Otter.

If you're an online student, Otter works with Zoom, Google Meet, and Microsoft Teams to record, transcribe, and summarize virtual classes. You can also use Otter to record meetings for group projects to easily keep track of what was talked about and what actions were assigned to everyone.

Best AI tool for explaining concepts: Google Socratic

Socratic by Google is a free AI tool available for Android and iOS that helps explain complex concepts to students with helpful visuals, AI-generated answers to questions, and links to relevant YouTube videos.

The app can help high school and university-level students with basic subjects, including algebra, geometry, trigonometry, biology, chemistry, physics, history, and literature.

Socratic can solve math problems and answer questions, but it shouldn't be used to complete homework for you. Instead, it's a useful AI tool when you're stuck on a problem or you don't understand why you got an answer wrong. Socratic can show you step-by-step explanations, helping prepare you for exams.

Another free tool that can offer in-depth explanations is Bing Chat , a GPT-4-based chatbot. This tool scours the entire internet, so Bing Chat can find answers to more complex questions on various subjects compared to Socratic.

Best AI tool for researching academic papers: Consensus

Consensus is an AI search engine for research that helps students find academic papers and studies. This AI tool is best for college and late-high school students who are starting to write research papers requiring academic sources.

You can type any research question or topic into the Consensus website to find relevant sources, and each pulled source will have pre-populated citations in multiple formats for you to copy and paste into your paper. 

Consensus also paired up with Copilot to bring ChatGPT -type functionality to the service. This means you can tack on a command to your search, like "Group together pro and con cases" or "Explain for an 8-year-old." 

A free Consensus account gives you unlimited searches, unlimited AI-powered filters, and 20 AI credits every month for more powerful features, like GPT-4 Summaries, Consensus Meters, Study Snapshots, and Copilot. For unlimited use of those more powerful features, a premium subscription costs $8.99/month.

MORE FROM LAPTOP MAG FOR STUDENTS

• Best college student discounts and perks
• Best laptop backpacks
• Best laptops for college: Student laptops for every budget and major

Windows 12 isn't Microsoft's only missing operating system

How to switch devices on Google Meet: Seamlessly transfer from phone to laptop

A tale of two flops: What Apple Vision Pro can learn from Google and Nintendo failures

Most Popular

• 2 Razer Viper V3 Pro vs. Alienware Pro Wireless: Which gaming mouse is better?
• 3 3 Windows 11 taskbar tricks to change up your desktop
• 4 The 9 most important factors for buying a gaming laptop
• 5 Windows 12 isn't Microsoft's only missing operating system

Our approach

• Responsibility
• Infrastructure
• Try Meta AI

RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

Our latest updates delivered to your inbox

Subscribe to our newsletter to keep up with Meta AI news, events, research breakthroughs, and more.

Join us in the pursuit of what’s possible with AI.

Product experiences

Foundational models

Latest news

Meta © 2024

Academic Citation Finder

Ai-powered academic citation and resource discovery.

• Write a research paper: Find high-quality, reputable sources to support your arguments and hypotheses.
• Conduct a literature review: Discover key studies and articles in your field of interest.
• Prepare a thesis or dissertation: Gather a comprehensive list of academic resources related to your research topic.
• Create a bibliography or reference list: Easily find all the necessary citation information for each resource.
• Stay updated on current research: Regularly search for recent publications in your field of study.

New & Trending Tools

Ai text message responder, chemistry expert ai, bible study ai assistant.

• Skip to main content
• Keyboard shortcuts for audio player

Book News & Features

Ai is contentious among authors. so why are some feeding it their own writing.

Chloe Veltman

The vast majority of authors don't use artificial intelligence as part of their creative process — or at least won't admit to it.

Yet according to a recent poll from the writers' advocacy nonprofit The Authors Guild, 13% said they do use AI, for activities like brainstorming character ideas and creating outlines.

The technology is a vexed topic in the literary world. Many authors are concerned about the use of their copyrighted material in generative AI models. At the same time, some are actively using these technologies — even attempting to train AI models on their own works.

These experiments, though limited, are teaching their authors new things about creativity.

Best known as the author of technology and business-oriented non-fiction books like The Long Tail, lately Chris Anderson has been trying his hand at fiction. Anderson is working on his second novel, about drone warfare.

He says he wants to put generative AI technology to the test.

"I wanted to see whether in fact AI can do more than just help me organize my thoughts, but actually start injecting new thoughts," Anderson says.

Anderson says he fed parts of his first novel into an AI writing platform to help him write this new one. The system surprised him by moving his opening scene from a corporate meeting room to a karaoke bar.

Authors push back on the growing number of AI 'scam' books on Amazon

"And I was like, you know? That could work!" Anderson says. "I ended up writing the scene myself. But the idea was the AI's."

Anderson says he didn't use a single actual word the AI platform generated. The sentences were grammatically correct, he says, but fell way short in terms of replicating his writing style. Although he admits to being disappointed, Anderson says ultimately he's OK with having to do some of the heavy lifting himself: "Maybe that's just the universe telling me that writing actually involves the act of writing."

Training an AI model to imitate style

It's very hard for off-the-shelf AI models like GPT and Claude to emulate contemporary literary authors' styles.

The authors NPR talked with say that's because these models are predominantly trained on content scraped from the Internet like news articles, Wikipedia entries and how-to manuals — standard, non-literary prose.

But some authors, like Sasha Stiles , say they have been able to make these systems suit their stylistic needs.

"There are moments where I do ask my machine collaborator to write something and then I use what's come out verbatim," Stiles says.

The poet and AI researcher says she wanted to make the off-the-shelf AI models she'd been experimenting with for years more responsive to her own poetic voice.

So she started customizing them by inputting her finished poems, drafts, and research notes.

"All with the intention to sort of mentor a bespoke poetic alter ego," Stiles says.

She has collaborated with this bespoke poetic alter ego on a variety of projects, including Technelegy (2021), a volume of poetry published by Black Spring Press; and " Repetae: Again, Again ," a multimedia poem created last year for luxury fashion brand Gucci.

Stiles says working with her AI persona has led her to ask questions about whether what she's doing is in fact poetic, and where the line falls between the human and the machine.

read it again… pic.twitter.com/sAs2xhdufD — Sasha Stiles | AI alter ego Technelegy ✍️🤖 (@sashastiles) November 28, 2023

"It's been really a provocative thing to be able to use these tools to create poetry," she says.

Potential issues come with these experiments

These types of experiments are also provocative in another way. Authors Guild CEO Mary Rasenberger says she's not opposed to authors training AI models on their own writing.

"If you're using AI to create derivative works of your own work, that is completely acceptable," Rasenberger says.

Thousands of authors urge AI companies to stop using work without permission

But building an AI system that responds fluently to user prompts requires vast amounts of training data. So the foundational AI models that underpin most of these investigations in literary style may contain copyrighted works.

Rasenberger pointed to the recent wave of lawsuits brought by authors alleging AI companies trained their models on unauthorized copies of articles and books.

"If the output does in fact contain other people's works, that creates real ethical concerns," she says. "Because that you should be getting permission for."

Circumventing ethical problems while being creative

Award-winning speculative fiction writer Ken Liu says he wanted to circumvent these ethical problems, while at the same time creating new aesthetic possibilities using AI.

So the former software engineer and lawyer attempted to train an AI model solely on his own output. He says he fed all of his short stories and novels into the system — and nothing else.

Liu says he knew this approach was doomed to fail.

That's because the entire life's work of any single writer simply doesn't contain enough words to produce a viable so-called large language model.

"I don't care how prolific you are," Liu says. "It's just not going to work."

Liu's AI system built only on his own writing produced predictable results.

"It barely generated any phrases, even," Liu says. "A lot of it was just gibberish."

Yet for Liu, that was the point. He put this gibberish to work in a short story. 50 Things Every AI Working With Humans Should Know , published in Uncanny Magazine in 2020, is a meditation on what it means to be human from the perspective of a machine.

"Dinoted concentration crusch the dead gods," is an example of one line in Liu's story generated by his custom-built AI model. "A man reached the torch for something darker perified it seemed the billboding," is another.

Liu continues to experiment with AI. He says the technology shows promise, but is still very limited. If anything, he says, his experiments have reaffirmed why human art matters.

"So what is the point of experimenting with AIs?" Liu says. "The point for me really is about pushing the boundaries of what is art."

Audio and digital stories edited by Meghan Collins Sullivan .

• large language model
• mary rasenberger
• chris anderson
• sasha stiles
• authors guild