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AI for thesis writing — Unveiling 7 best AI tools

Table of Contents

Writing a thesis is akin to piecing together a complex puzzle. Each research paper, every data point, and all the hours spent reading and analyzing contribute to this monumental task.

For many students, this journey is a relentless pursuit of knowledge, often marked by sleepless nights and tight deadlines.

Here, the potential of AI for writing a thesis or research papers becomes clear: artificial intelligence can step in, not to take over but to assist and guide.

Far from being just a trendy term, AI is revolutionizing academic research, offering tools that can make the task of thesis writing more manageable, more precise, and a little less overwhelming.

In this article, we’ll discuss the impact of AI on academic writing process, and articulate the best AI tools for thesis writing to enhance your thesis writing process.

The Impact of AI on Thesis Writing

Artificial Intelligence offers a supportive hand in thesis writing, adeptly navigating vast datasets, suggesting enhancements in writing, and refining the narrative.

With the integration of AI writing assistant, instead of requiring you to manually sift through endless articles, AI tools can spotlight the most pertinent pieces in mere moments. Need clarity or the right phrasing? AI-driven writing assistants are there, offering real-time feedback, ensuring your work is both articulative  and academically sound.

AI tools for thesis writing harness Natural Language Processing (NLP) to generate content, check grammar, and assist in literature reviews. Simultaneously, Machine Learning (ML) techniques enable data analysis, provide personalized research recommendations, and aid in proper citation.

And for the detailed tasks of academic formatting and referencing? AI streamlines it all, ensuring your thesis meets the highest academic standards.

However, understanding AI's role is pivotal. It's a supportive tool, not the primary author. Your thesis remains a testament to your unique perspective and voice.

AI for writing thesis is there to amplify that voice, ensuring it's heard clearly and effectively.

How AI tools supplement your thesis writing

AI tools have emerged as invaluable allies for scholars. With just a few clicks, these advanced platforms can streamline various aspects of thesis writing, from data analysis to literature review.

Let's explore how an AI tool can supplement and transform your thesis writing style and process.

Efficient literature review : AI tools can quickly scan and summarize vast amounts of literature, making the process of literature review more efficient. Instead of spending countless hours reading through papers, researchers can get concise summaries and insights, allowing them  to focus on relevant content.

Enhanced data analysis : AI algorithms can process and analyze large datasets with ease, identifying patterns, trends, and correlations that might be difficult or time-consuming for humans to detect. This capability is especially valuable in fields with massive datasets, like genomics or social sciences.

Improved writing quality : AI-powered writing assistants can provide real-time feedback on grammar, style, and coherence. They can suggest improvements, ensuring that the final draft of a research paper or thesis is of high quality.

Plagiarism detection : AI tools can scan vast databases of academic content to ensure that a researcher's work is original and free from unintentional plagiarism .

Automated citations : Managing and formatting citations is a tedious aspect of academic writing. AI citation generators  can automatically format citations according to specific journal or conference standards, reducing the chances of errors.

Personalized research recommendations : AI tools can analyze a researcher's past work and reading habits to recommend relevant papers and articles, ensuring that they stay updated with the latest in their field.

Interactive data visualization : AI can assist in creating dynamic and interactive visualizations, making it easier for researchers to present their findings in a more engaging manner.

Top 7 AI Tools for Thesis Writing

The academic field is brimming with AI tools tailored for academic paper writing. Here's a glimpse into some of the most popular and effective ones.

Here we'll talk about some of the best ai writing tools, expanding on their major uses, benefits, and reasons to consider them.

If you've ever been bogged down by the minutiae of formatting or are unsure about specific academic standards, Typeset is a lifesaver.

Typeset specializes in formatting, ensuring academic papers align with various journal and conference standards.

It automates the intricate process of academic formatting, saving you from the manual hassle and potential errors, inflating your writing experience.

An AI-driven writing assistant, Wisio elevates the quality of your thesis content. It goes beyond grammar checks, offering style suggestions tailored to academic writing.

This ensures your thesis is both grammatically correct and maintains a scholarly tone. For moments of doubt or when maintaining a consistent style becomes challenging, Wisio acts as your personal editor, providing real-time feedback.

Known for its ability to generate and refine thesis content using AI algorithms, Texti ensures logical and coherent content flow according to the academic guidelines.

When faced with writer's block or a blank page, Texti can jumpstart your thesis writing process, aiding in drafting or refining content.

JustDone is an AI for thesis writing and content creation. It offers a straightforward three-step process for generating content, from choosing a template to customizing details and enjoying the final output.

JustDone AI can generate thesis drafts based on the input provided by you. This can be particularly useful for getting started or overcoming writer's block.

This platform can refine and enhance the editing process, ensuring it aligns with academic standards and is free from common errors. Moreover, it can process and analyze data, helping researchers identify patterns, trends, and insights that might be crucial for their thesis.

Tailored for academic writing, Writefull offers style suggestions to ensure your content maintains a scholarly tone.

This AI for thesis writing provides feedback on your language use, suggesting improvements in grammar, vocabulary, and structure . Moreover, it compares your written content against a vast database of academic texts. This helps in ensuring that your writing is in line with academic standards.

Isaac Editor

For those seeking an all-in-one solution for writing, editing, and refining, Isaac Editor offers a comprehensive platform.

Combining traditional text editor features with AI, Isaac Editor streamlines the writing process. It's an all-in-one solution for writing, editing, and refining, ensuring your content is of the highest quality.

PaperPal , an AI-powered personal writing assistant, enhances academic writing skills, particularly for PhD thesis writing and English editing.

This AI for thesis writing offers comprehensive grammar, spelling, punctuation, and readability suggestions, along with detailed English writing tips.

It offers grammar checks, providing insights on rephrasing sentences, improving article structure, and other edits to refine academic writing.

The platform also offers tools like "Paperpal for Word" and "Paperpal for Web" to provide real-time editing suggestions, and "Paperpal for Manuscript" for a thorough check of completed articles or theses.

Is it ethical to use AI for thesis writing?

The AI for writing thesis has ignited discussions on authenticity. While AI tools offer unparalleled assistance, it's vital to maintain originality and not become overly reliant. Research thrives on unique contributions, and AI should be a supportive tool, not a replacement.

The key question: Can a thesis, significantly aided by AI, still be viewed as an original piece of work?

AI tools can simplify research, offer grammar corrections, and even produce content. However, there's a fine line between using AI as a helpful tool and becoming overly dependent on it.

In essence, while AI offers numerous advantages for thesis writing, it's crucial to use it judiciously. AI should complement human effort, not replace it. The challenge is to strike the right balance, ensuring genuine research contributions while leveraging AI's capabilities.

Wrapping Up

Nowadays, it's evident that AI tools are not just fleeting trends but pivotal game-changers.

They're reshaping how we approach, structure, and refine our theses, making the process more efficient and the output more impactful. But amidst this technological revolution, it's essential to remember the heart of any thesis: the researcher's unique voice and perspective .

AI tools are here to amplify that voice, not overshadow it. They're guiding you through the vast sea of information, ensuring our research stands out and resonates.

Try these tools out and let us know what worked for you the best.

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Frequently Asked Questions

Yes, you can use AI to assist in writing your thesis. AI tools can help streamline various aspects of the writing process, such as data analysis, literature review, grammar checks, and content refinement.

However, it's essential to use AI as a supportive tool and not a replacement for original research and critical thinking. Your thesis should reflect your unique perspective and voice.

Yes, there are AI tools designed to assist in writing research papers. These tools can generate content, suggest improvements, help with formatting, and even provide real-time feedback on grammar and coherence.

Examples include Typeset, JustDone, Writefull, and Texti. However, while they can aid the process, the primary research, analysis, and conclusions should come from the researcher.

The "best" AI for writing papers depends on your specific needs. For content generation and refinement, Texti is a strong contender.

For grammar checks and style suggestions tailored to academic writing, Writefull is highly recommended. JustDone offers a user-friendly interface for content creation. It's advisable to explore different tools and choose one that aligns with your requirements.

To use AI for writing your thesis:

1. Identify the areas where you need assistance, such as literature review, data analysis, content generation, or grammar checks.

2. Choose an AI tool tailored for academic writing, like Typeset, JustDone, Texti, or Writefull.

3. Integrate the tool into your writing process. This could mean using it as a browser extension, a standalone application, or a plugin for your word processor.

4. As you write or review content, use the AI tool for real-time feedback, suggestions, or content generation.

5. Always review and critically assess the suggestions or content provided by the AI to ensure it aligns with your research goals and maintains academic integrity.

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What is a thesis | A Complete Guide with Examples

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

The 11 best AI tools for academic writing

What are the best AI tools for academic writing?

Furthermore, the optional functions were compared to their respective prices to ensure a fair pricing structure. AI support for academic writing should be affordable and not strain your budget.

Best Overall for Academic Writing ($6.67/month)

Trinka takes into account the specific research subjects, ensuring that the writing style, word choice, and tone align with disciplinary standards and scientific conventions.

Key Features:

3. quil lbot, 4. writefull, 5. grammarly, 6. wordtune, 7. paperpal, 8. sourcely, 10. writesonic, 11. textcortex, summary and top picks, master academia, get new content delivered directly to your inbox, how to benefit from chatgpt as an academic, 38 common academic job interview questions (+ powerful answers), related articles, chatgpt for academics ethical considerations of ai in research, theoretical vs. conceptual frameworks: simple definitions and an overview of key differences, a guide to industry-funded research: types, examples & getting started, journal vs conference papers: key differences & advice.

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Using AI tools

AI Writing Tools | Definition, Uses & Implications

AI writing tools are artificial intelligence (AI) software applications like ChatGPT that help to automate or assist the writing process. These tools use machine learning algorithms to generate human-sounding text in response to users’ text-based prompts.

Other AI tools, such as grammar checkers , paraphrasers and summarizers serve more specific functions, like identifying grammar and spelling mistakes or rephrasing text.

Table of contents

How do ai writing tools work, what can ai writing tools be used for, implications of ai writing tools, other interesting articles, frequently asked questions about ai writing tools.

AI writing tools (chatbots, grammar checkers, etc.) use natural language processing (NLP) algorithms, machine learning , and large language models (LLMs) to generate or improve written text. These tools are trained to identify patterns in vast amounts of data. The tools then use these patterns to analyze human inputs (or prompts) and generate natural-sounding outputs (similar to how predictive text works).

Different AI tools may be programmed in different ways. For example, ChatGPT, the popular chatbot developed by OpenAI and released in November 2022, uses generative pre-trained transformers (GPTs) and was trained using reinforcement learning from human feedback (RLHF) , meaning that the tool was given feedback from humans to help guide it towards the most helpful responses.

AI writing tools have a number of helpful applications. Some of these applications are discussed below.

Interactive learning and feedback

Generative AI tools can be helpful resources for conversational learning. By inputting questions, users can engage in interactive learning at any time.

You can also prompt these tools to provide feedback on your work to improve the clarity or consistency of your writing. However, it’s important not to upload sensitive information, as user inputs are generally saved by the tool for the purposes of training future models.

Below is an example of how ChatGPT can be used for interactive learning.

Example: Using ChatGPT for interactive learning

• Tell me three facts about Carthaginian culture
• Carthaginians were known for their maritime trade and commerce, and their navy was one of the strongest in the ancient Mediterranean world. They established colonies and trading posts throughout the Mediterranean, including in Sicily, Sardinia, and North Africa.
• Carthaginians had a polytheistic religion, and their chief deity was Baal Hammon, the god of fertility, agriculture, and the sky. They also worshiped a number of other gods and goddesses, including Tanit, the goddess of love, fertility, and war.
• Carthaginians were skilled metalworkers and produced a variety of metal goods, including weapons, armor, jewelry, and household objects. They were particularly famous for their bronze work, which was highly prized in the ancient world. Carthaginian bronze was so valued that it was used by the Romans to make their famous statues and other works of art.
• Tell me three more facts about the Carthaginian navy
• The Carthaginian navy was one of the most advanced and powerful navies of the ancient world. It was instrumental in establishing Carthage as a dominant power in the Mediterranean region.
• The Carthaginians were known for their skill in shipbuilding and developed a variety of ship types for different purposes, including warships, transport ships, and cargo ships. One of their most famous ship types was the quinquereme, a large warship with five rows of oars.
• The Carthaginian navy was led by a commander called the “admiral.” The admiral was responsible for overseeing the fleet and making strategic decisions about naval warfare. One of the most famous Carthaginian admirals was Hannibal Barca, who led the Carthaginian fleet during the First Punic War against Rome.

Generating research questions and outlines

AI writing tools can be used to generate potential research questions or research paper outlines . You can then refine the tool’s responses by further prompting it or directly editing the output yourself to ensure the result makes sense and fits your requirements.

Below is an example of how ChatGPT can be used to generate potential reseach questions and research method ideas.

Example: Using ChatGPT to generate research questions and methods

• Develop three possible research questions on the following topic: “The influence of technology on second language acquisition.”
• What is the impact of technology-assisted language learning (TALL) on the acquisition of a second language?
• How do different types of technology, such as mobile applications, virtual reality, and online platforms, affect second language acquisition outcomes?
• How do individual learner characteristics, such as age, proficiency level, and motivation, interact with the use of technology in second language acquisition?
• Suggest three quantitative research methods appropriate to the second question.
• Experimental research: This research method involves manipulating an independent variable (in this case, the type of technology used) to observe its effect on a dependent variable (second language acquisition outcomes). Participants would be randomly assigned to different groups using different types of technology to learn a second language, and then their language acquisition outcomes would be measured and compared.
• Survey research: This research method involves using questionnaires to gather data from a large group of participants. In this case, a survey could be designed to gather information on participants’ use of different types of technology to learn a second language, as well as their language acquisition outcomes. This would allow for a large-scale investigation of how different types of technology are being used, and what their impact might be.
• Correlational research: This research method involves examining the relationship between two or more variables. In this case, a correlational study could be conducted to investigate whether there is a relationship between the type of technology used and language acquisition outcomes. Data could be collected from participants using different types of technology to learn a second language, and then the correlation between the type of technology and language acquisition outcomes could be calculated.

Paraphrasing text

AI tools like ChatGPT and Scribbr’s free paraphrasing tool can help you paraphrase text to express your ideas more clearly, avoid repetition, and maintain a consistent tone throughout your writing.

They can also help you incorporate scholarly sources in your writing in a more concise and fluent way, without the need for direct quotations. However, it’s important to correctly cite all sources to avoid accidental plagiarism.

Summarizing text

AI writing tools can help condense a text to its most important and relevant ideas. This can help you understand complex information more easily. You can also use summarizer tools on your own work to summarize your central argument, clarify your research question, and form conclusions.

You can do this using generative AI tools or more specialized tools like Scribbr’s free text-summarizer .

Proofreading text

AI writing tools can be used to identify spelling, grammar, and punctuation mistakes and suggest corrections. These tools can help to improve the clarity of your writing and avoid common mistakes .

While AI tools like ChatGPT offer useful suggestions, they can also potentially miss some mistakes or even introduce new grammatical errors into your writing.

We advise using Scribbr’s proofreading and editing service  or a tool like Scribbr’s free grammar checker , which is designed specifically for this purpose.

Translating text

AI translation tools like Google Translate can be used to translate text from a source language into various target languages. While the quality of these tools tend to vary depending on the languages used, they’re constantly developing and are increasingly accurate.

While there are many benefits to using AI writing tools, some commentators have emphasized the limitations of AI tools and the potential disadvantages of using them. These drawbacks are discussed below.

Impact on learning

One of the potential pitfalls of using AI writing tools is the effect they might have on a student’s learning and skill set. Using AI tools to generate a paper, thesis , or dissertation , for example, may impact a student’s research, critical thinking, and writing skills.

However, other commentators argue that AI tools can be used to promote critical thinking (e.g., by having a student evaluate a tool’s output and refine it).

Consistency and accuracy

Generative AI tools (such as ChatGPT) are not always trustworthy and sometimes produce results that are inaccurate or factually incorrect. Although these tools are programmed to answer questions, they can’t judge the accuracy of the information they provide and may generate incorrect answers or contradict themselves.

It’s important to verify AI-generated information against a credible source .

Grammatical mistakes

While generative AI tools can produce written text, they don’t actually understand what they’re saying and sometimes produce grammar, spelling, and punctuation mistakes.

You can combine the use of generative AI tools with Scribbr’s grammar checker , which is designed to catch these mistakes.

Ethics and plagiarism

As AI writing tools are trained on large sets of data, they may produce content that is similar to existing content (which they usually cannot cite correctly), which can be considered plagiarism.

Furthermore, passing off AI-generated text as your own work is usually considered a form of plagiarism and is likely to be prohibited by your university. This offense may be recognized by your university’s plagiarism checker or AI detector .

If you want more tips on using AI tools , understanding plagiarism , and citing sources , make sure to check out some of our other articles with explanations, examples, and formats.

• Citing ChatGPT
• Best grammar checker
• Best paraphrasing tool
• ChatGPT in your studies
• Is ChatGPT trustworthy?
• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Best plagiarism checker

Citing sources

• Citation styles
• In-text citation
• Citation examples
• Annotated bibliography

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarize text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarizing tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalization, unless it is allowed by your university . Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up.” You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs $20 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

ChatGPT was publicly released on November 30, 2022. At the time of its release, it was described as a “research preview,” but it is still available now, and no plans have been announced so far to take it offline or charge for access.

ChatGPT continues to receive updates adding more features and fixing bugs. The most recent update at the time of writing was on May 24, 2023.

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The Ultimate Thesis Statement Generator

Thesis Statement Generator

Are you struggling with formulating a concise, compelling thesis statement? You're not alone. Jenni.ai is here to empower your academic writing journey with our thesis statement generator, a sophisticated AI tool dedicated to generating and refining thesis statements. Unlike other tools, Jenni ensures the authenticity and uniqueness of your thesis statement, setting a strong foundation for your academic piece.

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Envision a clearer path towards crafting thesis statements with these winning features

Personalized Thesis Statement Suggestions

Jenni AI tailors suggestions based on your input, helping you craft a thesis statement that aligns with your research goals and academic standards.

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Interactive Writing Assistant

Engage with Jenni to refine and improve your thesis statement. It's like having a collaborative partner in the writing process, ensuring your thesis is on the right track.

Accessible Anytime, Anywhere

With Jenni AI, receive thesis statement assistance whenever inspiration strikes, on any device.

User-friendly Interface

With an intuitive interface, Jenni AI is easy to navigate and use, making the process of crafting a thesis statement straightforward and enjoyable.

Your Integrity, Our Priority

Jenni AI isn’t here to do the work for you but to assist you in overcoming the hurdles of thesis statement writing. We take academic integrity seriously. We are here to support you, not to shortcut your academic journey.

How does the Thesis Statement Generator Work?

Jenni AI seamlessly integrates into your research workflow

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Create your free Jenni.ai account to unlock a world of academic excellence. Your journey toward a compelling thesis statement begins here.

Input Your Topic

Type in your research topic or a key question you aim to answer through your thesis. Jenni’s intuitive interface makes this step a breeze.

Receive Suggestions

Based on your input, Jenni.ai provides you with a set of preliminary thesis statement suggestions. These are tailored to reflect the essence of your research while adhering to academic standards.

Refine with Real-Time Feedback

Jenni.ai offers real-time feedback as you tweak and refine your thesis statement. This iterative process ensures clarity, coherence, and a strong alignment with your research objectives.

Export Your Thesis Statement

Once satisfied, simply download or copy your thesis statement, and you’re ready to delve deeper into your academic writing journey.

Real Testimonials from Real Achievers

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Can I use Jenni AI on my mobile device?using Jenni AI's outline generator guarantee a better grade?

Jenni AI vs Competitors: A Comparative Insight

Experience a notable difference in how you articulate and present your scholarly ideas with Jenni AI

Feature Featire

COMPETITORS

Quality of Suggestions

Advanced AI algorithms ensure high-quality, academically sound thesis statement suggestions tailored to your research topic.

Generic suggestions that may lack depth or alignment with your specific research objectives.

User-Friendly Interface

User-friendly interface designed for seamless navigation and immediate engagement, requiring no technical expertise.

May have a steeper learning curve, with complex features that require time to understand.

Comprehensive Customization

Jenni's outline generator allows for comprehensive customization, empowering you to tailor your outline to your specific needs and preferences.

While competitors may offer customization options, they may not provide the same level of flexibility and adaptability.

Real-Time Feedback

Provides instant feedback as you refine your thesis statement, aiding in the iterative improvement process.

May lack real-time feedback, leaving you without guidance for improvement.

Competitive pricing with a free version available to get you started.

May have higher pricing or lack a free version for trial.

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Introduction

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

1. Different kinds of reading

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

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

1.1. Exploratory reading

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

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

1.2. Narrow, deep reading

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

1.3. Broad, targeted reading

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

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

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

2. Drawbacks of broad targeted reading

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

3. Existing solutions to speed up literature reviews

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

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

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

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

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

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

4. Lateral — a new solution

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

5. Final note — novel research alongside the literature review

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

References:

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

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

Spread the word

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

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

Remote group work and the best student collaboration tools

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

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

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

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Write a Dissertation with AI

Molin can help you with your dissertation in many ways from picking the topic, creating the outline, and writing entire sections.

How can I write a dissertation with AI? ‍

Follow this step-by-step guide on how you can make your student life easier and use artificial intelligence to generate entire essays for your homework. ‍

• Visit https://molin.ai
• From the Students category, pick the Dissertation Ideas template
• Enter your field of study (you can add your interests too for better results)
• Copy the chosen topic from the list of ideas (if you don't like any of them, simply generate again)
• Paste the topic into the Essay Outline template and generate. This will give you the entire outline of your dissertation.
• Finally, start copying the outline elements into the Entire Essay template one by one and create your essay from these pieces.
• That's it! You now have a plagiarism-free dissertation. ‍

This is how you can get your AI dissertation in a few minutes with references in multiple languages. ‍

Watch this video guide for more information:

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How to Write an Impressive Thesis Using an AI Language Assistant

Session Agenda

Writing an effective thesis is important for researchers to demonstrate their thorough knowledge about the research subject.  However, this could be a daunting task given the struggle to compile years of research in a structured and error-free manner. The process becomes even more stressful when the scuffle for using verbs in their correct tense, adding appropriate punctuation marks, and fixing grammatical and contextual spelling errors ensues. This webinar aims to help researchers understand how Trinka , an advanced artificial intelligence-powered writing assistant, can assess and enhance the quality of their thesis. It will further discuss how this dedicated AI-based tool can assist researchers to resolve the longstanding challenge of communicating their thesis in a grammatically correct and scientifically structured manner.

• Three stages of Writing: Planning, Writing, and Editing
• Structure of a Thesis – Types and Important Sections
• How to Organize your Thoughts and Begin Writing
• How to Write a Thesis Statement
• Common Errors in Thesis Writing
• Expert Tips on Drafting Each Section of a Thesis
• Role of AI in Academic Writing
• How AI Can Assist Authors to Write an Impressive Thesis

Who should attend this session?

• Graduate students
• Early-stage researchers
• Doctoral students

About the Speaker

Douglas W. Darnowski, Ph.D.

Dr. Darnowski is a highly published researcher with experience in publishing books, book chapters, research papers, review articles, teaching material (textbooks, instruction manuals, etc.) and book reviews. Dr. Darnowski has over 20 years of experience in editing of scientific papers for peer-reviewed journals. Furthermore, he has reviewed over 50 introductory biology textbook chapters and wrote over 9,000 questions for Sinauer, W.H. Freeman, McGraw Hill, and Pearson. Currently, he is associated with the Editorial Boards of Carnivorous Plant Newsletter and International Triggerplant Society. He is also the recipient of more than 40 research grants/fellowships.

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Producing a thesis is a lot of work. You need to develop an original topic, conduct some research, comprehend your results, and then – as the most difficult task – write the thesis and make sure it is of excellent quality. Given the complexity of the process, it is no wonder that students get stressed when their thesis is due for submission. The good news is that AI tools can help enhance your thesis and ensure that you submit work that your professors will value. Here we explore some ways AI can boost your thesis writing process.

Use AI to Locate the Literature

All dissertations require a comprehensive literature review that will introduce the topic and evaluate the literature in the area. However, getting hold of the relevant literature is not the easiest task. You may need to spend days browsing academic databases to locate books and articles on the topic. AI can help you speed up this process. In ChatGPT, you can install a plugin called ScholarAI that will list relevant literature in your chosen area. Whether your thesis is in engineering, medicine, business, or humanities, ScholarAI will have access to useful sources that you could consider when developing your literature review.

Use AI to Comprehend and Organise the Literature

Once you have the relevant literature at hand, your next task will be to read through it, comprehend it, and organise it. This step is crucial before starting to write the literature review itself. A ChatGPT plugin that could help you with this task is AskYourPDF. The plugin allows you to upload a PDF, such as a journal article, and summarise its content. You could even ask it questions to enhance your understanding of the source at hand. By repeating this process for all sources you located, AI will help you understand the literature, organise its main points, and report it in your thesis.

Use AI to Develop your Methodology Section

Regardless of your area of study, your thesis will require describing a methodological approach you adopted in your research. Students often find methodologies confusing because they encompass various strategies and techniques that must be tailored to a specific research question. ChatGPT can act as a personal methodology tutor and help you out. ChatGPT has an extensive understanding of different methodologies – so, by describing your research in the chat, the AI tool can help you discern what methodology you are using and why. When prompted, it can even suggest how to report your methodology and what methodological aspects you should consider to have a well-developed methodology section.

Use AI to Understand Your Results

Within your thesis, you will report quantitative or qualitative results – depending on your research's investigation. Your results will need to be understood in the context of your thesis and such understanding will require an interpretation based on the reviewed literature and adopted methodology. This may sound complex but AI can simplify the process for you. Paste your literature review and methodology section in ChatGPT and ask it to review it. Once done, explain your results to ChatGPT and ask it to interpret them in the context of your thesis. The tool will produce relevant insights that could spark your thinking about your results.

Use AI to Boost your Discussion Section

Your last step in the thesis-writing process will be to develop a discussion section. You will need to discuss aspects such as the strengths and limitations of your research and implications for the literature and real life. This is another section where ChatGPT can give relevant insights. Instruct it to brainstorm about strengths, limitations, and implications of your study and results to get ideas to start writing. However, be sure to cherry-pick only the best ideas that you could develop further as it is these ideas that will boost your thesis the most.

Takeaway Message

Writing a thesis is a complex process – however, it is a process that can be simplified and enhanced by AI. You can use ChatGPT for all aspects of your thesis, including the literature review, methodology, results, and discussion. By helping you locate and comprehend the literature, describe your methodology, understand your results, and gain insights into the strengths, limitations, and implications of your research, ChatGPT can enhance your thesis and help you produce a work that your supervisors will appreciate.

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Should I use AI to write my PhD thesis?

• 5 minute read

Imagine you’re struggling to write your thesis and wake up one morning to discover there’s an artificial intelligence (AI) tool that can weave your less-than-perfectly crafted prose into scholarly gold. Would, and should, you use it?

I recently received an email from a PhD candidate who’s wrestling with this question. With her submission deadline fast approaching, she asked my advice on how to approach using AI-powered writing tools to responsibly refine her thesis without committing academic integrity violations. Specifically, she wanted to know if AI tools with grammar and rephrasing capabilities would be considered plagiarism or an act of academic misconduct.

Those of you who’ve been following my thoughts on AI tools will know that I’m a big fan of leveraging AI to help augment our abilities. But I also stress in all my AI training sessions that there’s a line – albeit sometimes a fuzzy one – between using AI to enhance your contribution to knowledge and using AI to produce outputs that have little in common with your domain knowledge or level of expertise.

As more graduate students turn to AI tools to augment their writing process, I thought it timely to reflect on the topic in this blog post. Noting that what follows constitutes my opinion and not formal advice.

When considering the use of AI for the creation of scholarly outputs, always seek guidance from your university!

Could versus Should

First, let’s address the question of whether you can generate significant amounts of text with AI tools and paste them into your thesis without detection.

Yes, you can.

But should you?

I think not.

Asking an AI assistant to write entire sections of text and then pulling a sneaky Ctrl+C and Ctrl+V is problematic from several standpoints.

While it may seem expedient at first glance, this approach robs you of deeply engaging with the conceptual connections across your research and limits the knowledge growth that comes through grappling to articulate such links yourself. Allowing algorithms to do the heavy lifting might also preclude you from catching deeper insights that often emerge in the struggle to translate fuzzy mental models into clear text through ‘writing to think’.

Relying solely on AI to outsource your thesis writing rids you of the opportunity to build the foundational knowledge that will be required to effectively participate in academic discourse in your field in the future.

Furthermore, and perhaps the most cited reason for avoiding cut-and-paste output creation is the fact that claiming authorship over content you didn’t substantively contribute to generating (beyond writing an initial prompt to get an AI tool to generate the text) undermines scholarly rigour and integrity.

While intelligent algorithms can creatively recombine impressive vocabulary, they cannot replicate the context-driven sensemaking cultivated through your long journey with the literature and analysis.

Excessively depending on synthetic support is no substitute for rigorous scholarly thinking and writing.

AI Copywriting Versus AI Copyediting

You can think of the cut-and-paste scenario as an example of AI copywriting, a term I’m using to refer to the process of outsourcing scholarly writing and thinking to algorithms to generate text with minimal or no human intervention.

AI copywriting raises ethical alarm bells.

An alternative is AI copyediting.

AI copyediting refers to using AI tools to work with text you’ve already written to augment your scholarly writing abilities; using AI to gain feedback on your writing and suggest improvements.

In this scenario, you are working with text you’ve already conceptualised, and then improving upon the hard work you’ve put in to lift the written style, tone, sentence structure etc. to a higher level.

When done well, using AI as a writing assistant can significantly lift the standard of scholarly writing.

As an example, sections of text can be provided to an AI tool like ChatGPT alongside an instructional command that requests the tool to make suggestions for how the text could be improved. Upon receiving the output, you can then work with the suggestions to improve your text. In this way, you are using AI with the level of agency to adopt or ignore its suggestions and enhance understanding of how text could be improved which can feed forward into future writing efforts.

I believe that AI copyediting is an efficient way to employ a sophisticated editing assistant without the financial price tag associated with hiring a human editor. Thus, using AI for copyediting also levels some of the privilege advantages that wealthier students (whether that be due to their university’s resourcing or private funds) have concerning hiring external assistance in the form of human editors. Hiring a human editor can cost thousands of dollars, whereas using AI tools for editing can be free, at least from the perspective of an individual’s outlay of financial capital. See Kate Crawford’s book Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligenc e for a deep dive into the wider costs associated with AI.

Appropriate Usages for AI Thesis Editing Tools

Concerning the email enquiry I received, I suggest applying editing features to streamline awkward phrasing, vary sentence structure, transition between ideas, adopt appropriate academic language and polish written work. I feel this is a legitimate use of AI tools that can move writing towards thesis-level quality while retaining your core ideas which is comparable to traditional human thesis editing and advising.

I also believe that leveraging the functions afforded by AI tools like summarising arguments to improve flow and transitions between chapters, catching minor grammar and punctuation issues, or paraphrasing very small sections of text for clarity represent responsible applications of AI.

Verify Your Institution’s AI Policies

As policies around AI usage continue to develop in response to rapid change, students wishing to use AI tools should keep an eye on their university’s guidelines. Some institutions already allow editing assistance when it’s accompanied by an acknowledgement. Please check your university’s guidelines!

Addressing Concerns Around AI Text Detection

Regarding fears over AI detectors concerning scholarly writing, I have a sustained scepticism that there will ever be a reliable or valid AI detection tool. For an interesting (and rather humorous) look at this issue, look at one Dr Lyndon Walker’s experiments with AI detection tools.

The AI Sniff Test

Many of us are operating in environments with an absence of clear guidelines on AI usage. I don’t blame universities for this situation. We are in a period of transition amidst the arrival of new technology and there are simply too many edge cases; academic integrity policies can never explicitly outline every single scenario in which AI might be used.

One way for students to navigate this period and make judgements about whether they should use AI to perform specific functional tasks that will contribute to the thesis document creation – whether that be generating entire paragraphs of text or using it to identify and fix typos – is the good old fashioned ‘sniff test’.

If something about using the tool’s output doesn’t seem right or makes you feel uneasy, listen to that inner voice – it often picks up on subtle signs of potential plagiarism or misconduct that should give you pause.

At the end of the day, you are submitting a thesis bearing your name. How you choose to use AI (if at all) to accomplish that task needs to be done in a way where you feel comfortable claiming authorship.

If in doubt, err on the side of caution.

If you ever feel yourself sliding into territory where it feels like AI tools are doing more work than you are, yet you’re claiming ownership of that work, it might be time to roll back your AI enthusiasm and save it to produce less career-critical outputs.

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Your studies

Regulations for the use of ai tools in thesis work.

The examiner for your thesis determines the extent to which and how AI tools are allowed in your thesis work. It's important to note that this decision may vary among theses, even when they share the same examiner.

Consequently, you may have permission to employ AI tools for tasks such as generating text, code, and conducting data analysis, among other applications. However, this is contingent upon executing these tasks in a responsible and transparent manner, as outlined below.

You are required to assume full responsibility

You are required to assume full responsibility for your work and must be capable of justifying the choices you've made regarding its content. This includes engaging in discussions about and defending the role AI played in shaping your thesis, demonstrating a clear understanding of how it contributed. When using AI tools in your thesis work, it's crucial to approach them responsibly, which means ethically, ensuring data integrity and academic honesty. This involves:

• Being cautious of over-reliance on AI, which includes the risks of uncritically accepting AI-generated answers that can be biased or wrong. For example, do not uncritically accept a rewrite of your text, code, literature review, or analysis of your data.
• Understanding the potential for plagiarism and copyright infringements. For example, GitHub Copilot has used copyright protected code when helping programmers to code, so always be vary of such problems.
• That the use of AI tools must be clear and transparent. At a minimum, provide a description detailing how and to what extent AI tools have been utilized in your work. If you have used AI generated content in you thesis, this should be cited as other works.
• Recognizing the need to secure sensitive or proprietary information. For example, you might have access to valuable information in the organization you are writing your thesis and sharing it with the AI tool might not be advisable.

Always consult with your supervisor

Always consult with your Chalmers’ supervisor or relevant industry/public sector collaborators before using AI tools if you have any uncertainties, particularly concerning data privacy and ethical issues. To date, Chalmers has licensing agreements in place with Bing Chat Enterprise, which include privacy arrangements.

How to Write a Better Thesis Statement Using AI (2023 Updated)

Table of contents

Meredith Sell

With the exceptions of poetry and fiction, every piece of writing needs a thesis statement. 

- Opinion pieces for the local newspaper? Yes. 

- An essay for a college class? You betcha.

- A book about China’s Ming Dynasty? Absolutely.

All of these pieces of writing need a thesis statement that sums up what they’re about and tells the reader what to expect, whether you’re making an argument, describing something in detail, or exploring ideas.

But how do you write a thesis statement? How do you even come up with one?

This step-by-step guide will show you exactly how — and help you make sure every thesis statement you write has all the parts needed to be clear, coherent, and complete.

Let’s start by making sure we understand what a thesis is (and what it’s not).

What Is a Thesis Statement?

A thesis statement is a one or two sentence long statement that concisely describes your paper’s subject, angle or position — and offers a preview of the evidence or argument your essay will present.

A thesis is not:

• An exclamation
• A simple fact

Think of your thesis as the road map for your essay. It briefly charts where you’ll start (subject), what you’ll cover (evidence/argument), and where you’ll land (position, angle). 

Writing a thesis early in your essay writing process can help you keep your writing focused, so you won’t get off-track describing something that has nothing to do with your central point. Your central point is your thesis, and the rest of your essay fleshes it out.

Get help writing your thesis statement with this FREE AI tool > Get help writing your thesis statement with this FREE AI tool >

Different Kinds of Papers Need Different Kinds of Theses

How you compose your thesis will depend on the type of essay you’re writing. For academic writing, there are three main kinds of essays:

• Persuasive, aka argumentative
• Expository, aka explanatory

A persuasive essay requires a thesis that clearly states the central stance of the paper , what the rest of the paper will argue in support of. 

Paper books are superior to ebooks when it comes to form, function, and overall reader experience.

An expository essay’s thesis sets up the paper’s focus and angle — the paper’s unique take, what in particular it will be describing and why . The why element gives the reader a reason to read; it tells the reader why the topic matters.

Understanding the functional design of physical books can help ebook designers create digital reading experiences that usher readers into literary worlds without technological difficulties.

A narrative essay is similar to that of an expository essay, but it may be less focused on tangible realities and more on intangibles of, for example, the human experience.

The books I’ve read over the years have shaped me, opening me up to worlds and ideas and ways of being that I would otherwise know nothing about.

As you prepare to craft your thesis, think through the goal of your paper. Are you making an argument? Describing the chemical properties of hydrogen? Exploring your relationship with the outdoors? What do you want the reader to take away from reading your piece?

Make note of your paper’s goal and then walk through our thesis-writing process.

Now that you practically have a PhD in theses, let’s learn how to write one:

How to Write (and Develop) a Strong Thesis

If developing a thesis is stressing you out, take heart — basically no one has a strong thesis right away. Developing a thesis is a multi-step process that takes time, thought, and perhaps most important of all: research . 

Tackle these steps one by one and you’ll soon have a thesis that’s rock-solid.

1. Identify your essay topic.

Are you writing about gardening? Sword etiquette? King Louis XIV?

With your assignment requirements in mind, pick out a topic (or two) and do some preliminary research . Read up on the basic facts of your topic. Identify a particular angle or focus that’s interesting to you. If you’re writing a persuasive essay, look for an aspect that people have contentious opinions on (and read our piece on persuasive essays to craft a compelling argument).

If your professor assigned a particular topic, you’ll still want to do some reading to make sure you know enough about the topic to pick your specific angle.

For those writing narrative essays involving personal experiences, you may need to do a combination of research and freewriting to explore the topic before honing in on what’s most compelling to you.

Once you have a clear idea of the topic and what interests you, go on to the next step.

2. Ask a research question.

You know what you’re going to write about, at least broadly. Now you just have to narrow in on an angle or focus appropriate to the length of your assignment. To do this, start by asking a question that probes deeper into your topic. 

This question may explore connections between causes and effects, the accuracy of an assumption you have, or a value judgment you’d like to investigate, among others.

For example, if you want to write about gardening for a persuasive essay and you’re interested in raised garden beds, your question could be:

What are the unique benefits of gardening in raised beds versus on the ground? Is one better than the other?

Or if you’re writing about sword etiquette for an expository essay , you could ask:

How did sword etiquette in Europe compare to samurai sword etiquette in Japan?

How does medieval sword etiquette influence modern fencing?

Kickstart your curiosity and come up with a handful of intriguing questions. Then pick the two most compelling to initially research (you’ll discard one later).

3. Answer the question tentatively.

You probably have an initial thought of what the answer to your research question is. Write that down in as specific terms as possible. This is your working thesis . 

Gardening in raised beds is preferable because you won’t accidentally awaken dormant weed seeds — and you can provide more fertile soil and protection from invasive species.

Medieval sword-fighting rituals are echoed in modern fencing etiquette.

Why is a working thesis helpful?

Both your research question and your working thesis will guide your research. It’s easy to start reading anything and everything related to your broad topic — but for a 4-, 10-, or even 20-page paper, you don’t need to know everything. You just need the relevant facts and enough context to accurately and clearly communicate to your reader.

Your working thesis will not be identical to your final thesis, because you don’t know that much just yet.

This brings us to our next step:

4. Research the question (and working thesis).

What do you need to find out in order to evaluate the strength of your thesis? What do you need to investigate to answer your research question more fully? 

Comb through authoritative, trustworthy sources to find that information. And keep detailed notes.

As you research, evaluate the strengths and weaknesses of your thesis — and see what other opposing or more nuanced theses exist. 

If you’re writing a persuasive essay, it may be helpful to organize information according to what does or does not support your thesis — or simply gather the information and see if it’s changing your mind. What new opinion do you have now that you’ve learned more about your topic and question? What discoveries have you made that discredit or support your initial thesis?

Raised garden beds prevent full maturity in certain plants — and are more prone to cold, heat, and drought.

If you’re writing an expository essay, use this research process to see if your initial idea holds up to the facts. And be on the lookout for other angles that would be more appropriate or interesting for your assignment.

Modern fencing doesn’t share many rituals with medieval swordplay.

With all this research under your belt, you can answer your research question in-depth — and you’ll have a clearer idea of whether or not your working thesis is anywhere near being accurate or arguable. What’s next?

5. Refine your thesis.

If you found that your working thesis was totally off-base, you’ll probably have to write a new one from scratch. 

For a persuasive essay , maybe you found a different opinion far more compelling than your initial take. For an expository essay , maybe your initial assumption was completely wrong — could you flip your thesis around and inform your readers of what you learned?

Use what you’ve learned to rewrite or revise your thesis to be more accurate, specific, and compelling.

Raised garden beds appeal to many gardeners for the semblance of control they offer over what will and will not grow, but they are also more prone to changes in weather and air temperature and may prevent certain plants from reaching full maturity. All of this makes raised beds the worse option for ambitious gardeners. 

While swordplay can be traced back through millennia, modern fencing has little in common with medieval combat where swordsmen fought to the death.

If you’ve been researching two separate questions and theses, now’s the time to evaluate which one is most interesting, compelling, or appropriate for your assignment. Did one thesis completely fall apart when faced with the facts? Did one fail to turn up any legitimate sources or studies? Choose the stronger question or the more interesting (revised) thesis, and discard the other.

6. Get help from AI

To make the process even easier, you can take advantage of Wordtune's generative AI capabilities to craft an effective thesis statement. You can take your current thesis statement and try the paraphrase tool to get suggestions for better ways of articulating it. WordTune will generate a set of related phrases, which you can select to help you refine your statement. You can also use Wordtune's suggestions to craft the thesis statement. Write your initial introduction sentence, then click '+' and select the explain suggestion. Browse through the suggestions until you have a statement that captures your idea perfectly.

Thesis Check: Look for These Three Elements

At this point, you should have a thesis that will set up an original, compelling essay, but before you set out to write that essay, make sure your thesis contains these three elements:

• Topic: Your thesis should clearly state the topic of your essay, whether swashbuckling pirates, raised garden beds, or methods of snow removal.
• Position or angle: Your thesis should zoom into the specific aspect of your topic that your essay will focus on, and briefly but boldly state your position or describe your angle.
• Summary of evidence and/or argument: In a concise phrase or two, your thesis should summarize the evidence and/or argument your essay will present, setting up your readers for what’s coming without giving everything away.

The challenge for you is communicating each of these elements in a sentence or two. But remember: Your thesis will come at the end of your intro, which will already have done some work to establish your topic and focus. Those aspects don’t need to be over explained in your thesis — just clearly mentioned and tied to your position and evidence.

Let’s look at our examples from earlier to see how they accomplish this:

Notice how:

• The topic is mentioned by name. 
• The position or angle is clearly stated. 
• The evidence or argument is set up, as well as the assumptions or opposing view that the essay will debunk.

Both theses prepare the reader for what’s coming in the rest of the essay: 

• An argument to show that raised beds are actually a poor option for gardeners who want to grow thriving, healthy, resilient plants.
• An exposition of modern fencing in comparison with medieval sword fighting that shows how different they are.

Examine your refined thesis. Are all three elements present? If any are missing, make any additions or clarifications needed to correct it.

It’s Essay-Writing Time!

Now that your thesis is ready to go, you have the rest of your essay to think about. With the work you’ve already done to develop your thesis, you should have an idea of what comes next — but if you need help forming your persuasive essay’s argument, we’ve got a blog for that.

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• 26 June 2024

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

• Dashun Wang 0

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

You can also search for this author in PubMed   Google Scholar

You have full access to this article via your institution.

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

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

Revealed: the ten research papers that policy documents cite most

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

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

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

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

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

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

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

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

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

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

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

Nature 630 , 794 (2024)

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

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

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How should a faculty deal with the problem of Artificial Intelligence (AI) generated texts?

For many years one of the main problems for academic essays and BA/MA/PhD theses was plagiarism. However with the recent advent of artificial intelligence (AI) that can write high quality texts (e.g. ChatGPT) a fresh concern has risen: was a text written by a student or by an AI?

Plagiarism scanners do not detect AI texts (I tried it!) because they are not copied and pasted. Even if you have a certain suspicion that a text might be written by an AI (e.g. because an overall direction in the text is missing or the text is just too good to be true given the student's previous essays) there is no real way to prove the suspicion.

How to deal with this new problem? Yesterday we had a faculty meeting where we discussed this issue of AI texts but no real conclusion - so I was thinking of getting some input from here ...

• generative-ai

• 1 Comments are not for extended discussion; this conversation has been moved to chat . –  Massimo Ortolano Commented Dec 16, 2022 at 17:02
• TurnItIn seems to have made some advances in this area: universitybusiness.com/… . –  G. Allen Commented Mar 1, 2023 at 17:59

18 Answers 18

One general approach is to make sure the assignment is not bullshitable .

ChatGPT generates bullshit , i.e. text that attempts to resemble genuine responses to the prompt, but does not attempt to actually be correct. The AI does not generally understand what it means to be correct, it just produces relevant text (relevant text is often correct, but often incorrect).

Unfortunately, many students use a similar process, so it can be hard to distinguish. So my suggestion is to design prompts where bullshit results in a bad grade (and is easy for you to detect), even if an automated tool cannot detect cheating.

ChatGPT uses nonexistent, made-up references . Ask students to submit references in a format that can be easily spot-checked.

Ask students to provide drafts of their work, incomplete versions showing their thought process, and maybe errors they made on the way.

Avoid open-ended prompts like "write X words about Y".

Prefer prompts with a global goal and require each local part of the essay to contribute to that goal. Take off points in the rubric for parts of the essay that do not clearly support the global goal. I.e. take off points for rambling or unnecessary material. Take points off for good material if it is not shown how that material contributes to the global goal.

Do not prompt for content for content's sake. Instead of a minimum word count, give a maximum word limit and require students to accomplish a definable goal within that limited space.

Caveat: I haven't tried this, my background is more in AI and less in essay assignments. Also, this answer is focused on essays because this kind of cheating on a longer text, like a thesis, should be much easier to detect.

Example reference with similar opinion: ChatGPT Is Dumber Than You Think (The Atlantic)

• 59 To rephrase in a less flattering way: if a ChatGPT generated answer would result in a good mark for your homework assignment, the problem is with the tasks assigned for homework, not with the student that used ChatGPT. –  quarague Commented Dec 16, 2022 at 9:22
• 11 I was trying to explain to a colleague why I was actually quite excited about ChatGPT's emergence, and this articulates it very well - a forced push towards productive, shorter essays, which I hope also sets people up to write more clearly in their future papers. Fewer bullshit exams or assignments, simply because it's trivial to generate answers for them. I think we are going to need to teach writing to a higher standard, but, honestly? I think that's a great thing for research. –  lupe Commented Dec 16, 2022 at 14:30
• 10 The problem with requesting drafts is that not all of us write (or keep) drafts. –  Mark Commented Dec 17, 2022 at 4:08
• 6 Non bullshitable assignments might actually end up wrecking a bunch of humans. A lot of people get through most of their essay classes writing bullshit to a degree (I wish there was a non vulgar term for this but I know what you mean). ChatGPT and successively better AI writers are effectively already displacing this class of student –  Sidharth Ghoshal Commented Dec 18, 2022 at 3:15
• 9 @SidharthGhoshal I consider that a feature, not a bug... The fact that some students survived only writing BS was already a problem in the education system, and this is just forcing us to face the problem and solve it. –  Denis Nardin Commented Dec 18, 2022 at 15:48

You ask the writer about the text

Frame change. You are not worried about computers writing papers. You are worried about people falsely claiming to be writing papers.

If a human writes a paper for another human you have the same problem. And the same solution : ask the writer or interview them about the text.

A human that generates parts or entire papers automatically will have a very hard time explaining said parts.

• 6 This is a great answer. Do we need new rules to account for AIs doing things? I don't see why. The problem of AIs is no different than the problem of humans. How do you know a student didn't subcontract their paper from someone in Bangladesh? How do you know they wrote it themselves? I don't know the answer to that, but it's an old question, and the AI problem is just the same old question. –  JamieB Commented Dec 16, 2022 at 19:33
• 4 To clarify: Are you suggesting that the instructor interview every student in the class, or just the suspicious ones? –  Dan Commented Dec 17, 2022 at 0:54
• 7 Remark (also for the commenters): note that there are countries where exams with an oral part have always been the norm. Yes, for everyone. –  Federico Poloni Commented Dec 17, 2022 at 8:36
• 2 @EthanBolker yep, the problem with this answer is that it doesn't scale. Sure you can do this for a small classroom with an occasional writing assignment. But no one is going to routinely do this for bigger classes with routine writing assignments. –  eps Commented Dec 17, 2022 at 23:04
• 5 @JamieB this completely ignores scale, there is a GIGANTIC amount of difference in perceived morality, money, time, etc etc etc in firing up chatgpt and outsourcing a writing assignment. It's like comparing the ENIAC with a smartphone. yes, they are both computers, but at some point the differences are so big it becomes a different thing entirely. People could always cheat but this changes the game in big ways. It also begs the question of how this stuff should work -- if you can use chatgpt to generate 90% of the paper and all you have to do is a little cleanup .... shouldn't you do that? –  eps Commented Dec 17, 2022 at 23:12

An interesting idea on this topic comes from Ben Thompson at Stratechery: instead of banning the use of AIs, require it - the students' job is not to produce an essay, instead they have to check and correct what the AI says about the topic.

A quote from his article :

Imagine that a school acquires an AI software suite that students are expected to use for their answers about Hobbes or anything else; every answer that is generated is recorded so that teachers can instantly ascertain that students didn’t use a different system. Moreover, instead of futilely demanding that students write essays themselves, teachers insist on AI. Here’s the thing, though: the system will frequently give the wrong answers (and not just on accident — wrong answers will be often pushed out on purpose); the real skill in the homework assignment will be in verifying the answers the system churns out — learning how to be a verifier and an editor, instead of a regurgitator.

Ben argues that the skills required to check and correct the unreliable AI's output are more relevant in the modern world than the skills required to write an essay in the first place. Perhaps as a bonus, the students will also put 2 and 2 together to realise that if they have an essay to submit to a different teacher, they can't expect ChatGPT to write a decent essay for them.

• 5 An extension this would be a question along the lines of "I asked an AI to write two different answers to the question '.....' discuss these answers, the ayers and weakness of each, and which is better" –  Ian Sudbery Commented Dec 19, 2022 at 15:01
• 1 This is the only correct, non-Luddite answer. Fighting the future is futile, instead we should embrace it. –  JonathanReez Commented Jan 6, 2023 at 5:50
• @JonathanReez "This is the only correct [...] answer" - sorry, that's patently not true. The ability of doing quick estimates in the mind rather than relying on the pocket calculator comes in useful everytime when you have to check whether something is in the right ballpark. And I am not talking just science. Knowing what an increase/reduction of 2% of your salary means is a very useful skill to have. Thus, knowing how to do approximate arithmetics without a calculator is a critical skill even if everyone carries a powerful computer with them at all times. So is writing. –  Captain Emacs Commented Mar 11, 2023 at 15:13
• @CaptainEmacs The goal of the university is to produce members of society who can produce useful intellectual work. How they produce said work should be of zero relevance. –  JonathanReez Commented Mar 11, 2023 at 21:50
• 1 @JonathanReez I don't think there's any disagreement about your first sentence, but the "how" is relevant insofar as there are methods and practices which are likely to produce better intellectual work and one of the roles of higher education is to instil those practices. What I think you and Captain Emacs disagree about is the scope of those practices. Personally I would tend to agree with you that some of the skills we're currently expecting students to learn are going to be unnecessary by the time they would be relevant. But I also think it's better to learn something that might not be ... –  kaya3 Commented Mar 11, 2023 at 22:03

I want to raise three points to guide the discussion, but they're too long for a comment, so an answer it is:

• We should assume NLP models will continue becoming 1. better and 2. widespread. Consequence of (1): it will eventually become impossible for the well-trained human eye to sense that an essay was written by a machine. Consequence of (2): the solution of a "textual fingerprint" for identifying an auto-generated text, as mentioned above, won't be feasible. Currently, this could still work, because there is only one company offering one model for generating ChatGPT-quality text, namely OpenAI itself. Fast-forward 2 years, and there will be many such companies, perhaps even explicitly aimed towards high-schoolers. In that case, a teacher would have to go down the list verifying the fingerprint with each service, and such a service would be foolish (in their own market interest) to offer such a service in the first place.
• We should view this in the broader and existing context of ghost-writing. Some take solace in being able to discern the "bullshittery level" of ChatGPT, but this is tackling the wrong problem. For decades, students have paid others to write assignments for them, and obviously, the task of detecting those texts has nothing to do with detecting that the resulting text was human-generated, because it was -- just not by the right human.
• Some have claimed that something like a thesis cannot be generated by a machine, because they "cannot be creative". We should refrain from these kinds of statements, because 1. even search algorithms can be creative, and 2. there are, sadly, many disciplines in academia where innovation hardly exists and bullshittery is the entire game . Ask ChatGPT to write an opinion piece, and it'll give you an essay at high-schooler level. Then ask it to do it in an academic style with complicated words , and you've entered the domain of master's theses (or higher!) in some domains.

Perhaps the take-home opinion essay is just dead, and the future consists of technical reports and intensively researched term papers. Perhaps a fingerprinting system will be the future, but it will be the students whose capacities will be fingerprinted and tracked through time, not the texts.

• 3 great answer, it really amazes me how many people think this tech is going to always be controlled by well meaning researchers. and how many people that don't realize how quickly this tech is evolving. –  eps Commented Dec 17, 2022 at 23:42

While the idea of ChatGPT writing entire MA/PhD theses is certainly entertaining, right now the technology just isn't at that level. It can write multiple paragraphs at a time, and maybe a human can manually piece those together into a proper "paper", but it's limited in what the human can feed in. It's really a glorified chat bot, and it's not designed to write essays for students. For example, there's no supported way you can feed in an article/story and have ChatGPT respond to it. Moreover, there are simply limits to ChatGPT's semantic understanding.

In the future I suppose some companies might make a 'nefarious' version of ChatGPT and monetize it, considering all the various companies we already have that monetize academic dishonesty. When the technology is more at that level, hopefully there will be new tools on the detection side that can deal with the issue.

• 1 I can't comfortably let ChatGPT do something for me. –  Neuchâtel Commented Dec 15, 2022 at 15:49
• 1 This is a great answer. The biased samples introduced to the public are just that. "AI" is a bubble. Wheres my self driving car? Lets see AI beat Zelda 1. Lets see it. Anyone who works hands on at a low level fundamentally understands the algorithm only does what its told, diametrically opposite the process of scientific discovery. –  user156207 Commented Dec 15, 2022 at 16:59
• 21 Perhaps the answerer doesn't know how bad, in fact, is the writing of some college undergraduates. –  workerjoe Commented Dec 15, 2022 at 21:49
• 8 I think the point about things not being in the training set is a red herring. The training set probably doesn't include any movie scripts where the Teletubbies fight Superman, but it can generate those just fine. You might not be able to prompt it with a whole article, but you can take the key points from that article and form a prompt that gets you some response, then prompt it further to get it to change its response. (I first got a script where Superman is the good guy, then I asked it to make Superman the bad guy, then I asked it to also make the Teletubbies win.) –  kaya3 Commented Dec 16, 2022 at 0:11
• 4 Besides, most essay-style assessments are not asking the students to produce any new knowledge - for PhD theses of course, but for coursework at undergraduate level the "training set" very likely includes previously-written essays about the same topic. The main reason that we don't need to worry just yet, I think, is that ChatGPT tends to produce a lot of rubbish that only resembles good academic work in superficial form, not in substance. –  kaya3 Commented Dec 16, 2022 at 0:17

One might want to return to writing in-person essays. This would only apply in situations where the length and time involved made that appropriate, but for certain assignments (not theses!) one could imagine either taking class time for this, or proctoring in some fashion. Particularly as (current!) AI seems to "excel" with fairly short-form material, giving people 20 minutes to write such a short assignment in person may be a viable way to deal with it.

(That said, I like usul's answer better, and have used it for many years for certain longer assignments.)

• Whatever happens, it needs to happen in class. Like maybe teaching them instead of them learning on their own by doing homework. –  Mazura Commented Dec 17, 2022 at 2:45

Sort of an extension to André LFS Bacci's answer : consider an oral exam. It's not going to be ideal since oral exams have their own set of problems, but ChatGPT can't fake an oral exam, and neither can Chegg.

You could also use an oral exam as a check to see if the student wrote the work. It won't prove an AI wrote the essay, but it could indicate the student did not write it.

• 7 Difficult to do oral exams for 600 students in each of the 10 modules they take in a year. That's 6000 peak exams per year group, per year. –  Ian Sudbery Commented Dec 19, 2022 at 14:48

The International Baccalaureate (IB) is an international high school school curriculum used by many international high schools. Although it is for high school, one part of it can provide insight to the OP: the " Extended Essay " (EE), which is basically a 4,000 word essay.

For every student, the EE research and writing process includes three formal 20-30 minute "reflection sessions", which are interviews between the student and teacher, the purposes of which are to support the student's learning and progress, and to check for authenticity , i.e. to check if the essay was actually written by the student.

• In the first reflection session, they discuss the student's proposed research question.
• In the second reflection session, they discuss the student's first draft.
• In the third reflection session, they discuss the student's second (and final) version.

This system has been used by the IB for a long time, and it seems to work, as far as checking for authenticity. Perhaps a modified system could be considered for university.

• 5 Yes, indeed, but quite labor-intensive. I think the questioner wants to know how do do this more "automatically", ironically to filter out "automatic" writing. :) –  paul garrett Commented Dec 17, 2022 at 2:23
• @paulgarrett I did not get the impression that the OP is only interested in quick fixes. –  Dan Commented Dec 17, 2022 at 5:26
• 1 @paulgarrett ... "quite labor-intensive". But, in the future, we can get computers to do those interviews of the students. :) –  GEdgar Commented Dec 26, 2022 at 13:15

The question points to an underlying development that was identified by Ray Kurzweil: The continuing application of the No True Scotsman argument to intelligence. What we consider "truly" intelligent has changed with the advancing capabilities of machines, on the grounds that a task that can be mechanized is by definition not a sign of "true" intelligence. Far into the 1900s, playing world class chess or being able to translate reasonably well between a dozen languages would have been considered a sign of the highest intelligence. So would have been the ability to write reasoned essays in college grade English about almost any topic known to mankind (which is what ChatGPT does, of course).

The progress made in information technology shows us that all these tasks can be done by mechanisms. Nobody in their right mind, apart from the occasional excited google engineer, would claim that these mechanisms are "truly" intelligent. Because we don't think of ourselves as mechanisms, we backtrack and change our classification of what we consider "truly" intelligent. Everything that is rule-based is obviously not truly intelligent: You can beat it with stupid brute force. Everything that is simply based on pattern recognition is not truly intelligent: There is no true "understanding" and no "originality". But alas: The texts are good enough to earn certificates and pass German college term tests . And this is just a prototype.

There are a couple conclusions here. We can either backtrack further and say:

• Much, if not most of what we do in academia is not "truly" intelligent. The amount of original, creative, essentially unpredictable work is small.
• Much of what we do professionally (and what college education prepares us for) is not truly intelligent work. Programmers, radiologists, lawyers: Just pattern recognition and -application.
• Our education and our professions are on the brink of being obsolete.

This is scary.

Or we hold our ground and continue to consider our education and professions at least somewhat intelligent. Then we cannot deny that we have produced intelligent machines. The google engineer was right. Rather sooner than later machines will be able to do any intellectual task we can do, including the ones one might currently consider original, creative, and essentially unpredictable. They will probably be able to perform them better than we do. In fact, they will probably become able to perform intellectual tasks that are entirely beyond our regular reach. 1

This is even more scary.

My guess is that we will take a much bigger step back than ever before: We will redefine what it means not to be intelligent but to be human. We will be forced to realize that intelligence is not what defines us as human. It is probably not even art that defines us, or only insofar as art defines us as individuals. 2 Instead, it is emotions: Love, compassion, passion, even hate. Machines are unable to feel and will be unable to feel for the foreseeable future.

P.S. Are you still waiting for an answer to your question?

• Using ChatGPT is not any more plagiarism than using a pocket calculator. If it gets you results, it's a useful tool.
• Therefore, don't be a Don Quixote. Instead of a futile attempt at preventing the use of ChatGPT e.a., embrace it. Kaya3 wrote an answer in this direction. The future of academia and humanity lies not in defending the indefensible but in employing the useful.
• Change the curriculum to stay relevant. Nobody teaches how to manually draw a root in algebra any longer, or matrix tricks. Try to teach things which may be hard for AI even in 20 years.

1 I'm not necessarily hinting at the prospect of a technological singularity which often involves an unpleasant quasi-religious sentiment; a much weaker development would suffice: Machines continue to improve on cognitive tasks (like recognizing cancer cells, designing mechanical things, predicting the weather, making investment decisions, driving a car). We increasingly find that they do it better than we typically do, and we increasingly rely on them. This is a gradual development without tipping points of any kind. (It is funny that the proponents of a singularity recognize that technological development is exponential but fail to see that exponential curves are emphatically void of singularities; quite to the contrary: They look the same everywhere. The discovery of fire, advent of agriculture or the industrial revolution have disrupted societies much more than a mechanical lawyer or programmer ever could.)

2 As today, different individuals would produce different art. This would include mechanical individuals, i.e. different neural nets, or differently trained neural nets (the equivalent of separately raised identical twins). Like today, experts (including, of course, mechanical experts) would be able to make an educated guess which individual (including, of course, mechanical individuals) created a given piece of art, or at least which tribe and era it is from (e.g. 17th century Flemish, 19th century Xhosa, or 202x Dall E3 lineage).

At the moment, and as a minimum, you need a clear policy on its use, perhaps forbidding its use. That isn't enough, of course, but you need to make it clear. That will ameliorate the problem slightly, as most students will comply if the policy is stated in a reasonable way that emphasizes the learning goals. The reasoning behind a policy needs to be made as clear as the policy itself (as usual).

Mid term, it is likely that AI solutions will emerge that can catch the use of such things with fair accuracy, maybe even good accuracy. Some are already underway. The might even provide a good balance between false positives and false negatives. The former might be handled if students were always subject to a follow up oral presentation of essays and such as is done with theses.

I think that the development of a detection tool is a worthwhile AI research project at the moment.

Long term, it is harder. I don't think (but am not certain) that ChatGpt in particular tries to obfuscate its use, but that can happen. At some point, we may just need to completely change the techniques we use to encourage and evaluate student honest work. It is worth spending some time with that now, and trying out ideas. Oral exams don't scale well, but are harder to misuse.

At the moment the AI text generation isn't very creative and a careful reading might catch a lot of it - especially for longer texts. But AI generated work and poor but honest work might be harder to distinguish.

As I understand it, ChatGpT doesn't have access to the internet (say, Wikipedia). That would change the game, perhaps, but might also make plagiarism detection easier.

I'll note that forbidding its use might not be the only proper policy. Use with citation might be considered as you develop such a policy.

• 1 I had downvoted this as theoretical. The line drawn for using writing aides is arbitrary. How is it any better if a student gets writing help from a human? The human is better at it and provides more contribution to a written article. The technology for material contribution does not exist. Enforcement of any policy is practically impossible. The entire discussion about regulation is a giant waste of time. Its planning for something that cannot happen in reality. –  user156207 Commented Dec 15, 2022 at 17:17
• 5 @user156207, note that they are already banned here with no really effective enforcement mechanism. And see arxiv.org/abs/2011.01314 , helpfully provided by user/mod cag51. Look at academia meta for more. –  Buffy Commented Dec 15, 2022 at 19:46
• 4 @user156207 you made your opinion quite clear enough with one of those comments, no need to repeat it. Anyway, a) lots of people, including experts of the domain, strongly disagree that there will be no problem “in our lifetimes” b) even if there was only a low probability of this happening, it would be haphazard to not prepare for it anyway c) already today ChatGPT is a problem – not in that it actually generates serious scientific content yet, but in that it easily generates such a quantity of convincing-looking bogus that it risks overloading the human gatekeepers. –  leftaroundabout Commented Dec 15, 2022 at 20:02
• 1 @leftaroundabout just following up buffys questions. Im not saying im right. Im saying i disagree. I feel those "experts" are selling you something. I myself have expertise in prediction. I myself have seen a lot of promises that arent being kept. Alas. What are your preparations for my cat doing my homework in trigonometry? –  user156207 Commented Dec 15, 2022 at 20:06
• 1 @Magma, actually I recommend a policy concerning its use , not one forbidding its use necessarily. But some uses probably need forbidding, as plagiarism, though a bit weird here, is still a problem. –  Buffy Commented Dec 16, 2022 at 19:38

In a few years, when current students enter the workforce, it seems likely they will be using AI-based language tools like they are using Google today. They will be using these tools to access existing knowledge, to polish the presentation of their results, to translate text that they have written in their native tongue into the language they are supposed to work in, or maybe even as research assistants that contribute genuinely new results. Their tools will likely be vastly superior to what is available now. I would argue we should prepare our students for that future.

With that in mind, I think a scalable and relatively future-proof way of dealing with AI assistance is to supply an AI-generated response to a homework assignment as part of the assignment . The instructor would set an essay assignment as usual, but in an additional step they would also use the best model they can get their hands on to auto-generate a response, maybe in an iterative fashion where the model first generates an essay outline and then fills in the chapters. They would then add the process they used to generate the essay to the methods section of this example essay. The students will then be graded on whether and by how much they managed to improve on this baseline. A brief critique and grading of the baseline could maybe be included by the teacher as well to show the students in what ways the AI-generated text is still failing.

In this way, the topic of AI assistance can be openly discussed in class; students get naturally exposed and can discuss different ways to use AI; they can discuss whatever limitations current AI still has; they learn about attributing credit; and they have to learn and ultimately demonstrate how to do better than the output of a current state of the art public model. Compared to schemes that rely on oral examinations, additional teacher workload should also be relatively low, as the baseline essay they will be producing will be produced in a highly automated way.

One IT website has called ChatGPT “Dunning-Kruger as a service”. It creates very convincing bullshit.

It’s very annoying and you might need to train people up to detect it (the convincing nonsense, not that it was created by an AI), but for a while at least you can judge these submissions just by their quality.

• You could also call it, "Ask Wikipedia". I very strongly doubt that the content of the bot's output on any topic will vary from what Wikipedia already has to say. –  EvilSnack Commented Dec 19, 2022 at 4:18

Just one small aspect: For student papers require the bibliography to also include YOUR library's call numbers for books and the addition of the link used for online access of journal articles or the call number if accessed print. This is also good for ghostwritten work, as it makes the ghostwriter have to use your library, upping the cost. ChatGPT will just write bullshit, or forget to add this. I know this is not standard for publications, but for training writers you get them to show the library work they (supposedly) did.

Ask the AI questions about the subject of the assignment. If you ask enough questions, you will gain a good idea of the AI's limitations, and also a good idea of the AI's writing style.

Then in the assignment, require the student to answer a question that the AI was unable to answer.

The answers you receive from the students will show the AI's limitations and style if they are using the AI, otherwise they will at the very least be the work of a live human being.

• 1 Example of a usable essay prompt that IA cannot legitimately respond to? –  Dan Commented Dec 17, 2022 at 2:37
• 1 That would depend on the topic and the level of knowledge you want to see. –  EvilSnack Commented Dec 17, 2022 at 2:41
• Topic: Free will (from Philosophy). Level of knowledge: basic understanding of the standard arguments for and against free will. But I don't know what your area of expertise is, so it may be better for you to choose your own example. I think an example would strengthen your answer. –  Dan Commented Dec 17, 2022 at 3:08
• 2 this answer assumes humans won't eventually learn to mimic AI generated text (which is already happening because of text choice assistance apps). it also assumes there wont' be different ai's with different types of voices and writing styles. –  eps Commented Dec 17, 2022 at 23:45

Perhaps a direct inquiry to the student, such as "did you write this yourself" would provide the desired response.

• If all students were honest, no exams would be needed... –  Trang Oul Commented Mar 28 at 15:05

Going along with EvilSnack's answer, perhaps this should be seen as an opportunity rather than a threat. There are some reflections here about how schools used to view Wikipedia and similar sites. While there is certainly backlash against the increasing use of AI, unscrupulous people are already seeking to advance its use past the point where we can tell it is used. News articles, social media posts, forum messages, and even telephone calls are already hosted by AI bots.

Maybe have students generate an article or essay, and run them through the process of proofreading, fact-checking, and editing. Changes should be tracked along the way (a feature that's part of any decent word processor these days), and the students should be prepared to defend their actions and choices. This could also be a collaborative assignment.

Along the way, the kids may notice peculiarities and biases that we might not, and thus become better critics of AI-generated copy than we could hope to. This should also help inform them on the topics of misinformation and deception.

Even if AI is not widely adopted, academia should always approach these sort of matters thoroughly, rather than immediately regarding them as a challenge to tradition.

(I'm adding another answer because it's fundamentally different from the other one I wrote)

There are tools available now to detect AI-written text. One way is to get another AI to classify the text; the other way is to add a watermark to the AI-generated text. Watermarking is more sophisticated than physical watermarking; it modifies the output text (by the original text-generating AI) in a way that is detectable by computers, but not by humans.

See sources for classifiers and watermarking respectively.

Students are expected to understand grammar and punctuations but grammarly is allowed and even recommended by many tutors.... students can use grammarly to improve the quality of writing. If grammarly was embraced why shouldn't other AI advancements be embraced. If 20 student's out of 100 use chatgtp to answer the same question won't turnitin flag some of them for plagiarism? Even when you use chatgtp you still have to read and edit the output ... you have to assume the answer you are getting has not been submitted elsewhere... It cannot write 15000 words dissertation or even 5000 words essay asking the student to analyze a case study... The student will need to ask several questions to get a good word count and also proof read and also format references.... At masters level Chatgtp is just like advanced Google .... It gives you the information you need instead of having to search multiple websites... But you still have to study the output and make it align with your intentions... The output will not always be original as more people use it and as more students submit to turnitin database or safe assign... Schools should wait before implementing any policy... Despite turnitins claim students are able to beat it using paraphrasing softwares... Turnitin is just another company trying to sell it's services... Institutions should not always jump at every service or product or update offered by the company.... PowerPoint slides with narration cannot be done by chatgtp... Student input is still needed... The process of creating the slide is the learning process it makes no difference whether the answer was provided by chatgtp Google or school library...

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Guest Post: AI Meets Academia—Navigating the New Terrain

James Bedford on how he’s using AI to help translate the ways of academia to students.

By  John Warner

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If you’ve been reading my posts here, you know that I am an AI skeptic when it comes to it being a good tool for teaching and learning. While I am not an abolitionist, my focus from the beginning has been on designing experiences and assessment strategies that make AI irrelevant to the work we ask students to do. I see it as a tool oriented around efficiency and productivity, not learning.

But I also know there are other people who share many, if not most, of the same values around teaching and learning who are experimenting with generative AI tools with the goal of helping improve their students’ engagement with the work of higher education. James Bedford is one of those folks, and when he told me about some of his experiments with AI in the classroom, I thought it would be useful for others to hear his perspective, the conditions from which it comes, and how it differs from my own. —John Warner

By James Bedford

The idea of universities ‘embracing’ AI technology has been met with justified concerns about students’ critical-thinking skills and whether they’ll develop an over-reliance on these tools. When ChatGPT shut down briefly this month, there was a flurry of concerned  posts on X  (formerly Twitter) which could be a sign of things to come. But one thing is certain: There is a growing divide amongst educators on whether AI should have any place at all in the lives of university students. 

It’s important we have these discussions as we still don’t know what the long-term impacts of AI will be—in particular, what it could mean for the environment, or individuals’ mental health and well-being. Despite the potential risks, I find myself taking a more neutral standpoint (something I tell my students never to do). When it comes to the presence of AI in education, I have seen both the negative implications for student learning, and also, how AI ‘tools’ are helping demystify and increase accessibility for students trying to engage in the scholarly practices required to obtain a university degree.  

Those who have been teaching academic skills for any period of time have noticed a disparity between what is expected from students and what students actually know. This gap exists, and it can leave students floundering, unable to see or understand what is required of them in their work and assessments. The lack of transparency about student expectations isn’t just a minor hurdle either—it fundamentally undermines student confidence and hampers their ability to engage with academic tasks meaningfully. Help-seeking behavior is good, but not great. Students are then set up to make a bunch of mistakes (which is fine, but usually reflected in their grades) and a major part of the university experience for students becomes a matter of adjusting to these largely unspoken expectations around academic writing and research behavior.

Let me give you an example. As an academic learning facilitator, one of the supports I offer is the one-to-one consultation. Students come from various faculties, skill levels and stages of the university lifecycle with questions about how to write an essay, or perhaps, how to improve their writing. The thing about these consults is that I’m not there to mark their work, I’m there to listen and help ‘facilitate’ the students’ own learning.  As a result, students seem to be more open and honest about what they are struggling with. I see and hear about all kinds of things—from poor or harsh feedback from their instructors to how students are actually using AI tools, for better or worse.

What I have learned from my work with students over the last 10 years is that many educators seem to forget the following: 

• academic skills aren’t explicitly taught and these skills are assumed knowledge.
• there is a stigma around asking for help relating to these skills because of the above.

Students often feel embarrassed asking for assistance relating to academic skills. This is due to feelings of inadequacy around not knowing. For example, it is expected students come to university already possessing the skills to write university standard essays or conduct literature reviews, when in fact, it might be the first time they’ve ever completed such tasks. Most have never accessed a journal database or even been taught how to write a proper thesis statement. Additionally, the methods they were previously taught might not align with the expectations of their institution. 

The written feedback ‘we’ provide can also get in the way of things, messing with a students’ confidence and their learning. A few of my favorite and far too common examples of poor feedback from educators are the classic three question marks  ‘??? ’ or ‘ Awk’  dragged and dropped next to sentences that the student has struggled to articulate. Or perhaps the all too common ‘ Ref ’ next to a citation error (a full stop out of place, or perhaps the wrong use of italics). I spoke with a student recently who had lost all confidence in their academic writing ability after their first year toward a master's degree. One of their professors made the comment that their writing appeared to be at a high school level. Whether or not the comment was accurate (it wasn’t) is beside the point.

What I’m getting at is, when it comes to course work, educators can often forget what it’s like to know next to nothing about their subject. We expect students to become mini-experts on a topic in a matter of weeks, while juggling multiple other courses with multiple assessments with almost no discussion of the workload between these courses or when assessments are due. We then scrutinize their performance which is tested, graded, and measured through assessments that are largely broken, in a system that is barely coping due to the record-high student-to-teacher ratios and extensive casualization of the workforce. And then we have the nerve to tell students what tools they should and shouldn’t use to support their studies. 

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Of course, the alternative isn’t good either. We don’t want students mindlessly adopting these tools in ways that are counterproductive to their learning. There are already a flood of influencers pushing AI products onto students with ads on Instagram and Tik Tok. If universities are going to continue assuring the public that their graduates have met the required learning outcomes of their programs, we need to consider how students might be using these tools to bypass learning altogether. This means we absolutely must have people who are thinking critically about widespread adoption and who are holding ed-tech companies up to immense scrutiny. 

However, showcasing the capabilities, and lack thereof, around generative AI applications has become an important skill students need to develop. Doing so empowers them to make their own informed decisions about appropriate-use cases and enables the development of critical-thinking skills essential for any responsible adoption of these tools. Students need agency in order to decide for themselves when engaging with AI tools might enhance their learning, and crucially, when they might not. 

One of the great advantages of exploring these products with students is that it often leads to them wanting to develop their own skills further, particularly when they see the limitations and drawbacks in the outputs these tools provide. When I demonstrate one of the many AI research tools like  Elicit  or  Consensus  in workshops, the reaction from students is mostly positive. There are sighs of relief as I demonstrate how the user experience is embedded into some of these applications, making them far more accessible and easier to navigate than your standard scholarly databases. In addition, the ability to search articles with  vector search technology , allows you to locate relevant papers without relying solely on keywords. Many students in their first year of university find using standard databases overly complicated and unnecessarily confusing. AI research tools help them, at least, begin to navigate a landscape that has always been notoriously difficult for them to engage with. 

Based on multiple surveys I’ve run in workshops over the years it appears around 40 to 50 percent of students are using AI to support their studies, whereas for students with English as an Additional Language (aka English as a Second Language) it seems to be closer to 70 to 80 percent. These numbers should not be representative of any student population but do seem to be fairly consistent with other studies I’ve seen. While there are always going to be students who will choose to actively avoid using these tools altogether, and for various reasons, others find using AI tools can support their learning in productive and helpful ways.

Of course, for all the fantastic things these tools can do, nothing will ever replace human reasoning and intuition (hopefully). Research is about finding a unique narrative thread hidden between all the papers you’ve been taking in, which is very difficult to do if you’re incapable of understanding and are without human experience. But before dismissing the use of AI research tools, it’s useful to consider the expectations placed upon students already, and to understand why they might see these tools as useful. The requirements to produce decent academic writing and research seem absurd given many of these tasks are prescribed to undergrads who have little to no scholarly research experience. Students tend to ‘perform’ these tasks in a vacuum. For the most part, many of them will never have to write essays ever again.

If we are going to assign research-related tasks to students, then we should expect some of them to engage with these tools at some point. Engaging with these tools doesn’t have to reduce their capacity to engage with academic skills either. In many ways, using these tools well has become a new academic skill.

When considering whether to allow students to use generative AI (GenAI) tools for research, I always refer to the moments I’ve had showing how these tools work and what they can and can’t do. Some students seem excited to do something they previously couldn’t have cared less about. Others will look at them, and think, that’s not for me. Over all, this is not about making the process easier, or saving time, or “reducing friction.” AI is simply another, albeit flawed, source of inspiration some students might glean from in order to participate in what can often be a cagey, performative and at times, gated community. 

If educators are already turning to AI tools to complete mindless tasks, why then wouldn’t students do the same? In addition, not all students are going to use these tools. In fact, many are actively deciding not to. Though this shouldn't mean we dismiss the potential benefits for those students who might find GenAI and AI tools useful. Instead, we should be educating them on responsible use, the limitations, and the importance of critical thinking in an age where AI-generated content is only going to become more and more ubiquitous. As educators grapple with the impact of GenAI on student learning, the best path forward is not one of outright bans or blind acceptance, but of equipping students with the skills to navigate this new terrain.

James Bedford is an experienced writer and educator at the University of New South Wales. He has published short fiction as well as teaching and learning scholarship. He holds a Ph.D. in Creative Writing and is currently guiding staff and students on how to understand artificial intelligence (AI) technologies, with a specific focus on generative AI applications used for research and writing purposes. 

Our students have been drifting away, Helen Kapstein writes, but we want them to drift back to the mindset of being c

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How to use AI for Studying

Featured In

Table of contents, ai tools for studying.

• Try Speechify Text to Speech, the Best Option

Using AI for Different Learning Styles

Enhancing time management and retention, ensuring academic integrity, best ai tools for research and writing, practical applications of ai in studying, free ai tools for students.

When it comes to enhancing our learning experience, artificial intelligence (AI) has revolutionized the way we study. As a college student myself, I've found that integrating AI tools into my study routine has significantly improved my efficiency and retention. In this article, I'll share my insights on using AI for studying, covering various tools and techniques that can transform your academic journey.

AI-powered tools have become indispensable for students. From note-taking to creating flashcards, these tools leverage machine learning and natural language processing to provide real-time assistance. Let's explore some of the best AI tools that can aid in your studies:

• ChatGPT by OpenAI : Using ChatGPT, you can answer questions, generate summaries, and even draft research papers. This generative AI tool is excellent for producing detailed explanations and clarifications on complex topics.
• Quizlet : This tool uses AI algorithms to help create custom flashcards and quizzes, making it easier to retain information. It’s particularly useful for subjects that require memorization.
• Otter.ai : For note-taking, Otter.ai is a lifesaver. It transcribes lectures and study sessions in real-time, allowing you to focus on understanding rather than jotting down notes.
• Microsoft OneNote : This tool integrates with various AI features to enhance note-taking, organize study materials, and even generate study guides based on your notes.

Try Speechify Text to Speech , the Best Option

Cost : Free to try

Speechify Text to Speech is a groundbreaking tool that has revolutionized the way individuals consume text-based content. By leveraging advanced text-to-speech technology, Speechify transforms written text into lifelike spoken words, making it incredibly useful for those with reading disabilities, visual impairments, or simply those who prefer auditory learning. Its adaptive capabilities ensure seamless integration with a wide range of devices and platforms, offering users the flexibility to listen on-the-go.

Top 5 Speechify TTS Features :

High-Quality Voices : Speechify offers a variety of high-quality, lifelike voices across multiple languages. This ensures that users have a natural listening experience, making it easier to understand and engage with the content.

Seamless Integration : Speechify can integrate with various platforms and devices, including web browsers, smartphones, and more. This means users can easily convert text from websites, emails, PDFs, and other sources into speech almost instantly.

Speed Control : Users have the ability to adjust the playback speed according to their preference, making it possible to either quickly skim through content or delve deep into it at a slower pace.

Offline Listening : One of the significant features of Speechify is the ability to save and listen to converted text offline, ensuring uninterrupted access to content even without an internet connection.

Highlighting Text : As the text is read aloud, Speechify highlights the corresponding section, allowing users to visually track the content being spoken. This simultaneous visual and auditory input can enhance comprehension and retention for many users.

AI tools cater to different learning styles, ensuring that every student can find a method that works best for them:

• Visual Learners : Tools like Quizlet and Anki create visual aids such as flashcards, which are perfect for visual learners.
• Auditory Learners : Otter.ai and other transcription services allow you to listen to your notes and lectures, catering to auditory learners.
• Kinesthetic Learners : Interactive AI-powered platforms like Coursera and Khan Academy provide hands-on learning experiences through quizzes and practical exercises.

One of the significant advantages of using AI tools is improved time management. Tools like Notion and Trello use AI to prioritize tasks and manage your study schedule effectively. This ensures that you stay on track and make the most of your study sessions.

Moreover, AI tools enhance retention through personalized learning experiences. For instance, adaptive learning platforms like Smart Sparrow use AI to tailor content based on your strengths and weaknesses, providing a customized learning path that maximizes retention.

While AI tools offer numerous benefits, it's crucial to maintain academic integrity. Here are some tips to avoid plagiarism and ensure your work is original:

• Citations : Use AI-powered citation tools like Zotero and Mendeley to properly reference your sources.
• Plagiarism Checkers : Tools like Turnitin and Grammarly help detect and prevent plagiarism, ensuring your work is unique.
• Understanding AI Limitations : While AI can assist in generating content, it’s essential to critically evaluate and supplement it with your understanding to maintain originality.

Researching and writing academic papers can be daunting, but AI tools can simplify the process:

• EndNote and RefWorks : These AI tools assist in managing citations and bibliographies, making the research process more efficient.
• Grammarly : An AI-powered writing assistant that helps in grammar checking, enhancing the clarity and coherence of your papers.
• Google Scholar : Leverages AI to provide relevant research papers and articles, making it easier to gather information for your studies.

Let me share how I personally use AI in my study routine:

• Generating Study Guides : I use ChatGPT to create comprehensive study guides, summarizing chapters and highlighting key points.
• Real-Time Queries : During study sessions, I use ChatGPT to answer questions and clarify doubts instantly.
• Creating Flashcards : Quizlet helps me create flashcards for quick revisions, especially before exams.
• Transcribing Lectures : Otter.ai transcribes my lectures, allowing me to focus on understanding rather than writing notes.

For students on a budget, several free AI tools can enhance your studying experience:

• Google Docs : Includes AI features like voice typing and smart compose, making writing and editing easier.
• Canva : While primarily a design tool, Canva’s AI features can help create visual aids and presentations.
• Python : For programmers, Python’s libraries and AI capabilities can be leveraged to create custom study tools and automate tasks.

Incorporating AI into our studying habits can significantly enhance our learning process, making it more efficient and tailored to our needs. From note-taking to creating study guides, AI tools provide a wide range of functionalities that cater to different learning styles and requirements. By leveraging these tools responsibly and maintaining academic integrity, we can maximize our academic potential and achieve our educational goals.

Whether you're using ChatGPT to generate summaries, Quizlet for flashcards, or Otter.ai for note-taking, AI has something to offer every student. Embrace these technologies to transform your study routine and achieve greater success in your academic pursuits.

AI reader summary

Text to Speech API Python: A Comprehensive Guide

Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

The AI Investment Race In Software: Snowflake Vs. Palantir

• Snowflake and Palantir are two of the leading software companies offering generative AI solutions to enterprises.
• Both companies have strong offerings - although most of what is being offered by Snowflake is just now generally available.
• Investors are very concerned about Snowflake's competitive positioning, particularly vis-à-vis Databricks, still a private company.
• We believe that many investor concerns regarding Snowflake are either overblown, out of date or totally spurious.
• We recommend shares of Snowflake compared to Palantir, and absolutely as well, because of its attractive relative valuation based on our estimates.

Sundry Photography

Can Snowflake, the star-crossed contender put up a reasonable AI strategy and will Palantir's AI Platform accelerate its growth?

A few years ago, SA ran a contest in which participants were asked to write about comparing two of the high growth IT vendors of the last decade, Palantir Technologies Inc. ( PLTR ) and Snowflake Inc. ( NYSE: SNOW ). I wrote an article then arguing that Palantir was a better choice although truth to tell I didn't quite recommend it. I suppose at one level, the call has weathered reasonably - Palantir shares have outperformed Snowflake shares significantly. That said, Palantir shares are basically flat over the last 3 years, while Snowflake shares are down by almost 50%.

Recently a subscriber to my Ticker Target investment service asked me if I would reprise my discussion of the two companies and see which stood where in my investment universe. This is a reprise of the comparison I made 3 years ago. The results are different; I recommend shares of Snowflake now and while I would like to recommend shares of Palantir, its relative valuation simply is too high for me at this point.

That, I realize, is a bit of a contrarian call, especially on the pages of SA. Palantir has an average buy rating from SA analysts while Snowflake is rated by the SA analyst cohort as a hold. The Quant service that SA offers labels both stocks as a hold.

Snowflake shares look simply horrible on a chart. According to IBD, the shares have an RSI share of 12 out of 99 and IBD provides a composite rating of 24 out of 99. It has an accumulate/distribution rating of E. All of these ratings are saying the same thing; many investors do not like Snowflake as an investment at this time.

In recent weeks Snowflake shares have been dealing with multiple noticeable headwinds. These include an analyst day which was not specific enough for some - although being specific about the outlook for a new AI product would probably have been fairly foolish, at least in my opinion. One analyst wanted to see some customer testimonials - I can find a few on the company's website - Zoom and Bayer are using Cortex Analyst. Sigma Computing is using Cortex language models. Northern Trust is another early adopter that was recently called out as part of the Investor Day presentation. While customer testimonials are nice, adoption is really better. And it appears as though Cortex adoption is happening now. This quote is from Snowflake's latest earnings conference call.

We've seen an impressive ramp in Cortex AI customer adoption since going generally available. As of last week, over 750 customers are using these capabilities.

The shares have also been dealing with an outlook which has margins down because of higher than anticipated consumption of GPUs, in part to support demand for new deployments of Cortex. And most recently, the company has been dealing with a cyber-attack - not on its infrastructure which has been found to be safe, but based on the theft of passwords of some users who have not been using multi-factor authentication - hard to believe, but apparently the case.

I personally am far more of a momentum investor than a falling knife buyer. But I will lay out here my case to buy the shares, even knowing all that I know and the negatives that have been highlighted by many over the past several months.

By comparison, as a business, Palantir has gone from strength to strength. It reported strong results and raised its forecast as part of its latest earnings release - although like many other companies, its shares fell post earnings . It has a well-received AI platform - AIP and a well running sales engine - in part based on its boot camp strategy, particularly in North America. Its issue has been one of valuation, and to an extent, the company's commercial revenue growth. That said, the shares are up 53% year-to-date while Snowflake shares are down by 34% over the same span.

I am going to review Snowflake's positioning and outlook first, simply because it has so many more investment issues to consider as compared to Palantir. But before I do, I think it is worth noting one theme that seems to have eluded some investors and commentators. Generative AI software demand is still in what many have called a discovery phase . I wrote that I guessed that would be the case based on several surveys that Gartner published at the start of the year. Now, after a quarter in which software demand has been said to be mixed, a theme was presented at the B of A Global Technology conference just concluded by their analyst team. Their thesis is that there is perhaps some evidence that the consumption of GPUs and other AI hardware has had some "cannibalizing" impact on the growth of software demand.

In the next several quarters that will start to change. Users are planning to use their AI hardware to run something - we just do not know yet exactly what that something is or will be and how quickly this change will take place. This theme has been picked up by other analysts and has been a factor in the relative underperformance of software stocks in the short term. That in no way suggests that long-term demand for Generative AI software will not be huge and that there is a discovery phase before massive investments in deploying software that is entirely revolutionary.

I have seen much written already about winners and losers in the AI space. It is beyond foolish to pick winners and losers before all the horses are on the track and at least approaching the starting gate. Users are still searching for AI applications that can deliver real ROI. That discovery process is going to take a few more months. Perhaps in the last 1/3rd of the year, some AI horses will really leave the starting gate. But don't get mad at your investment because it is not yet talking about specific enhanced IT demand coming from generative AI. We live in an impatient world, I get that, but users need to figure out what AI strategy is right for their business and will produce the best ROI most quickly. And when they do, they have to create models that are specific to their own businesses and can be used to create generative AI apps. That hasn't yet happened and much of that will not happen until 2025

Snowflake - the stock seems like a lost cause; the business… not really, if at all

For some years Snowflake's growth was incredible and the shares were priced as though the company were offering a service that had benefits akin to those that might be expected from the 2nd coming. Inevitably, growth slowed, and the share price has gotten eviscerated. The company seems under attack from many quarters and concerns have emerged about a multiplicity of competitors. It is probably relevant to put Snowflake issues in perspective. This is still a company whose growth last quarter, which significantly exceeded expectations was 34%. It is non-GAAP profitable, and it has a free cash flow margin in the mid-high 20% range. As these things go, Snowflake isn't sick, or feeble, or showing signs of senescence.

What are some of the concerns that have bedeviled the shares, and are they being mitigated?

1.) Snowflake's core business is based on consumption and there is a lot of remaining controversy about the outlook for consumption

Snowflake's core business has been that of providing a database built for use on the web. Customers have chosen to partner with Snowflake as they moved workloads to the web. This in turn led to an explosion of usage on Snowflake's infrastructure - and an explosion in customer bills as well. Starting about 2 years ago, most users started a generalized strategy of trying to optimize their bills by optimizing their usage of Snowflake - and much else besides. In turn, this dramatically reduced Snowflake's growth rates. Like many other trends in IT, optimization can go only so far. It might, for example, be possible to reduce the number of years of data retained in a Snowflake database, but once being done, it can't be done again. At some point, the analytics, which is the outcome of queries against the Snowflake database, won't work without enough years of data. Users experimented in terms of eliminating or reducing some storage and compute requirements, and there were some users who delayed or eliminated their plans to move workloads to the cloud. User optimization activities are going to continue in the future as they have in the past - it is the elevated optimization that has been an issue for investors.

Optimization activities caused Snowflake's revenue growth to fall precipitously. Optimization has always been a component of managing an IT deployment. It is part of the business and always has been. But excessive optimization as was seen over an 18-month period was a surprise to many, including vendors, and upset growth rate expectations. The last couple of quarters, including the quarter that was reported a few weeks ago, seem to suggest that the era of above-trend optimization has come to an end. Snowflake's growth last quarter was well above expectations; this was mainly driven by faster than anticipated usage growth.

Usage growth is mainly a function of workload growth. Recently, Snowflake switched its sales comp plan to focus compensation on acquiring new workloads. The CFO suggested that this changed incentive structure was starting to bear fruit. That said, the guidance provided by Snowflake basically has ignored these positive trends. The company did raise revenue guidance for the balance of the year - but to my mind by a derisory amount. The initial forecast for FY 2025 revenue had been for revenues of $3,250 million. Q1 product revenues were $40 million greater than forecast. The revised product revenue forecast increased by just $50 million, or in other words by about $10 million adjusted for Q1 actuals. Usage growth trends were said to have softened in April, perhaps because of seasonal factors, and then to have improved in May. It seems likely that the current guidance for product revenue growth is excessively "prudent" or substantially de-risked. At this point, I think investor concern about usage growth should be essentially laid to rest.

2) Snowflake's Competitive positioning-a major investor concern

Another major concern amongst many Snowflake investors and commentators has been competition. Snowflake faces a variety of competitors, including companies such as Oracle Corporation ( ORCL ) and Teradata who have had data warehouse solutions for years, the hyperscalers who all have their own data warehouse offerings, but perhaps most significantly from Databricks. Databricks has a very similar database offering that was purpose developed for the cloud. It has tools that make developing applications built on its stack rapid and efficient. It has acquired much of the technology necessary to develop AI applications on its infrastructure. I am going to focus on competition with Databricks because I think many institutional investors and analysts feel this is by far the greatest threat to Snowflake's future.

Databricks is still private, but it has reported revenue. Last year, Databricks reported revenue of $1.6 billion , which was growth of greater than 50% last year, which compares to Snowflake's revenues of $2.8 billion or growth of 36%. It is now projecting growth of greater than 60 % this year. Databricks might be profitable; its gross margin was greater than 80% according to industry publications. But given the cadence of acquisitions of some scale, profitability has probably proven elusive. I think that with all of these acquisitions which likely have highly elevated opex ratios, Databricks has been losing money.

The company's faster growth when compared to Snowflake is possibly due to its generative AI offerings. It indicated that in its latest quarter, its bookings metric was at a record level and had doubled year on year in part because of its generative AI offering. Obviously, some of the company's growth has been inorganic - it bought MosaicML for $1.3 billion and it subsequently bought Arcion, Einblack and LilacAI. It most recently has announced plans to buy Tabular for more than $1 billion. Tabular. Tabular offers users an approach to what are called Iceberg tables. Apparently this acquisition came after a bidding war with Snowflake. In response, a few days ago, Snowflake announced Polaris Catalog, a service tied to Iceberg.

The CEO of Databricks, Ali Ghodsi has recently been quoted as saying that Snowflake

Snowflake was basically not doing AI whatsoever.

Of course that was at the end of March - life has changed since then. I will try to provide some perspective on those changes just a bit further on.

Ok, so Snowflake has lots of competitors and its AI offerings are just now becoming available. Where has that left the company in terms of bookings, probably the most salient number to consider when looking at competition? Last quarter, the proxy for bookings, RPO balance rose by 46%. The prior quarter, the RPO balance rose by 41%. Basically, these metrics were pre-AI in the sense that AI has just now become generally available.

In the latest earnings conference call, the CEO talked about the early adoption of Cortex.

We're investing in AI and machine learning, and our pace of progress in a short amount of time has been fantastic. What is resonating most with our customers is that we are bringing differentiation to the market. Snowflake delivers enterprise AI that is easy, efficient, and trusted. We've seen an impressive ramp in Cortex AI customer adoption since going generally available. As of last week, over 750 customers are using these capabilities. Cortex can increase productivity by reducing time-consuming tasks. For example, Sigma Computing uses Cortex language models to summarize and categorize customer communications from their CRM.

Given the CEO's comments regarding AI adoption, I expect that the company will continue to enjoy strong bookings results. While in the Snowflake model, bookings do not immediately lead to revenue - revenue is mainly consumption-based - they suggest that the company has been able to see notable success in the marketplace.

Concerns about Snowflake competition aren't going away any time soon-or at least not until the company shows it is generating significant revenues from Cortex in all of its many incarnations. That is not going to happen immediately, although obviously with 750 customers already deploying Cortex, it would appear that it is a gross exaggeration and oversimplification to believe that Snowflake will not have a significant competitive position in the world of Large Language Models and as foundation technology for generative AI applications. But so long as the company is growing bookings in the 40%+ range before its AI solution become available, I am comfortable that the company's growth is not being upended because of competitive pressures.

3) Snowflake's CEO succession - was it forced or was it a positive progression?

We live in an impatient world. The simplified story about Snowflake is that its former CEO, Frank Slootman didn't understand the signs that the world was to be upended by the generative AI revolution, and that the board had to act precipitously because the company was in a bad place. The story, so it goes is that the company had no AI strategy and needed an emergency transfusion.

Frank Slootman has never been a technologist. He has been a legendary operator - his prior two companies were DataDomain, ultimately acquired by EMC/Dell Technologies Inc. ( DELL ) and ServiceNow, Inc. ( NOW ). His reputation has been built on his record as a sales leader and a company leader.

The reality of the transition is quite different than the current legend. In the spring of 2023, Snowflake acquired an early-stage company, Neeva that had been founded by Sridhar Ramaswamy . Neeva's initial product offering was that of search technology that was essentially based on AI. Ramaswamy has considerable knowledge of the space given his role as head of Google's advertising and commerce operations.

At the time of the acquisition, Neeva had just pivoted to providing a search experience for the enterprise based on AI. When Neeva was acquired, Ramaswamy became the head of Snowflake's AI. In the few months he was in that role , he launched Snowflake's Cortex. Cortex, is just now in general availability. In February of this year, Ramaswamy succeeded Frank Slootman as CEO of Snowflake. Slootman remains as Chairman of the Board. Ramaswamy has indicated in interviews that he was heavily recruited by several vendors for a CEO role but he ultimately chose to work with Snowflake in that role.

Ramaswamy has a powerful resume that seems well-suited for his current role. He is driven both as a technology visionary, but also as an operator. From what I can tell from interviews, Ramaswamy is the right man at the right time for the right job.

Slootman is now 65, and Snowflake is the largest organization he has ever led. My own take is that Slootman and his team have been well aware that Snowflake was behind Databricks in terms of AI development and bought Neeva as a tactic to accelerate its own AI development. While I doubt if there was a conscious succession plan involved, I think this succession was always considered as a possibility, and essentially Neeva's development team started to build the AI suite that has become Cortex even before the acquisition was finalized under the leadership of Ramaswamy.

4) What is Cortex and what are its chances in the market

Cortex is Snowflake's AI platform. It would be presumptuous of me to believe that I am able to precisely handicap its likely success and contribution to Snowflake overall. In addition to Cortex, Snowflake will be introducing other AI elements to its offering in the next several months. These will be:

Iceberg, Snowpark Container Services, and Hybrid Tables will all be generally available later this year.

I am not going to review all of these offerings; it would be more than a bit tedious to do so, and I doubt it would contribute specifically to developing an investment thesis.

I think it is easy to underestimate the element of drag in all of this. Drag, in the IT world, means the impact of one product on the success of another product. In this case, just having Cortex and other AI products available is likely to influence the ability of Snowflake to gain new workloads and acquire new users. Users who heretofore might have been concerned about Snowflake's product roadmap can now make commitments based on understanding the extent of Snowflake's platform.

Drag can be very hard to measure. But it is real, and not something I would dismiss as insignificant in considering the company's growth cadence going forward.

A couple of weeks ago Snowflake made a series of announcements regarding product availability and functionality for Cortex. The announcements were centered around ease of use features including a new chat experience and a no-code capability. Interfaces are now available to access many leading large learning models and integration with Snowflake ML (Machine Learning) was also announced. Other capabilities, such as Cortex Analyst and Cortex Search, were announced, but are not yet in public previews. The image here is a graphical representation of how these capabilities work together:

Snowflake AI Stack (Snowflake)

Zoom is a Cortex reference customer. I call this out because of some commentary that Snowflake doesn't have reference customers for Cortex;

"Data security and governance are of the utmost importance for Zoom when leveraging AI for our enterprise analytics. We rely on the Snowflake AI Data Cloud to support our internal business functions and develop customer insights as we continue to democratize AI across our organization," said Awinash Sinha, Corporate CIO, Zoom . "By combining the power of Snowflake Cortex AI and Streamlit, we've been able to quickly build apps leveraging pre-trained large language models in just a few days. This is empowering our teams to use AI to quickly and easily access helpful answers."

Recently, Snowflake shares have been pressured because of news about data breaches. The breaches are really those of customers not properly protecting their data assets stored on Snowflake, and not a breach of Snowflake infrastructure. I will expand on that in the next section. Ironically, the company is introducing Snowflake Cortex Guard which uses LLM input/output capabilities to filter and flag harmful content.

With Cortex Guard, Snowflake is further unlocking trusted AI for enterprises, helping customers ensure that available models are safe and usable.

Snowflake technology has always been carefully orchestrated to reduce threat surfaces and maximize data security.

Another capability that has been announced is Snowflake's Document AI. Document AI is basically a function that enables visual document question answering. And here too, there is a reference, in this case Northern Trust which uses the service to process documents at scale to lower operational costs - it is replacing people with software to answer queries.

This is not intended to be a Cortex commercial. And I do not wish to be tedious in addressing readers. What I am suggesting here is that Snowflake has a cogent generative AI strategy which offers users the ability to create lots of applications easily that solve a multitude of business problems. Snowflake's AI is built on the data it stores in its Cloud Data Platform, the models it has available, both internally developed Arctic , and the several other models with which it has connectors, its analyst and search capabilities, and its no-code studio offering.

Given that much of Cortex is even now either just generally available, or will be generally available in a few weeks, it would be more or less impossible to develop an informed point of view on how the functionality the company has announced compares to what is on offer from Databricks and others in this space. That said, I wouldn't be surprised, given the background of the CEO, that Snowflake's Cortex offerings would be at least competitive, if not more than competitive.

As mentioned, Snowflake's management has made a decision not to make any revenue estimates for Cortex or its other new products as part of its forecast. I have linked to a discussion of Cortex which includes pricing data - but while it shows how costs are calculated, it really doesn't provide any estimate of how much a user might pay for a workload.

Snowflake at this point is forecasting product revenues of $3.3 billion. It would probably be overly optimistic to believe that revenues from Cortex are going to change this forecast materially in the current fiscal year. My guess would be something like 1%-2% is likely, although changing expected revenue growth from 24% - the current 1st Call consensus, to 26% would likely be meaningful for Snowflake's valuation, in my opinion.

Really, revenue growth from usage will be far more important in determining just how much the company might exceed its current revenue growth forecast. And that is where drag comes in. My view is that users, seeing a clear functional path to the creation of generative AI applications, are more likely than before to choose Snowflake for new workloads. And that isn't baked into estimates or expectations at this point.

5) Does Snowflake have a cyber-security problem?

In two words, not really. A few days ago, headlines blared from every investor platform and from many publications addressed to the IT community about Snowflake's data breaches. And almost certainly that has brought the shares down even further. It probably seems like every day there has been a negative story about the company. But the fact is - Snowflake didn't have a breach. Really!

"Mandiant's investigation has not found any evidence to suggest that unauthorized access to Snowflake customer accounts stemmed from a breach of Snowflake's enterprise environment," the Alphabet Inc. ( GOOG ), ( GOOGL ) -owned company said. "Instead, every incident Mandiant responded to associated with this campaign was traced back to compromised customer credentials."

In addition to Mandiant, Snowflake apparently contracted with CrowdStrike Holdings, Inc.'s ( CRWD ) Falcon consultancy to analyze potential Snowflake vulnerabilities or misconfigurations. The results of this analysis were a similar clean bill of health.

The subject of cybersecurity is complex and riven with details and nuances. But what happened is that some of Snowflake's 9850 customers - 165 perhaps - were themselves allegedly guilty of major security malpractice. Users were not using MFA to protect their data estates, they were using long-exposed credentials that had not been rotated, and users were not using "network allow lists" a basic cybersecurity concept.

The list of Snowflake users who had their data stolen is considerable. And the thieves are extorting money from these users. Potential victims of these infostealers include Ticketmaster, Santander Bank, Anheuser-Busch, Allstate, Advance Auto Parts, Mitsubishi, Neiman Marcus, Progressive and State Farm. Infostealing is gradually replacing Ransomware as the crime of choice in 2024.

What is happening now is that Snowflake is enforcing basic cyber security policies on its customers. Users will have to adopt MFA to use Snowflake, and they will have to rotate credentials.

Will these incidents have a noticeable impact on Snowflake usage? Most users will rapidly adopt MFA and continue to process queries on Snowflake infrastructure. Will these breaches impact Snowflake bookings this quarter? Probably not, although explaining the situation may cause a slight elongation of some sales cycles. Apparently, one insider doesn't think too much of the issue. Michael Speiser, a Snowflake director and a managing partner at VC firm Sutter Hill Ventures, recently bought $10 million of Snowflake shares. Presumably he knows the company well as he was an initial investor in the firm and was its founding CEO from 2012-2014. His purchase reversed multiple sales over the last few years. I have to believe that if there were any issues - even short-term issues of usage or bookings, Mr. Speiser would not be buying the shares and he would be in a better position to know specifics about usage and bookings than almost anyone else.

6) Snowflake's guidance - was it alarming?

Snowflake shares fell significantly in the wake of its earnings release when it provided guidance which essentially lowered margin expectations. I am going to focus on just the element of guidance that brought down margin expectations. I will simply comment that the CFO, a highly respected IT industry veteran who has pledged to remain at Snowflake for the next 3 years has indicated that guidance has been significantly de-risked in an understandable effort to provide Ramaswamy some breathing room for his innovations to bear fruit.

Specifically, Snowflake reduced its full year guidance for gross margins from 76% to 75%, its guidance for non-GAAP operating income from 6% to 3% and its guidance for its free cash flow margin from 29% to 26%. This forecast was the primary reason for share price decline in the wake of the latest earnings report. The reason for the margin guide down was specifically because of the greater GPU costs related to the company's AI initiatives. It is also a function of a small acquisition, TruEra which added 35 development employees as well as a higher budget for additional R&D hires.

And at this point, the guidance becomes a bit misleading. The company needs more GPUs to support the deployment of Cortex as companies buy and deploy the offerings. But it doesn't charge users based directly on how many GPUs they wind up using; as mentioned earlier, Cortex, like other Snowflake products, is charged mainly based on compute-the user consumes tokens. But because Cortex is a new product, and revenues are not being forecast, while the purchase of GPUs is being forecast, the result is, almost inevitably, a forecast in which margins are showing a decrease. Again, almost inevitably, Snowflake is not likely to need to buy GPUs at an accelerated rate unless its customers both ramp usage and deploy Cortex.

The margin guide down is not the result of any existential problem with the Snowflake business model. It is the result of a specific methodology used to forecast revenues that is a mismatch with the forecast used to forecast GPU costs. And it is a function of extremely rapid and accelerated product development activities, whose consummation is likely to accelerate revenue growth. Of course, stocks trade on specific forecasts - I get that - but in this case, looking at the details, the margin guide down is really not the negative suggested by the share price decline.

Risks to the Investment Thesis

At this point, there are a few highly visible risks to the investment thesis. One is that my evaluation of the company's issues with cybersecurity are too optimistic. Another is that usage trends show less growth than the last two quarters. And finally, there is the risk that the company's AI initiatives fail to attract customers at a rapid cadence. To an extent, Snowflake shares are where they are because investors are concerned about these risks and have sold the shares aggressively the last several weeks. So, I will turn to valuation to see if the investment makes sense given the risks to the growth thesis cited above.

Snowflake's Valuation

Snowflake shares have historically sold at a premium valuation reflecting its strong growth until the optimization wave drove growth rates down substantially. The company's valuation metrics now reflect that slowdown, while not incorporating the potential impact of the company's AI initiatives and usage trends. Based on my forecast that total Snowflake revenues for the next 12 months will reach $3.84 billion, a conservative estimate in my view, the current EV/S for the shares is 10.1X based on the current Snowflake share price of about $127. I have forecast that the company's free cash flow margin will be 28% over that span, essentially because the company's margin forecast deliberately incorporates a mismatch between GPU costs and revenue. And finally, my analysis is based on a 3-year CAGR of 33%. Frankly, I wouldn't have bothered writing this article and recommending the shares if I didn't think that the company's core business in terms of usage trends would show consistent trends as the company captures more workloads. And, of course, unlike the company's forecasting paradigm, I am assigning a significant expectation for the company's revenue growth from Cortex and other AI initiatives. Using these assumptions, Snowflake shares are now valued at a discount to the relative valuation of peer high growth companies. Not only is it valued at far lower levels than Crowdstrike, for example, but it is also valued far below valuation levels of Samsara Inc. ( IOT ), Cloudflare, Inc. ( NET ), Datadog, Inc. ( DDOG ), The Trade Desk, Inc. ( TTD ), MongoDB, Inc. ( MDB ) and GitLab Inc. ( GTLB ). Its valuation is now approximately comparable to that of Zscaler, Inc. ( ZS ) shares. It's not just that this is a turnaround from the recent past which in itself wouldn't factor into my view; it's that Snowflake shares are now valued at significant discounts to peer high growth IT shares - and in some cases the discount is yawing. To reiterate, I recommend that long-term investors look through the current noise and establish or augment positions in Snowflake shares at this time and at this price.

Palantir: It's all about the ramp of the company's AIP and how that translates into revenue growth

As mentioned earlier, the Palantir debate is far simpler with fewer issues and that means a much attenuated discussion. Palantir shares fell by about 15% after quarterly results were announced despite a beat and raise quarter. Like many other software companies, the amount of the raise was probably too small to satisfy heightened expectations.

Palantir's management and the CEO have been more vocal than almost anyone else in the IT space in articulating support for Ukraine in its current conflict. Given the foundation of the company's business and the use of its technology by Western militaries, that is completely unsurprising. It isn't apparently impacting the company's ability to win commercial deals, and thus is a non-issue with regard to the company's investment thesis.

Despite the pull-back in the wake of earnings, Palantir shares are up by 35% since the start of the year, obviously far better than Snowflake's share price performance. Much of this related to the significant upside surprise the company reported back in February which drove the shares up by 29% in a single day.

Here are the specifics that show the company's results vs. its prior forecast and then show the actual change in guidance after adjusting for the quarterly beat.

At the start of 2024, Palantir had projected full year revenues of about $2.66 billion, US Commercial revenues of greater than $640 million or growth of 40%, non-GAAP operating income of a bit greater than $840 million and free cash flow at the mid-point of the forecast of $900 million.

Q1 was a beat with revenues $20 million above the forecast and non-GAAP operating income $28 million greater than forecast.

The company's new forecast is for revenue to be about $2.685 million, for US commercial revenue growth to be above 45% and for adjusted operating income of $875 million.

So, basically, the company raised its forecast for the balance of 2024, adjusted for the first quarter beat of $5 million, or about 0.3%. It raised its forecast for non-GAAP operating income, again adjusted for the Q1 beat of $28 million by $7 million, or a bit less than 1%. Given the beat in US commercial revenues last quarter, the forecast for 45% growth is really no increase at all for the balance of 2024.

The company called out that it would be increasing its opex investments in the US to take advantage of AIP-related opportunities. This opex investment is apparently constraining forward margin expansion opportunities in the current year, but should lead to faster revenue growth. It might be considered to be an analogous decision as to that made by Snowflake in terms of its own spending for development, and is thus far having similar impacts on valuation.

Some of the issues are what Palantir calls strategic contracts, or really, larger enterprise deals that are long-term commitments to Palantir. Strategic contracts were $24 million in Q1, and are expected to decline to $7-$9 million in Q2. Overall, strategic contracts are expected to be 2% of total revenues, the same as in the year earlier.

Overall, Palantir offered a forecast that was fairly typical in the software space over the last 60 days, and which has led to a valuation compression, not just of Palantir shares, but of the shares of many other high growth software companies as well. The company reported a strong increase in RPO balances, which were up 39% year on year and up 5% sequentially. While those numbers look impressive, they really are based on the performance of the company's commercial business; the US government business has contractual terms that prevent multi-year orders from being counted as part of the RPO balance.

So, the question is, if RPO growth for Palantir's commercial business was strong, then why not raise guidance for the growth of the US commercial business segment? And looking at Snowflake vs. Palantir as investments, it would appear that percentage bookings growth for Snowflake is actually growing noticeably faster than Palantir, and was doing so before Cortex was generally available.

The company has been trying to focus investor interest on US commercial growth which has been particularly strong with revenues, excluding strategic transactions, growing by 68% last quarter and 22% sequentially, in what is normally the lowest growth quarter of the year. US commercial revenue growth as reported was less strong, with growth of 40% year on year and 14% sequentially. Overall, US commercial revenue was $150 million last quarter or about 17% of the total. In other words, growth in the US commercial segment constitutes 40% of the company's total growth; the other 83% of the company's revenue had growth of about 12.5%. Needless to say, that kind of disaggregation, which is done by most professional investors, constitutes an issue in terms of valuation, at the least, and suggests a bit less positive momentum than the conference call presented.

As has been typical of Palantir calls , the Q&A segment was brief and not terribly enlightening. Alex Karp, the company CEO is who he is, and I suppose given the success of the company he is entitled to some of his idiosyncrasies. The call reaffirmed his support of Ukraine in its current conflict and indicated that while there had been a bit of turnover due to the company's stance, the stance had not impacted actual business trends.

That said, it would have been more enlightening to hear more about competition, particularly for AIP. Saying that AIP has no competitors, or that others use LLMs incorrectly (a part answer on this latest call) is really no answer. At some level, AIP does compete with the offerings of both Databricks and Snowflake. At the time of the call, Snowflake Cortex had not been formally released.

To me the call was lacking because no one had the temerity to ask the $64 question about why guidance was increased by such a small amount given the obvious momentum in Q1. If this company, or Snowflake or any company is going to ramp opex growth, wherein the numbers are the payback.

I am going to avoid commenting about Palantir's boot camp initiatives . It is a go-to-market motion; it is going to have some success but without a lot more quantification than has been provided, their overall efficacy cannot be determined.

AIP has, and will have many competitors. Basically, AIP includes machine learning, large language models and AI itself, or the user interface to the LLM. Is AIP better than similar offerings from the hyper-scalers, and from Databricks and Snowflake? This is really fluid situation; new offerings are being introduced weekly and I think trying to handicap who has a better set of models, or interfaces or has a better way of ingesting data to create what is called machine learning is premature to say the least.

The advantages that Snowflake and Databricks offer is that their customers are already storing lots of data that is needed to train models for specific use cases running on their infrastructure. On the other hand, Palantir has thousands of users who have deployed Foundry which might possibly be considered as a precursor to some generative AI apps.

AIP has been available for about 15 months now, and it has obviously kick-started the growth in the company's US commercial sector. The company is in the process of developing an AIP variant specifically designed to capture opportunities in the defense space.

The Comparison and My Conclusion

Without attempting to be too anodyne, investing is a matter of making choices. There are many ways to invest in AI. Both Snowflake and Palantir now offer AI solutions. I have neither the temerity nor the desire to try to declare that one of these two companies has a "better" AI solution. My guess is that both AIP and Cortex will be successful, and presumably more successful than what is embodied in consensus numbers. I personally think that having a data estate as part of an overall AI solution makes eminent sense, but I will leave technology evaluations to the many highly skilled industry consultants.

The assumptions used in this comparison are as follows:

Snowflake Palantir

Stock prices as of 6/14/24 $125.65 $23.12.

Enterprise Value $38.0 bil. $55.0 bil.

Current Revenue Estimates $3.84 bil. $3.1 bil.

(Next 12 months).

EV/S 10X 17.8X.

3-year CAGR 33% 27%.

Free Cash flow margin 28% 33%.

I want to note here that my 12-month forward revenue estimate is based on the average revenue beat percent compared to the forecast over the last 4 earnings reports for both companies. And to be clear, it is the next 12 months and not the calendar year - that requires trending an additional quarter that I am showing as a revenue forecast. The 3-year CAGR is my estimate. If readers think that different metrics are more appropriate, please use the comments section to express why a different estimate might be more reasonable.

I think with the data that is available, the choice of Snowflake compared to Palantir as an investment is clear. The valuation compression of Snowflake has carried the shares into GARP territory, although many readers have different things in mind for a GARP investment. Many of the reasons why Snowflake shares have seen their valuation eviscerate are either inaccurate or out of date or the result of unreasonable analysis in my opinion.

Palantir is a company in the right place at the right time with a strong AI product offering. The problem, basically, is that is well known, and reflected in the valuation-not just absolute, but relative. I have obviously ramped my CAGR estimate above consensus to take account of further success for AIP. Could it be more than that? Certainly. But it would have to be far greater than 27% to justify the company's relative valuation.

As mentioned at the start of the article - many words ago, I admit, that right now software stocks are under pressure because of the thesis that their current growth is being deflated by user priorities to create AI infrastructure using products from NVIDIA Corporation ( NVDA ), Super Micro Computer, Inc. ( SMCI ), Dell and perhaps Broadcom Inc. ( AVGO ). Like many such strategies, it is overdone, lacks context and nuance, and seemingly ignores that the following infrastructure will be apps. The CEO of Datadog had an excellent comment regarding the cadence of AI revenues on his latest conference call which I have linked here. Well worth the read.

Last week, both results from both Oracle Corporation (ORCL) and Adobe Inc. ( ADBE ) may have dented the thesis - the level of bookings Oracle reported might suggest that there is a rather significant budget available for AI-trained databases while the momentum of Adobe's Firefly and its impact on that company's growth was far greater than most (and that includes this writer) had expected.

The latest concerns about Snowflake and its so-called security issue seem to have been laid to rest. The company's usage problem seems to have come to an end. The company may have been behind rival Databricks in the AI race; that doesn't seem to have impacted bookings, and at this point, the company has seemingly caught up in terms of its overall AI offerings. Sentiment, as I mentioned, on Snowflake looks positively awful and I am sure technical analysis would not be very positive. That said, I am reiterating my recommendation to buy Snowflake shares at this time and this price with the expectation of significant positive alpha over the next year.

This article was written by

Analyst’s Disclosure: I/we have a beneficial long position in the shares of SNOW either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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