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Constructing Essay Tests

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• The four main types of essay | Quick guide with examples

The Four Main Types of Essay | Quick Guide with Examples

Published on September 4, 2020 by Jack Caulfield . Revised on July 23, 2023.

An essay is a focused piece of writing designed to inform or persuade. There are many different types of essay, but they are often defined in four categories: argumentative, expository, narrative, and descriptive essays.

Argumentative and expository essays are focused on conveying information and making clear points, while narrative and descriptive essays are about exercising creativity and writing in an interesting way. At university level, argumentative essays are the most common type. 

In high school and college, you will also often have to write textual analysis essays, which test your skills in close reading and interpretation.

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Table of contents

Argumentative essays, expository essays, narrative essays, descriptive essays, textual analysis essays, other interesting articles, frequently asked questions about types of essays.

An argumentative essay presents an extended, evidence-based argument. It requires a strong thesis statement —a clearly defined stance on your topic. Your aim is to convince the reader of your thesis using evidence (such as quotations ) and analysis.

Argumentative essays test your ability to research and present your own position on a topic. This is the most common type of essay at college level—most papers you write will involve some kind of argumentation.

The essay is divided into an introduction, body, and conclusion:

• The introduction provides your topic and thesis statement
• The body presents your evidence and arguments
• The conclusion summarizes your argument and emphasizes its importance

The example below is a paragraph from the body of an argumentative essay about the effects of the internet on education. Mouse over it to learn more.

A common frustration for teachers is students’ use of Wikipedia as a source in their writing. Its prevalence among students is not exaggerated; a survey found that the vast majority of the students surveyed used Wikipedia (Head & Eisenberg, 2010). An article in The Guardian stresses a common objection to its use: “a reliance on Wikipedia can discourage students from engaging with genuine academic writing” (Coomer, 2013). Teachers are clearly not mistaken in viewing Wikipedia usage as ubiquitous among their students; but the claim that it discourages engagement with academic sources requires further investigation. This point is treated as self-evident by many teachers, but Wikipedia itself explicitly encourages students to look into other sources. Its articles often provide references to academic publications and include warning notes where citations are missing; the site’s own guidelines for research make clear that it should be used as a starting point, emphasizing that users should always “read the references and check whether they really do support what the article says” (“Wikipedia:Researching with Wikipedia,” 2020). Indeed, for many students, Wikipedia is their first encounter with the concepts of citation and referencing. The use of Wikipedia therefore has a positive side that merits deeper consideration than it often receives.

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An expository essay provides a clear, focused explanation of a topic. It doesn’t require an original argument, just a balanced and well-organized view of the topic.

Expository essays test your familiarity with a topic and your ability to organize and convey information. They are commonly assigned at high school or in exam questions at college level.

The introduction of an expository essay states your topic and provides some general background, the body presents the details, and the conclusion summarizes the information presented.

A typical body paragraph from an expository essay about the invention of the printing press is shown below. Mouse over it to learn more.

The invention of the printing press in 1440 changed this situation dramatically. Johannes Gutenberg, who had worked as a goldsmith, used his knowledge of metals in the design of the press. He made his type from an alloy of lead, tin, and antimony, whose durability allowed for the reliable production of high-quality books. This new technology allowed texts to be reproduced and disseminated on a much larger scale than was previously possible. The Gutenberg Bible appeared in the 1450s, and a large number of printing presses sprang up across the continent in the following decades. Gutenberg’s invention rapidly transformed cultural production in Europe; among other things, it would lead to the Protestant Reformation.

A narrative essay is one that tells a story. This is usually a story about a personal experience you had, but it may also be an imaginative exploration of something you have not experienced.

Narrative essays test your ability to build up a narrative in an engaging, well-structured way. They are much more personal and creative than other kinds of academic writing . Writing a personal statement for an application requires the same skills as a narrative essay.

A narrative essay isn’t strictly divided into introduction, body, and conclusion, but it should still begin by setting up the narrative and finish by expressing the point of the story—what you learned from your experience, or why it made an impression on you.

Mouse over the example below, a short narrative essay responding to the prompt “Write about an experience where you learned something about yourself,” to explore its structure.

Since elementary school, I have always favored subjects like science and math over the humanities. My instinct was always to think of these subjects as more solid and serious than classes like English. If there was no right answer, I thought, why bother? But recently I had an experience that taught me my academic interests are more flexible than I had thought: I took my first philosophy class.

Before I entered the classroom, I was skeptical. I waited outside with the other students and wondered what exactly philosophy would involve—I really had no idea. I imagined something pretty abstract: long, stilted conversations pondering the meaning of life. But what I got was something quite different.

A young man in jeans, Mr. Jones—“but you can call me Rob”—was far from the white-haired, buttoned-up old man I had half-expected. And rather than pulling us into pedantic arguments about obscure philosophical points, Rob engaged us on our level. To talk free will, we looked at our own choices. To talk ethics, we looked at dilemmas we had faced ourselves. By the end of class, I’d discovered that questions with no right answer can turn out to be the most interesting ones.

The experience has taught me to look at things a little more “philosophically”—and not just because it was a philosophy class! I learned that if I let go of my preconceptions, I can actually get a lot out of subjects I was previously dismissive of. The class taught me—in more ways than one—to look at things with an open mind.

A descriptive essay provides a detailed sensory description of something. Like narrative essays, they allow you to be more creative than most academic writing, but they are more tightly focused than narrative essays. You might describe a specific place or object, rather than telling a whole story.

Descriptive essays test your ability to use language creatively, making striking word choices to convey a memorable picture of what you’re describing.

A descriptive essay can be quite loosely structured, though it should usually begin by introducing the object of your description and end by drawing an overall picture of it. The important thing is to use careful word choices and figurative language to create an original description of your object.

Mouse over the example below, a response to the prompt “Describe a place you love to spend time in,” to learn more about descriptive essays.

On Sunday afternoons I like to spend my time in the garden behind my house. The garden is narrow but long, a corridor of green extending from the back of the house, and I sit on a lawn chair at the far end to read and relax. I am in my small peaceful paradise: the shade of the tree, the feel of the grass on my feet, the gentle activity of the fish in the pond beside me.

My cat crosses the garden nimbly and leaps onto the fence to survey it from above. From his perch he can watch over his little kingdom and keep an eye on the neighbours. He does this until the barking of next door’s dog scares him from his post and he bolts for the cat flap to govern from the safety of the kitchen.

With that, I am left alone with the fish, whose whole world is the pond by my feet. The fish explore the pond every day as if for the first time, prodding and inspecting every stone. I sometimes feel the same about sitting here in the garden; I know the place better than anyone, but whenever I return I still feel compelled to pay attention to all its details and novelties—a new bird perched in the tree, the growth of the grass, and the movement of the insects it shelters…

Sitting out in the garden, I feel serene. I feel at home. And yet I always feel there is more to discover. The bounds of my garden may be small, but there is a whole world contained within it, and it is one I will never get tired of inhabiting.

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Though every essay type tests your writing skills, some essays also test your ability to read carefully and critically. In a textual analysis essay, you don’t just present information on a topic, but closely analyze a text to explain how it achieves certain effects.

Rhetorical analysis

A rhetorical analysis looks at a persuasive text (e.g. a speech, an essay, a political cartoon) in terms of the rhetorical devices it uses, and evaluates their effectiveness.

The goal is not to state whether you agree with the author’s argument but to look at how they have constructed it.

The introduction of a rhetorical analysis presents the text, some background information, and your thesis statement; the body comprises the analysis itself; and the conclusion wraps up your analysis of the text, emphasizing its relevance to broader concerns.

The example below is from a rhetorical analysis of Martin Luther King Jr.’s “I Have a Dream” speech . Mouse over it to learn more.

King’s speech is infused with prophetic language throughout. Even before the famous “dream” part of the speech, King’s language consistently strikes a prophetic tone. He refers to the Lincoln Memorial as a “hallowed spot” and speaks of rising “from the dark and desolate valley of segregation” to “make justice a reality for all of God’s children.” The assumption of this prophetic voice constitutes the text’s strongest ethical appeal; after linking himself with political figures like Lincoln and the Founding Fathers, King’s ethos adopts a distinctly religious tone, recalling Biblical prophets and preachers of change from across history. This adds significant force to his words; standing before an audience of hundreds of thousands, he states not just what the future should be, but what it will be: “The whirlwinds of revolt will continue to shake the foundations of our nation until the bright day of justice emerges.” This warning is almost apocalyptic in tone, though it concludes with the positive image of the “bright day of justice.” The power of King’s rhetoric thus stems not only from the pathos of his vision of a brighter future, but from the ethos of the prophetic voice he adopts in expressing this vision.

Literary analysis

A literary analysis essay presents a close reading of a work of literature—e.g. a poem or novel—to explore the choices made by the author and how they help to convey the text’s theme. It is not simply a book report or a review, but an in-depth interpretation of the text.

Literary analysis looks at things like setting, characters, themes, and figurative language. The goal is to closely analyze what the author conveys and how.

The introduction of a literary analysis essay presents the text and background, and provides your thesis statement; the body consists of close readings of the text with quotations and analysis in support of your argument; and the conclusion emphasizes what your approach tells us about the text.

Mouse over the example below, the introduction to a literary analysis essay on Frankenstein , to learn more.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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At high school and in composition classes at university, you’ll often be told to write a specific type of essay , but you might also just be given prompts.

Look for keywords in these prompts that suggest a certain approach: The word “explain” suggests you should write an expository essay , while the word “describe” implies a descriptive essay . An argumentative essay might be prompted with the word “assess” or “argue.”

The vast majority of essays written at university are some sort of argumentative essay . Almost all academic writing involves building up an argument, though other types of essay might be assigned in composition classes.

Essays can present arguments about all kinds of different topics. For example:

• In a literary analysis essay, you might make an argument for a specific interpretation of a text
• In a history essay, you might present an argument for the importance of a particular event
• In a politics essay, you might argue for the validity of a certain political theory

An argumentative essay tends to be a longer essay involving independent research, and aims to make an original argument about a topic. Its thesis statement makes a contentious claim that must be supported in an objective, evidence-based way.

An expository essay also aims to be objective, but it doesn’t have to make an original argument. Rather, it aims to explain something (e.g., a process or idea) in a clear, concise way. Expository essays are often shorter assignments and rely less on research.

The key difference is that a narrative essay is designed to tell a complete story, while a descriptive essay is meant to convey an intense description of a particular place, object, or concept.

Narrative and descriptive essays both allow you to write more personally and creatively than other kinds of essays , and similar writing skills can apply to both.

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Taking an essay test:

Jul 26, 2014

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Taking an essay test:. How to do well on fewer than 10 cups of coffee. In this workshop, we’ll talk about how to succeed at essay tests. What is an essay test?. Unlike multiple-choice or true-or-false exams, an essay test looks for more than mere correctness.

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Presentation Transcript

Taking an essay test: How to do well on fewer than 10 cups of coffee

In this workshop, we’ll talk about how to succeed at essay tests.

What is an essay test? Unlike multiple-choice or true-or-false exams, an essay test looks for more than mere correctness.

Instead, an essay test looks for depth, critical thinking skills, and an ability to synthesize ideas.

So what do you need to do?

Pay careful attention to the “direction” words your prof uses in each essay question. He/she is using these terms to tell you HOW to answer the question.

Know your terms! • Analyze: Break something down into its parts and show how the parts connect to each other. • Compare: Show how two or more things are similar and different. • Contrast: Show how two or more things are different.

Know your terms (contd.): • Define: Explain the meaning of something. • Describe: Give a full, detailed picture of something in words to show its key aspects and traits. • Diagram: Draw a picture of something and label its components.

Know your terms (contd.): • Evaluate: Present both the positive and negative features of something. • Explain: Give reasons and facts to clarify your points. • Justify: Support something with facts and reasons.

Know your terms (contd.): • List: Present information as a series of brief, numbered items. • Outline: Provide information in a clearly organized way.

Know your terms (contd.): • Summarize: Briefly recap your main points. • Trace: Describe the order in which something occurred.

Follow directions! If you’re not responding to the question you’re asked, you won’t get the grade you’re hoping for.

An “A” essay test is: • well-focused, • well-organized, • well-supported, and • well-packaged.

Well-focused Stick to the point and avoid “rambling.”

Well-organized Plan ahead, and include a clear introduction and conclusion.

Well-supported Use facts, figures, examples, research studies, etc. to bolster your points.

Well-packagedGive yourself time to check grammar, punctuation, and other surface-level concerns.

Well-packaged,contd: Neglecting grammar and punctuation is like showing up for a job interview in jeans and a dirty T-shirt. You may be the best candidate for the job, but no one is going to take you seriously.

So how do you start? Budget your time. If you have an hour and a half, give yourself 15 minutes to plan, one hour to write, and 15 minutes to review.

Read all the questions before you start. Jot down important words, ideas, etc. while they’re fresh in your mind.

Before writing, look at the direction words. Make sure you understand what your teacher expects.

Jot down a rough outline This will add clarity, organization, and conciseness to your response.

Review what you’ve written Your answers should be direct and clearly respond to the question.

When in doubt, generalize If you can’t remember that Romeo and Juliet was first performed in 1595, just put “late 16th century.”

Also: While taking an essay test, ask yourself the right questions.

Do I truly understand the question? • Have I jotted down a rough outline of my major points? • Does the first sentence of my answer tell the reader what the question is and how I will develop my response?

Have I highlighted my key points? • Am I backing up these points with facts and examples? • Are the key points linked with clear transitions?

Would someone who has not taken this course understand my responses? • Have I included all the important points? • Did I stay on track?

Did I proofread for misspellings, subject/pronoun and subject/verb agreement errors, comma splices, fused sentences, and sentence fragments?

Is my handwriting legible, and have I double-spaced?

What about text anxiety? Many of us get very nervous before an exam.

It’s not all bad A little bit of “stage fright” can pump out the adrenaline we need to perform.

However… Too much anxiety can erode our focus and sap our confidence.

Control your anxiety! • Replace negative thoughts by rehearsing your test-taking strategies. • Use a favorite pen if you have one.

Control (contd.) • Tense and release different muscle groups, one group at a time.

Control (contd.) • Breathe slowly and deeply. Oxygen feeds your brain!

Control (contd.) • Focus on a soothing word or image.

Remember: The best way to succeed at essay tests is to be prepared!

Give yourself time to study without cramming.

Make sure you understand all the questions.

Plan your answers before you start writing.

Take a deep breath…

…and watch yourself succeed!

Remember: Come to the Writing Center at ANY stage of the writing process: ● brain-storming, ● planning, ● developing, or ● finishing. Our professionally trained tutors will be happy to help!

WorkshopsMadonna’s Writing Center offers the following workshops:● APA● MLA● CMS● Using Sources Correctly● Introductions and Conclusions● Getting Started● Developing Ideas● Revision● Evaluation● Test-taking● Writing Concisely

Works Cited • www.how-to-study.com/EssayPrint.htm • owl.english.purdue.edu/handouts/general/gl_essay.html • owl.english.purdue.edu/handouts/general/gl_anxiety.html • www.csbsju.edu/academicadvising/help/essayexm.htm • www.mtsu.edu/studskl/essay.html

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ESSAY TYPE 0F QUESTIONS - PowerPoint PPT Presentation

ESSAY TYPE 0F QUESTIONS

Identify situations where essay type questions are used for evaluation. construct model answer and to assign ... reasoning and lucid expressions like. ex. ... – powerpoint ppt presentation.

• Dr.K.S.Sujatha
• Assistant Professor
• Bengaluru-560056
• Identify situations where essay type questions are used for evaluation
• Construct model answer and to assign weight age to its elements, for objective scoring
• Formulate short answer questions
• M E Q to evaluate higher levels of cognitive domain
• Relatively easy to frame
• Give the students a freedom for effective expression and to test their knowledge, reasoning and ability to organise ideas
• Essay questions are useful when
• -a few questions in depth are enough to assess
• reasoning and lucid expressions like
• Ex. Correlate the changes in the ovary with those of endometrium during a menstrual cycle or after fertilization
• Limited range of application
• Lack of objectivity
• Inconsistent and time consuming scoring pattern
• Hardly any feedback to the teacher
• Scope for guesswork by the students
• Scoring pattern shows halo/anti-halo effect
• Preparing a model answer with subdivisions of scores for each element
• Scoring of all answers to the same questions should be done before proceeding to the next to avoid the carry over or HALO effect
• Conceal the identity of students
• Averaging the scores of two examiners
• Avoid distractions during scoring
• Ensure adequate scoring time
• Framing short essay questions which elicit restricted response
• Easier to set as wide range of subjects can be covered.
• It ensures more objective scoring.
• Provides less scope for guesswork.
• Examiners should NOT
• _provide clues.
• _give trivial questions.
• Patient Management Problem ( P M P )
• 2. Modified Essay Question ( M E Q )
• The format has questions closely resembling a series of short answer questions than an essay question
• Framing M E Q requires a certain amount of skill.
• Things to be avoided are
• giving answers to previous questions
• avoid students from being penalised for the same error.
• It is apparent that essay type questions can be improved.
• So that the higher cognitive learning of the student can be improved.
• Essay test questions must be improved rather than abandoning the test totally in favour of multiple choice test.

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Different Types of Essay Writing

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Presentation on theme: "Different Types of Essay Writing"— Presentation transcript:

THE 5 Paragraph "Persuasive" Essay.

Writing an Effective Essay

THE OBJECTIVE ??? THIS week and NEXT week, we will study persuasive essay…

THE First, you need a topic… You will be a given a “prompt” to write about. Sometimes you will be given a choice of prompts. You will also be told how.

THE. First, you need a topic… You will be a given a “prompt” to write about. You must ALWAYS stick to the prompt. You MUST choose a side to argue (persuasive.

Unit 3- Types of Nonfiction What should we learn?

The Writing Process Introduction Prewriting Writing Revising

Essay Writing Strategies

California State Writing Test

TYPES OF ESSAYS EOI TEST PREP You need a sheet of paper to take notes! There is a lot of writing today…be prepared!

What Makes an Essay an Essay. Essay is defined as a short piece of composition written from a writer’s point of view that is most commonly linked to an.

THE. First, you need a topic… You will be a given a “prompt” to write about. You will be a given a “prompt” to write about. You will be given a choice.

Brad Case English 9. The standard five paragraph essay structure follows a specific format. The introduction has a thesis and introduces three main supporting.

STEPS FOR PASSING THE AP RHETORICAL ESSAY 4 Components 4 Components 1) What is the author’s purpose? What does the author hope to achieve? 1) What is the.

THE ORIGINAL COMPOSITION Intro. The Original Composition  Part Three of your provincial exam will ask you to:  Write a multi-paragraph composition on.

Introduction to the AP Style Essay: English 10Honors What will be covered in this Presentation: 1.How to dissect the AP essay question being asked of.

Expository Writing Comparison and Contrast Essay.

INTRODUCTION TO NONFICTION. WHAT IS NONFICTION? The subjects of nonfiction are real people, and the events are actual happenings. Nonfiction can tell.

Get To The Point! Writing Paragraphs.

Elements of Non Fiction

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Introducing Microsoft 365 Copilot – your copilot for work

Mar 16, 2023 | Jared Spataro - CVP, AI at Work

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Humans are hard-wired to dream, to create, to innovate. Each of us seeks to do work that gives us purpose — to write a great novel, to make a discovery, to build strong communities, to care for the sick. The urge to connect to the core of our work lives in all of us. But today, we spend too much time consumed by the drudgery of work on tasks that zap our time, creativity and energy. To reconnect to the soul of our work, we don’t just need a better way of doing the same things. We need a whole new way to work.

Today, we are bringing the power of next-generation AI to work. Introducing Microsoft 365 Copilot — your copilot for work . It combines the power of large language models (LLMs) with your data in the Microsoft Graph and the Microsoft 365 apps to turn your words into the most powerful productivity tool on the planet.

“Today marks the next major step in the evolution of how we interact with computing, which will fundamentally change the way we work and unlock a new wave of productivity growth,” said Satya Nadella, Chairman and CEO, Microsoft. “With our new copilot for work, we’re giving people more agency and making technology more accessible through the most universal interface — natural language.”

Copilot is integrated into Microsoft 365 in two ways. It works alongside you, embedded in the Microsoft 365 apps you use every day — Word, Excel, PowerPoint, Outlook, Teams and more — to unleash creativity, unlock productivity and uplevel skills. Today we’re also announcing an entirely new experience: Business Chat . Business Chat works across the LLM, the Microsoft 365 apps, and your data — your calendar, emails, chats, documents, meetings and contacts — to do things you’ve never been able to do before. You can give it natural language prompts like “Tell my team how we updated the product strategy,” and it will generate a status update based on the morning’s meetings, emails and chat threads.

With Copilot, you’re always in control. You decide what to keep, modify or discard. Now, you can be more creative in Word, more analytical in Excel, more expressive in PowerPoint, more productive in Outlook and more collaborative in Teams.

Microsoft 365 Copilot transforms work in three ways:

Unleash creativity. With Copilot in Word, you can jump-start the creative process so you never start with a blank slate again. Copilot gives you a first draft to edit and iterate on — saving hours in writing, sourcing, and editing time. Sometimes Copilot will be right, other times usefully wrong — but it will always put you further ahead. You’re always in control as the author, driving your unique ideas forward, prompting Copilot to shorten, rewrite or give feedback. Copilot in PowerPoint helps you create beautiful presentations with a simple prompt, adding relevant content from a document you made last week or last year. And with Copilot in Excel, you can analyze trends and create professional-looking data visualizations in seconds.

Unlock productivity. We all want to focus on the 20% of our work that really matters, but 80% of our time is consumed with busywork that bogs us down. Copilot lightens the load. From summarizing long email threads to quickly drafting suggested replies, Copilot in Outlook helps you clear your inbox in minutes, not hours. And every meeting is a productive meeting with Copilot in Teams. It can summarize key discussion points — including who said what and where people are aligned and where they disagree — and suggest action items, all in real time during a meeting. And with Copilot in Power Platform, anyone can automate repetitive tasks, create chatbots and go from idea to working app in minutes.

GitHub data shows that Copilot promises to unlock productivity for everyone. Among developers who use GitHub Copilot, 88% say they are more productive, 74% say that they can focus on more satisfying work, and 77% say it helps them spend less time searching for information or examples.

But Copilot doesn’t just supercharge individual productivity. It creates a new knowledge model for every organization — harnessing the massive reservoir of data and insights that lies largely inaccessible and untapped today. Business Chat works across all your business data and apps to surface the information and insights you need from a sea of data — so knowledge flows freely across the organization, saving you valuable time searching for answers. You will be able to access Business Chat from Microsoft 365.com, from Bing when you’re signed in with your work account, or from Teams.

Uplevel skills. Copilot makes you better at what you’re good at and lets you quickly master what you’ve yet to learn. The average person uses only a handful of commands — such as “animate a slide” or “insert a table” — from the thousands available across Microsoft 365. Now, all that rich functionality is unlocked using just natural language. And this is only the beginning.

Copilot will fundamentally change how people work with AI and how AI works with people. As with any new pattern of work, there’s a learning curve — but those who embrace this new way of working will quickly gain an edge.

The Copilot System: Enterprise-ready AI

Microsoft is uniquely positioned to deliver enterprise-ready AI with the Copilot System . Copilot is more than OpenAI’s ChatGPT embedded into Microsoft 365. It’s a sophisticated processing and orchestration engine working behind the scenes to combine the power of LLMs, including GPT-4, with the Microsoft 365 apps and your business data in the Microsoft Graph — now accessible to everyone through natural language.

Grounded in your business data. AI-powered LLMs are trained on a large but limited corpus of data. The key to unlocking productivity in business lies in connecting LLMs to your business data — in a secure, compliant, privacy-preserving way. Microsoft 365 Copilot has real-time access to both your content and context in the Microsoft Graph. This means it generates answers anchored in your business content — your documents, emails, calendar, chats, meetings, contacts and other business data — and combines them with your working context — the meeting you’re in now, the email exchanges you’ve had on a topic, the chat conversations you had last week — to deliver accurate, relevant, contextual responses.

Built on Microsoft’s comprehensive approach to security, compliance and privacy. Copilot is integrated into Microsoft 365 and automatically inherits all your company’s valuable security, compliance, and privacy policies and processes. Two-factor authentication, compliance boundaries, privacy protections, and more make Copilot the AI solution you can trust.

Architected to protect tenant, group and individual data. We know data leakage is a concern for customers. Copilot LLMs are not trained on your tenant data or your prompts. Within your tenant, our time-tested permissioning model ensures that data won’t leak across user groups. And on an individual level, Copilot presents only data you can access using the same technology that we’ve been using for years to secure customer data.

Integrated into the apps millions use every day. Microsoft 365 Copilot is integrated in the productivity apps millions of people use and rely on every day for work and life — Word, Excel, PowerPoint, Outlook, Teams and more. An intuitive and consistent user experience ensures it looks, feels and behaves the same way in Teams as it does in Outlook, with a shared design language for prompts, refinements and commands.

Designed to learn new skills.  Microsoft 365 Copilot’s foundational skills are a game changer for productivity: It can already create, summarize, analyze, collaborate and automate using your specific business content and context. But it doesn’t stop there. Copilot knows how to command apps (e.g., “animate this slide”) and work across apps, translating a Word document into a PowerPoint presentation. And Copilot is designed to learn new skills. For example, with Viva Sales, Copilot can learn how to connect to CRM systems of record to pull customer data — like interaction and order histories — into communications. As Copilot learns about new domains and processes, it will be able to perform even more sophisticated tasks and queries.

Committed to building responsibly

At Microsoft, we are guided by our AI principles and Responsible AI Standard and decades of research on AI, grounding and privacy-preserving machine learning. A multidisciplinary team of researchers, engineers and policy experts reviews our AI systems for potential harms and mitigations — refining training data, filtering to limit harmful content, query- and result-blocking sensitive topics, and applying Microsoft technologies like InterpretML and Fairlearn to help detect and correct data bias. We make it clear how the system makes decisions by noting limitations, linking to sources, and prompting users to review, fact-check and adjust content based on subject-matter expertise.

Moving boldly as we learn  

In the months ahead, we’re bringing Copilot to all our productivity apps—Word, Excel, PowerPoint, Outlook, Teams, Viva, Power Platform, and more. We’ll share more on pricing and licensing soon. Earlier this month we announced Dynamics 365 Copilot as the world’s first AI Copilot in both CRM and ERP to bring the next-generation AI to every line of business.

Everyone deserves to find purpose and meaning in their work — and Microsoft 365 Copilot can help. To serve the unmet needs of our customers, we must move quickly and responsibly, learning as we go. We’re testing Copilot with a small group of customers to get feedback and improve our models as we scale, and we will expand to more soon.

Learn more on the Microsoft 365 blog and visit WorkLab to get expert insights on how AI will create a brighter future of work for everyone.

And for all the blogs, videos and assets related to today’s announcements, please visit our microsite .

Tags: AI , Microsoft 365 , Microsoft 365 Copilot

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What is generative AI?

In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis  and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled  over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

Get to know and directly engage with McKinsey's senior experts on generative AI

Aamer Baig is a senior partner in McKinsey’s Chicago office;  Lareina Yee  is a senior partner in the Bay Area office; and senior partners  Alex Singla  and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, are based in the Chicago and London offices, respectively.

Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion  to the global economy—annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective .

But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.

To stay up to date on this critical topic, sign up for email alerts on “artificial intelligence” here .

Learn more about QuantumBlack , AI by McKinsey.

What every CEO should know about generative AI

What’s the difference between machine learning and artificial intelligence, about quantumblack, ai by mckinsey.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning’s potential , as well as the need for it.

What are the main types of machine learning models?

Machine learning is founded on a number of building blocks, starting with classical statistical techniques  developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

Introducing McKinsey Explainers : Direct answers to complex questions

How do text-based machine learning models work how are they trained.

ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner .

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media  posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?

Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt . OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world’s best computer scientists and engineers.

But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a “ solid A- ” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza . Other generative AI models can produce code, video, audio, or business simulations .

But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.

What kinds of problems can a generative AI model solve?

The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?

Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks  involved in using them—some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities  is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate  to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

Articles referenced include:

• " Implementing generative AI with speed and safety ,” March 13, 2024, Oliver Bevan, Michael Chui , Ida Kristensen , Brittany Presten, and Lareina Yee
• “ Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom ,” February 22, 2024, Venkat Atluri , Peter Dahlström , Brendan Gaffey , Víctor García de la Torre, Noshir Kaka , Tomás Lajous , Alex Singla , Alex Sukharevsky , Andrea Travasoni , and Benjamim Vieira
• “ As gen AI advances, regulators—and risk functions—rush to keep pace ,” December 21, 2023, Andreas Kremer, Angela Luget, Daniel Mikkelsen , Henning Soller , Malin Strandell-Jansson, and Sheila Zingg
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ Exploring opportunities in the generative AI value chain ,” April 26, 2023, Tobias Härlin, Gardar Björnsson Rova , Alex Singla , Oleg Sokolov, and Alex Sukharevsky
• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022,  Michael Chui ,  Bryce Hall ,  Helen Mayhew , Alex Singla , and Alex Sukharevsky
• “ McKinsey Technology Trends Outlook 2023 ,” July 20, 2023,  Michael Chui , Mena Issler,  Roger Roberts , and  Lareina Yee  
• “ An executive’s guide to AI ,” Michael Chui , Vishnu Kamalnath, and Brian McCarthy
• “ What AI can and can’t do (yet) for your business ,” January 11, 2018,  Michael Chui , James Manyika , and Mehdi Miremadi

This article was updated in April 2024; it was originally published in January 2023.

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