criticism of five paragraph essay

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Ultimate guide to writing a five paragraph essay.

Are you struggling with writing essays? Do you find yourself lost in a sea of ideas, unable to structure your thoughts cohesively? The five paragraph essay is a tried-and-true method that can guide you through the writing process with ease. By mastering this format, you can unlock the key to successful and organized writing.

In this article, we will break down the five paragraph essay into easy steps that anyone can follow. From crafting a strong thesis statement to effectively supporting your arguments, we will cover all the essential components of a well-written essay. Whether you are a beginner or a seasoned writer, these tips will help you hone your skills and express your ideas clearly.

Step-by-Step Guide to Mastering the Five Paragraph Essay

Writing a successful five paragraph essay can seem like a daunting task, but with the right approach and strategies, it can become much more manageable. Follow these steps to master the art of writing a powerful five paragraph essay:

• Understand the structure: The five paragraph essay consists of an introduction, three body paragraphs, and a conclusion. Each paragraph serves a specific purpose in conveying your message effectively.
• Brainstorm and plan: Before you start writing, take the time to brainstorm ideas and create an outline. This will help you organize your thoughts and ensure that your essay flows smoothly.
• Write the introduction: Start your essay with a strong hook to grab the reader’s attention. Your introduction should also include a thesis statement, which is the main argument of your essay.
• Develop the body paragraphs: Each body paragraph should focus on a single point that supports your thesis. Use evidence, examples, and analysis to strengthen your argument and make your points clear.
• Conclude effectively: In your conclusion, summarize your main points and restate your thesis in a new way. Leave the reader with a thought-provoking statement or a call to action.

By following these steps and practicing regularly, you can become proficient in writing five paragraph essays that are clear, coherent, and impactful. Remember to revise and edit your work for grammar, punctuation, and clarity to ensure that your essay is polished and professional.

Understanding the Structure of a Five Paragraph Essay

When writing a five paragraph essay, it is important to understand the basic structure that makes up this type of essay. The five paragraph essay consists of an introduction, three body paragraphs, and a conclusion.

Introduction: The introduction is the first paragraph of the essay and sets the tone for the rest of the piece. It should include a hook to grab the reader’s attention, a thesis statement that presents the main idea of the essay, and a brief overview of what will be discussed in the body paragraphs.

Body Paragraphs: The body paragraphs make up the core of the essay and each paragraph should focus on a single point that supports the thesis statement. These paragraphs should include a topic sentence that introduces the main idea, supporting details or evidence, and explanations or analysis of how the evidence supports the thesis.

Conclusion: The conclusion is the final paragraph of the essay and it should summarize the main points discussed in the body paragraphs. It should restate the thesis in different words, and provide a closing thought or reflection on the topic.

By understanding the structure of a five paragraph essay, writers can effectively organize their thoughts and present their ideas in a clear and coherent manner.

Choosing a Strong Thesis Statement

One of the most critical elements of a successful five-paragraph essay is a strong thesis statement. Your thesis statement should clearly and concisely present the main argument or point you will be making in your essay. It serves as the foundation for the entire essay, guiding the reader on what to expect and helping you stay focused throughout your writing.

When choosing a thesis statement, it’s important to make sure it is specific, debatable, and relevant to your topic. Avoid vague statements or generalizations, as they will weaken your argument and fail to provide a clear direction for your essay. Instead, choose a thesis statement that is narrow enough to be effectively supported within the confines of a five-paragraph essay, but broad enough to allow for meaningful discussion.

By choosing a strong thesis statement, you set yourself up for a successful essay that is well-organized, coherent, and persuasive. Take the time to carefully craft your thesis statement, as it will serve as the guiding force behind your entire essay.

Developing Supporting Arguments in Body Paragraphs

When crafting the body paragraphs of your five paragraph essay, it is crucial to develop strong and coherent supporting arguments that back up your thesis statement. Each body paragraph should focus on a single supporting argument that contributes to the overall discussion of your topic.

To effectively develop your supporting arguments, consider using a table to organize your ideas. Start by listing your main argument in the left column, and then provide evidence, examples, and analysis in the right column. This structured approach can help you ensure that each supporting argument is fully developed and logically presented.

Additionally, be sure to use transitional phrases to smoothly connect your supporting arguments within and between paragraphs. Words like “furthermore,” “in addition,” and “on the other hand” can help readers follow your train of thought and understand the progression of your ideas.

Remember, the body paragraphs are where you provide the meat of your argument, so take the time to develop each supporting argument thoroughly and clearly. By presenting compelling evidence and analysis, you can effectively persuade your readers and strengthen the overall impact of your essay.

Polishing Your Writing: Editing and Proofreading Tips

Editing and proofreading are crucial steps in the writing process that can make a significant difference in the clarity and effectiveness of your essay. Here are some tips to help you polish your writing:

1. Take a break before editing: After you finish writing your essay, take a break before starting the editing process. This will help you approach your work with fresh eyes and catch mistakes more easily.

2. Read your essay aloud: Reading your essay aloud can help you identify awkward phrasing, grammar errors, and inconsistencies. This technique can also help you evaluate the flow and coherence of your writing.

3. Use a spelling and grammar checker: Utilize spelling and grammar checkers available in word processing software to catch common errors. However, be mindful that these tools may not catch all mistakes, so it’s essential to manually review your essay as well.

4. Check for coherence and organization: Make sure your ideas flow logically and cohesively throughout your essay. Ensure that each paragraph connects smoothly to the next, and that your arguments are supported by relevant evidence.

5. Look for consistency: Check for consistency in your writing style, tone, and formatting. Ensure that you maintain a consistent voice and perspective throughout your essay to keep your argument coherent.

6. Seek feedback from others: Consider asking a peer, teacher, or tutor to review your essay and provide feedback. External perspectives can help you identify blind spots and areas for improvement in your writing.

7. Proofread carefully: Finally, proofread your essay carefully to catch any remaining errors in spelling, grammar, punctuation, and formatting. Pay attention to details and make any necessary revisions before submitting your final draft.

By following these editing and proofreading tips, you can refine your writing and ensure that your essay is polished and ready for submission.

Tips for Successful Writing: Practice and Feedback

Writing is a skill that improves with practice. The more you write, the better you will become. Set aside time each day to practice writing essays, paragraph by paragraph. This consistent practice will help you develop your writing skills and grow more confident in expressing your ideas.

Seek feedback from your teachers, peers, or mentors. Constructive criticism can help you identify areas for improvement and provide valuable insights into your writing. Take their suggestions into consideration and use them to refine your writing style and structure.

• Set writing goals for yourself and track your progress. Whether it’s completing a certain number of essays in a week or improving your introductions, having specific goals will keep you motivated and focused on your writing development.
• Read widely to expand your vocabulary and expose yourself to different writing styles. The more you read, the more you will learn about effective writing techniques and ways to engage your readers.
• Revise and edit your essays carefully. Pay attention to sentence structure, grammar, punctuation, and spelling. A well-polished essay will demonstrate your attention to detail and dedication to producing high-quality work.

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5.5: The Five-Paragraph Essay is Rhetorically Sound

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• Cheryl E. Ball & Drew M. Loewe ed.
• West Virginia University via Digital Publishing Institute and West Virginia University Libraries

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Author: Quentin Vieregge, Department of English, University of Wisconsin–Barron County

The five-paragraph essay (5PE) doesn’t have many vocal defenders in Departments of English in higher education, but for some instructors, the 5PE remains a useful tool in the pedagogical kit. Most college writing instructors have eschewed the 5PE, contending that it limits what writing can be, constricts writers’ roles, and even arbitrarily shapes writers’ thoughts. Yet, defenders of the 5PE counter that beginning writers need the guidance and structure that it affords. It works, they say, and it gives writers a place from which to start.

The 5PE may sound familiar. In its most basic form, it is an introduction, three points, and a conclusion. Students are often given a topic to discuss, a passage to respond to, or a question to answer. The introduction and body paragraphs typically follow prescribed conventions regardless of content. For instance, the introduction has an attention-getter and explains what others have said about the topic, and the thesis usually comes close to the end of the paragraph. Each of the body paragraphs has a topic sentence that makes a claim that can be backed up with evidence and that refers back to the thesis. Each topic sentence is followed by sentences that provide evidence and reinforce the thesis. The body paragraphs end with a wrap-up sentence. The conclusion reminds the reader of the main idea, summarizes the main points, and might even leave the reader with one lasting impression.

If all that sounds familiar, then it might be because you were taught the 5PE. Defenders of the 5PE can sometimes be found in high schools or two-year colleges, where they might work with students who struggle with writing or are learning English as a second language. One such teacher, David Gugin, writes about how the five-paragraph model benefits students learning English as a second language. Like many proponents of the 5PE, he assumes that the main impediment to expressing an idea is knowing how to organize it. As he puts it, “Once they have the vessel, so to speak, they can start thinking more about what to fill it with.”

This type of metaphor abounds. Byung-In Seo compares writing to building a house: One builds a basic structure and the individual spark comes from personalizing the details, either decorating the house or the content of the essay. She refers particularly to her experience with at-risk students, usually meaning students who come into college without the writing skills needed to immediately dive into college-level work. Similarly, Susanna L. Benko describes the 5PE as scaffolding that can either enhance or hinder student learning. A scaffold can be useful as construction workers move about when working on a building, but it should be removed when the building can stand on its own; the problem, as Benko observes, is when neither teacher nor student tears down the scaffold.

Here is the thing, though: When writers (and critics) talk about the 5PE, they’re not really talking about five paragraphs any more than critics or proponents of fast-food restaurants are talking about McDonald’s. Most defenders of the 5PE will either explicitly or implicitly see the sentence, the paragraph, and the essay as reflections of each other. Just as an essay has a thesis, a paragraph has a topic sentence; just as a paper has evidence to support it, a paragraph has detail. An essay has a beginning, middle, and end; so does a paragraph. To quote a line from William Blake, to be a defender of the 5PE is “To see a World in a Grain of Sand.” There are circles within circles within circles from this perspective. If you take this approach to writing, form is paramount. Once you understand the form, you can say anything within it.

This focus on form first (and on the use of the 5PE) is a hallmark of what composition scholars call the current-traditional approach to writing instruction. The current-traditional approach is traceable to the late 19th century, but still persists today in the 5PE and in writing assignments and textbooks organized around a priori modes of writing (the modes being definition, argument, exposition, and narrative). Current-traditional rhetoric valorizes form, structure, and arrangement over discovering and developing ideas. In current-traditional pedagogy, knowledge does not need to be interpreted or analyzed, but merely apprehended. Writing processes are mostly about narrowing and defining ideas and about applying style as external dressing to a finished idea.

Detractors of the 5PE claim that it all but guarantees that writing will be a chore. What fun is it to write when you have no choices, when the shape of your words and thoughts are controlled by an impersonal model that everyone uses, but only in school? Teaching the 5PE is like turning students into Charlie Chaplin’s character from Modern Times , stuck in the gears of writing. The 5PE allegedly dehumanizes people. A number of writing specialists from University of North Carolina–Charlotte wrote an article called, “The Five-Paragraph Essay and the Deficit Model of Education.” One of their critiques is that this model means that students aren’t taught to think and feel fully; rather they’re taught to learn their place as future workers in an assembly line economy: topic sentence, support, transition, repeat. Finally, as several writing instructors have observed, the 5PE doesn’t comport with reality. Who actually writes this way? Who actually reads this way? Does anyone care if an essay in The Atlantic or David Sedaris’s non-fiction collection Me Talk Pretty One Day doesn’t follow some prescriptive model? If the model doesn’t connect to how people actually write when given a choice, then how useful can it be?

Well, as it happens, formulaic writing has some support. Two such people who support it are Gerald Graff and Cathy Birkenstein, coauthors of one of a celebrated writing textbook, They Say/I Say . Graff and Birkenstein’s book rests on the assumption that all writers—especially skilled writers—use templates, which they’ve learned over time. For instance, there are templates for thesis sentences, templates for counterarguments, templates for rebuttals, templates for introducing quotes, and templates for explaining what quotations mean. One example from their book is this: “While they rarely admit as much, __________ often take for granted that _______,” which is a template students might use to begin writing their paper. Students are supposed to plug their own thoughts into the blanks to help them express their thoughts. Graff and Birkenstein tackle the issue of whether templates inhibit creativity. They make several of the same arguments that proponents of the 5PE make: Skilled writers use templates all the time; they actually enhance creativity; and they’re meant to guide and inspire rather than limit. This doesn’t mean Graff and Birkenstein love the 5PE, though. In an article in The Chronicle of Higher Education , they contend that templates are an accurate reflection of how people write because templates are dialogic, but the 5PE is not.

Formulas, including templates, can be effective, and arbitrary formulas can be useful under the right circumstances too. They can be useful if they are used as a point-of-inquiry, meaning if writers use them as a starting place rather than a destination when writing. In what ways does the five-paragraph model work for this particular assignment? How should I deviate from it? Should I have an implied thesis rather than an explicit one?

Now, you might be thinking, that’s well and good for beginning students, but what about advanced students or professionals? They never use formulas. Well, when my proposal for this piece was accepted, the two editors sent me explicit instructions about how to organize the essay. They divided their instructions into “first paragraph,” “middle paragraphs,” and “later paragraphs,” and then instructions about what comes after the essay. Within each part, they gave specific directions; everything was spelled out. I had a problem; I planned to argue in favor of the five-paragraph essay, so I couldn’t use their formula, which presupposed I would argue against the bad idea.

Hmm. That conundrum required me to ask myself questions, to inquire. How should I innovate from the model? How should I not? Their prescriptive advice was a point-of-inquiry for me that forced me to think rhetorically and creatively. Maybe the five-paragraph model can be a point-of-inquiry—a way to start asking questions about rhetoric and writing. When I wrote this piece, I asked myself, “Why do the editors want me to write using a specific format?” And I then asked, “In what ways does this format prevent or enable me from making my point?” Finally, I asked, “In what ways can I exploit the tension between what they want me to do and what I feel I must do?” Asking these questions forced me to think about audience and purpose. But, perhaps more crucially, I was forced to think of the editors’ purpose, not just my own. By understanding their purpose, the format was more than an arbitrary requirement but an artifact indicating a dynamic rhetorical context that I, too, played a role in.

Once I understood the purpose behind the format for this essay, I could restructure it in purposeful and creative ways. The 5PE follows the same logic. Teachers often, mistakenly, think of it as an arbitrary format, but it’s only arbitrary if students and teachers don’t converse and reflect on its purpose. Once students consider their teacher’s purpose in assigning it, then the format becomes contextualized in consideration of audience, purpose, and context, and students are able to negotiate the expectations of the model with their own authorial wishes.

Further Reading

If you’re interested in reading defenses for the 5PE, you might start with Byung-In Seo’s “Defending the Five-Paragraph Essay.” A longer more formal argument in favor of the 5PE can be found in David Gugin’s “A Paragraph-First Approach to the Teaching of Academic Writing.” In the essay, “In Teaching Composition, ‘Formulaic’ Is Not a 4-Letter Word,” Cathy Birkenstein and Gerald Graff criticize the 5PE but defend writing formulas done in more rhetorically effective ways.

For more information about the connection between the five-paragraph essay and current-traditional rhetoric, you might read Michelle Tremmel’s “What to Make of the Five-Paragraph Theme: History of the Genre and Implications.” For a critique of the 5PE, you might read Lil Brannon et al.’s “The Five-Paragraph Essay and the Deficit Model of Education.”

Defenses of the five-paragraph theme often frame the genre as a scaffolding device. Susanna Benko’s essay, “Scaffolding: An Ongoing Process to Support Adolescent Writing Development,” explains the importance of scaffolding and how that technique can be misapplied. Though her essay only partially addresses the 5PE, her argument can be applied to the genre’s potential advantages and disadvantages.

basic writing, current-traditional rhetoric, discursive writing, five-paragraph essay (or theme), prescriptivism

Quentin Vieregge is a faculty member in the Department of English at the University of Wisconsin–Barron County, a two-year liberal arts college. He teaches first-year composition, advanced composition, business writing, literature, and film courses. He can be followed on Twitter at @Vieregge. His website is quentinvieregge.com.

The Ultimate Guide to the 5-Paragraph Essay

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• M.Ed., Education Administration, University of Georgia
• B.A., History, Armstrong State University

A five-paragraph essay is a prose composition that follows a prescribed format of an introductory paragraph, three body paragraphs, and a concluding paragraph, and is typically taught during primary English education and applied on standardized testing throughout schooling.

Learning to write a high-quality five-paragraph essay is an essential skill for students in early English classes as it allows them to express certain ideas, claims, or concepts in an organized manner, complete with evidence that supports each of these notions. Later, though, students may decide to stray from the standard five-paragraph format and venture into writing an  exploratory essay  instead.

Still, teaching students to organize essays into the five-paragraph format is an easy way to introduce them to writing literary criticism, which will be tested time and again throughout their primary, secondary, and further education.

Writing a Good Introduction

The introduction is the first paragraph in your essay, and it should accomplish a few specific goals: capture the reader's interest, introduce the topic, and make a claim or express an opinion in a thesis statement.

It's a good idea to start your essay with a hook (fascinating statement) to pique the reader's interest, though this can also be accomplished by using descriptive words, an anecdote, an intriguing question, or an interesting fact. Students can practice with creative writing prompts to get some ideas for interesting ways to start an essay.

The next few sentences should explain your first statement, and prepare the reader for your thesis statement, which is typically the last sentence in the introduction. Your  thesis sentence  should provide your specific assertion and convey a clear point of view, which is typically divided into three distinct arguments that support this assertation, which will each serve as central themes for the body paragraphs.

Writing Body Paragraphs

The body of the essay will include three body paragraphs in a five-paragraph essay format, each limited to one main idea that supports your thesis.

To correctly write each of these three body paragraphs, you should state your supporting idea, your topic sentence, then back it up with two or three sentences of evidence. Use examples that validate the claim before concluding the paragraph and using transition words to lead to the paragraph that follows — meaning that all of your body paragraphs should follow the pattern of "statement, supporting ideas, transition statement."

Words to use as you transition from one paragraph to another include: moreover, in fact, on the whole, furthermore, as a result, simply put, for this reason, similarly, likewise, it follows that, naturally, by comparison, surely, and yet.

Writing a Conclusion

The final paragraph will summarize your main points and re-assert your main claim (from your thesis sentence). It should point out your main points, but should not repeat specific examples, and should, as always, leave a lasting impression on the reader.

The first sentence of the conclusion, therefore, should be used to restate the supporting claims argued in the body paragraphs as they relate to the thesis statement, then the next few sentences should be used to explain how the essay's main points can lead outward, perhaps to further thought on the topic. Ending the conclusion with a question, anecdote, or final pondering is a great way to leave a lasting impact.

Once you complete the first draft of your essay, it's a good idea to re-visit the thesis statement in your first paragraph. Read your essay to see if it flows well, and you might find that the supporting paragraphs are strong, but they don't address the exact focus of your thesis. Simply re-write your thesis sentence to fit your body and summary more exactly, and adjust the conclusion to wrap it all up nicely.

Practice Writing a Five-Paragraph Essay

Students can use the following steps to write a standard essay on any given topic. First, choose a topic, or ask your students to choose their topic, then allow them to form a basic five-paragraph by following these steps:

• Decide on your  basic thesis , your idea of a topic to discuss.
• Decide on three pieces of supporting evidence you will use to prove your thesis.
• Write an introductory paragraph, including your thesis and evidence (in order of strength).
• Write your first body paragraph, starting with restating your thesis and focusing on your first piece of supporting evidence.
• End your first paragraph with a transitional sentence that leads to the next body paragraph.
• Write paragraph two of the body focussing on your second piece of evidence. Once again make the connection between your thesis and this piece of evidence.
• End your second paragraph with a transitional sentence that leads to paragraph number three.
• Repeat step 6 using your third piece of evidence.
• Begin your concluding paragraph by restating your thesis. Include the three points you've used to prove your thesis.
• End with a punch, a question, an anecdote, or an entertaining thought that will stay with the reader.

Once a student can master these 10 simple steps, writing a basic five-paragraph essay will be a piece of cake, so long as the student does so correctly and includes enough supporting information in each paragraph that all relate to the same centralized main idea, the thesis of the essay.

Limitations of the Five-Paragraph Essay

The five-paragraph essay is merely a starting point for students hoping to express their ideas in academic writing; there are some other forms and styles of writing that students should use to express their vocabulary in the written form.

According to Tory Young's "Studying English Literature: A Practical Guide":

"Although school students in the U.S. are examined on their ability to write a  five-paragraph essay , its  raison d'être  is purportedly to give practice in basic writing skills that will lead to future success in more varied forms. Detractors feel, however, that writing to rule in this way is more likely to discourage imaginative writing and thinking than enable it. . . . The five-paragraph essay is less aware of its  audience  and sets out only to present information, an account or a kind of story rather than explicitly to persuade the reader."

Students should instead be asked to write other forms, such as journal entries, blog posts, reviews of goods or services, multi-paragraph research papers, and freeform expository writing around a central theme. Although five-paragraph essays are the golden rule when writing for standardized tests, experimentation with expression should be encouraged throughout primary schooling to bolster students' abilities to utilize the English language fully.

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This form of writing goes by different names. Maybe you've heard some of them before: "The Basic Essay," "The Academic Response Essay," "The 1-3-1 Essay." Regardless of what you've heard, the name you should remember is "The Easy Essay."

Once you are shown how this works--and it only takes a few minutes--you will have in your hands the secret to writing well on almost any academic assignment. Here is how it goes.

Secret #1—The Magic of Three

Three has always been a magic number for humans, from fairy tales like "The Three Little Pigs" to sayings like “third time’s a charm.” Three seems to be an ideal number for us--including the academic essay. So whenever you are given a topic to write about, a good place to begin is with a list of three. Here are some examples (three of them, of course):

Topic : What are the essential characteristics of a good parent? Think in threes and you might come up with:

• unconditional love 

Certainly, there are more characteristics of good parents you could name, but for our essay, we will work in threes.

Here's a topic that deals with a controversial issue:

Topic : Should women in the military be given frontline combat duties?

• The first reason that women should be assigned to combat is equality. 
• The second reason is their great teamwork. 
• The third reason is their courage.

As you see, regardless of the topic, we can list three points about it. And if you wonder about the repetition of words and structure when stating the three points, in this case, repetition is a good thing. Words that seem redundant when close together in an outline will be separated by the actual paragraphs of your essay. So in the essay instead of seeming redundant they will be welcome as signals to the reader of your essay’s main parts.

Finally, when the topic is an academic one, your first goal is the same: create a list of three.

Topic: Why do so many students fail to complete their college degree?

• First, students often...
• Second, many students cannot...
• Finally, students find that...

Regardless of the reasons you might come up with to finish these sentences, the formula is still the same.

Secret #2: The Thesis Formula

Now with your list of three, you can write the sentence that every essay must have—the thesis, sometimes called the "controlling idea," "overall point," or "position statement." In other words, it is the main idea of the essay that you will try to support, illustrate, or corroborate.

Here’s a simple formula for a thesis: The topic + your position on the topic = your thesis.

Let’s apply this formula to one of our examples:

Topic: Essential characteristics of a good parent Your Position: patience, respect, love Thesis: The essential characteristics of a good parent are patience, respect, and love.

As you see, all we did was combine the topic with our position/opinion on it into a single sentence to produce the thesis: The essential characteristics of a good parent are patience, respect, and love.

In this case, we chose to list three main points as part of our thesis. Sometimes that’s a good strategy. However, you can summarize them if you wish, as in this example:

Topic: Women in combat duty in the military Your Position: They deserve it Thesis: Women deserve to be assigned combat duty in the military.

This type of thesis is shorter and easier to write because it provides the overall position or opinion without forcing you to list the support for it in the thesis, which can get awkward and take away from your strong position statement. The three reasons women deserve to be assigned combat duties--equality, teamwork, courage--will be the subjects of your three body paragraphs and do not need to be mentioned until the body paragraph in which they appear.

Secret #3: The 1-3-1 Outline

With your thesis and list of three main points, you can quickly draw a basic outline of the paragraphs of your essay. You’ll then see why this is often called the 1-3-1 essay.

• Supporting Evidence for Claim 1    
• Supporting Evidence for Claim 2
• Supporting Evidence for Claim 3

The five-paragraph essay consists of one introduction paragraph (with the thesis at its end), three body paragraphs (each beginning with one of three main points) and one last paragraph—the conclusion. 1-3-1.

Once you have this outline, you have the basic template for most academic writing. Most of all, you have an organized way to approach virtually any topic you are assigned.

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Guide on How to Write a 5 Paragraph Essay Effortlessly

Defining What Is a 5 Paragraph Essay

Have you ever been assigned a five-paragraph essay and wondered what exactly it means? Don't worry; we all have been there. A five-paragraph essay is a standard academic writing format consisting of an introduction, three body paragraphs, and a conclusion.

In the introduction, you present your thesis statement, which is the main idea or argument you will discuss in your essay. The three body paragraphs present a separate supporting argument, while the conclusion summarizes the main points and restates the thesis differently.

While the five-paragraph essay is a tried and true format for many academic assignments, it's important to note that it's not the only way to write an essay. In fact, some educators argue that strict adherence to this format can stifle creativity and limit the development of more complex ideas.

However, mastering the five-paragraph essay is a valuable skill for any student, as it teaches the importance of structure and organization in writing. Also, it enables you to communicate your thoughts clearly and eloquently, which is crucial for effective communication in any area. So the next time you're faced with a five-paragraph essay assignment, embrace the challenge and use it as an opportunity to hone your writing skills.

And if you find it difficult to put your ideas into 5 paragraphs, ask our professional service - 'please write my essay ,' or ' write my paragraph ' and consider it done.

How to Write a 5 Paragraph Essay: General Tips

If you are struggling with how to write a 5 paragraph essay, don't worry! It's a common format that many students learn in their academic careers. Here are some tips from our admission essay writing service to help you write a successful five paragraph essay example:

• Start with a strong thesis statement : Among the 5 parts of essay, the thesis statement can be the most important. It presents the major topic you will debate throughout your essay while being explicit and simple.
• Use topic sentences to introduce each paragraph : The major idea you will address in each of the three body paragraphs should be established in a concise subject sentence.
• Use evidence to support your arguments : The evidence you present in your body paragraphs should back up your thesis. This can include facts, statistics, or examples from your research or personal experience.
• Include transitions: Use transitional words and phrases to make the flow of your essay easier. Words like 'although,' 'in addition,' and 'on the other hand' are examples of these.
• Write a strong conclusion: In addition to restating your thesis statement in a new way, your conclusion should highlight the key ideas of your essay. You might also leave the reader with a closing idea or query to reflect on.
• Edit and proofread: When you've completed writing your essay, thoroughly revise and proofread it. Make sure your thoughts are brief and clear and proofread your writing for grammatical and spelling mistakes.

By following these tips, you can write strong and effective five paragraph essays examples that will impress your teacher or professor.

5 Paragraph Essay Format

Let's readdress the five-paragraph essay format and explain it in more detail. So, as already mentioned, it is a widely-used writing structure taught in many schools and universities. A five-paragraph essay comprises an introduction, three body paragraphs, and a conclusion, each playing a significant role in creating a well-structured and coherent essay.

The introduction serves as the opening paragraph of the essay and sets the tone for the entire piece. It should captivate the reader's attention, provide relevant background information, and include a clear and concise thesis statement that presents the primary argument of the essay. For example, if the essay topic is about the benefits of exercise, the introduction may look something like this:

'Regular exercise provides numerous health benefits, including increased energy levels, improved mental health, and reduced risk of chronic diseases.'

The body paragraphs are the meat of the essay and should provide evidence and examples to support the thesis statement. Each body paragraph should begin with a subject sentence that states the major idea of the paragraph. Then, the writer should provide evidence to support the topic sentence. This evidence can be in the form of statistics, facts, or examples. For instance, if the essay is discussing the health benefits of exercise, a body paragraph might look like this:

'One of the key benefits of exercise is improved mental health. Regular exercise has been demonstrated in studies to lessen depressive and anxious symptoms and enhance mood.'

The essay's final paragraph, the conclusion, should repeat the thesis statement and summarize the essay's important ideas. A concluding idea or query might be included to give the reader something to ponder. For example, a conclusion for an essay on the benefits of exercise might look like this:

'In conclusion, exercise provides numerous health benefits, from increased energy levels to reduced risk of chronic diseases. We may enhance both our physical and emotional health and enjoy happier, more satisfying lives by including exercise into our daily routines.'

Overall, the 5 paragraph essay format is useful for organizing thoughts and ideas clearly and concisely. By following this format, writers can present their arguments logically and effectively, which is easy for the reader to follow.

Types of 5 Paragraph Essay 

There are several types of five-paragraph essays, each with a slightly different focus or purpose. Here are some of the most common types of five-paragraph essays:

• Narrative essay : A narrative essay tells a story or recounts a personal experience. It typically includes a clear introductory paragraph, body sections that provide details about the story, and a conclusion that wraps up the narrative.
• Descriptive essay: A descriptive essay uses sensory language to describe a person, place, or thing. It often includes a clear thesis statement that identifies the subject of the description and body paragraphs that provide specific details to support the thesis.
• Expository essay: An expository essay offers details or clarifies a subject. It usually starts with a concise introduction that introduces the subject, is followed by body paragraphs that provide evidence and examples to back up the thesis, and ends with a summary of the key points.
• Persuasive essay: A persuasive essay argues for a particular viewpoint or position. It has a thesis statement that is clear, body paragraphs that give evidence and arguments in favor of it, and a conclusion that summarizes the important ideas and restates the thesis.
• Compare and contrast essay: An essay that compares and contrasts two or more subjects and looks at their similarities and differences. It usually starts out simply by introducing the topics being contrasted or compared, followed by body paragraphs that go into more depth on the similarities and differences, and a concluding paragraph that restates the important points.

Each type of five-paragraph essay has its own unique characteristics and requirements. When unsure how to write five paragraph essay, writers can choose the most appropriate structure for their topic by understanding the differences between these types.

5 Paragraph Essay Example Topics

Here are some potential topics for a 5 paragraph essay example. These essay topics are just a starting point and can be expanded upon to fit a wide range of writing essays and prompts.

• The Impact of Social Media on Teenage Communication Skills.
• How Daily Exercise Benefits Mental and Physical Health.
• The Importance of Learning a Second Language.
• The Effects of Global Warming on Marine Life.
• The Role of Technology in Modern Education.
• The Influence of Music on Youth Culture.
• The Pros and Cons of Uniform Policies in Schools.
• The Significance of Historical Monuments in Cultural Identity.
• The Growing Importance of Cybersecurity.
• The Evolution of the American Dream.
• The Impact of Diet on Cognitive Functioning.
• The Role of Art in Society.
• The Future of Renewable Energy Sources.
• The Effects of Urbanization on Wildlife.
• The Importance of Financial Literacy for Young Adults.
• The Influence of Advertising on Consumer Choices.
• The Role of Books in the Digital Age.\
• The Benefits and Challenges of Space Exploration.
• The Impact of Climate Change on Agriculture.
• The Ethical Implications of Genetic Modification.

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General Grading Rubric for a 5 Paragraph Essay

The following is a general grading rubric that can be used to evaluate a five-paragraph essay:

Content (40%)

• A thesis statement is clear and specific
• The main points are well-developed and supported by evidence
• Ideas are organized logically and coherently
• Evidence and examples are relevant and support the main points
• The essay demonstrates a strong understanding of the topic

Organization (20%)

• The introduction effectively introduces the topic and thesis statement
• Body paragraphs are well-structured and have clear topic sentences
• Transitions between paragraphs are smooth and effective
• The concluding sentence effectively summarizes the main points and restates the thesis statement

Language and Style (20%)

• Writing is clear, concise, and easy to understand
• Language is appropriate for the audience and purpose
• Vocabulary is varied and appropriate
• Grammar, spelling, and punctuation are correct

Critical Thinking (20%)

• Student demonstrate an understanding of the topic beyond surface-level knowledge
• Student present a unique perspective or argument
• Student show evidence of critical thinking and analysis
• Students write well-supported conclusions

Considering the above, the paper should demonstrate a thorough understanding of the topic, clear organization, strong essay writing skills, and critical thinking. By using this grading rubric, the teacher can evaluate the essay holistically and provide detailed feedback to the student on areas of strength and areas for improvement.

Five Paragraph Essay Examples

Wrapping up: things to remember.

In conclusion, writing a five paragraph essay example can seem daunting at first, but it doesn't have to be a difficult task. Following these simple steps and tips, you can break down the process into manageable parts and create a clear, concise, and well-organized essay.

Remember to start with a strong thesis statement, use topic sentences to guide your paragraphs, and provide evidence and analysis to support your ideas. Don't forget to revise and proofread your work to make sure it is error-free and coherent. With time and practice, you'll be able to write a 5 paragraph essay with ease and assurance. Whether you're writing for school, work, or personal projects, these skills will serve you well and help you to communicate your ideas effectively.

Meanwhile, you can save time and reduce the stress associated with academic assignments by trusting our research paper writing services to handle the writing for you. So go ahead, buy an essay , and see how easy it can be to meet all of your professors' complex requirements!

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is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

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Microsoft 365 Life Hacks > Writing > The pros and cons of the five-paragraph essay

The pros and cons of the five-paragraph essay

The five-paragraph essay is a writing structure typically taught in high school. Structurally, it consists of an introductory paragraph, three body paragraphs, and a conclusion. This clear structure helps students connect points into a succinct argument. It’s a great introductory structure, but only using this writing formula has its limitations.

What is a five-paragraph essay?

Outside of the self-titled structure, the five-paragraph essay has additional rules. To start, your introductory paragraph should include a hook to captivate your audience. It should also introduce your thesis , or the argument you are proving. The thesis should be one sentence, conclude your introductory paragraph, and include supporting points. These points will become the body of your essay. The body paragraphs should introduce a specific point, include examples and supporting information, and then conclude. This process is repeated until you reach the fifth concluding paragraph, in which you summarize your essay.

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The benefits of a five-paragraph essay

• Your ideas are clear. Presenting your ideas in a succinct, organized manner makes them easy to understand and the five-paragraph essay is designed for that. It provides a clear outline to follow. And most importantly, it’s organized around the thesis, so the argument can be traced from the beginning of the essay to its conclusion. When learning how to write essays, losing track of your thesis can be a common mistake. By using this structure, it’s harder to go on tangents. Each of your points are condensed into a single paragraph. If you struggle presenting your ideas, following this structure might be your best bet.
• It’s simple. Creating an essay structure takes additional brainpower and time to craft. If an essay is timed in an exam, relying on this method is helpful. You can quickly convey your ideas so you can spend more time writing and less structuring your essay.
• It helps build your writing skills. If you’re new to writing essays, this is a great tool. Since the structure is taken care of, you can practice writing and build your skills. Learn more writing tips to improve your essays.

The cons of writing a five-paragraph essay

• The structure is rigid. Depending on its usage, the structure and convention of the five paragraphs can make creating an essay easier to understand and write. However, for writing outside of a traditional high school essay, this format can be limiting. To illustrate points creatively, you might want to create a different structure to illustrate your argument.
• Writing becomes repetitive. This format quickly becomes repetitive. Moving from body-to-body paragraph using the same rules and format creates a predictable rhythm. Reading this predictable format can become dull. And if you’re writing for a college professor, they will want you to showcase creativity in your writing. Try using a different essay structure to make your writing more interesting
• Lack of transitions. Quickly moving through ideas in a five-paragraph structure essay doesn’t always leave room for transitions. The structure is too succinct. Each paragraph only leaves enough space for a writer to broadly delve into an idea and then move onto the next. In longer essays, you can use additional paragraphs to connect ideas. Without transitions, essays in this format can feel choppy, as each point is detached from the previous one
• Its rules can feel unnecessary. Breaking your essay into three body paragraphs keeps it concise. But is three the perfect number of body paragraphs? Some arguments might need more support than three points to substantiate them. Limiting your argument to three points can weaken its credibility and can feel arbitrary for a writer to stick to.

Creating essays using the five-paragraph structure is situational. Use your best judgement to decide when to take advantage of this essay formula. If you’re writing on a computer with Microsoft Word , try using Microsoft Editor to edit your essay.

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Chapter 4: Structuring, Paragraphing, and Styling

4.10 A review of the five-paragraph essay

Julie A. Townsend

A review of the five-paragraph format

Many writers will be able to detail the five-paragraph format.

• The introduction previews the entire essay.
• The thesis statement goes at the end of the introduction and describes what the three body paragraphs will be about.
• The body paragraphs discuss each topic described in the thesis statement in detail.
• There should be transitions between each body paragraph.
• The conclusion repeats key points made in the essay and could be the introduction re-worded in a different way.

These are sample answers from writers who describe what they have learned about the five-paragraph format. The five-paragraph format is a reader-friendly organizational structure that writers can rely on if they need to get information quickly and formally across to a wide audience. For instance, the five-paragraph format might be useful when writing a report to a supervisor with the purpose of explaining progress on a project. The introduction gives the most important information at the beginning and each paragraph is clearly related to one topic. The conclusion leaves the reader with a summary and a possible call to action.

Problems with the five-paragraph format

Donald Murray in his article “Teach Writing as a Process Not a Product”argues writing should be taught as “…the process of discovery through language. It is the process of exploration of what we know and what we feel about what we know through language. It is the process of using language to learn about our world, to evaluate what we learn about our world, to communicate what we learn about our world” (4). However, when writers use writing to discover more about a topic, the five-paragraph format can be limiting because of the following:

• Writers usually decide on the three main body paragraphs before they start drafting.
• With three large topics to change from, writers are less likely to dig deep into a specific topic.
• Writers may use the five-paragraph format in ways that avoid detail and make their essays indistinguishable from other essays on the same topics.

For instance, a writer might want to discuss communication on social media. They decide before they start writing that their three main topics will be Facebook, Instagram, and Twitter. By the time they write a few details about Facebook, they move on to Instagram. There is not enough space for the writer to get into details and differentiate ideas on their topic and to describe observations, experiences, and research in depth. When writers start with three main topics, it’s also hard to find the space to teach the reader something new.

Discover with writing

Writing is an opportunity to share unique experiences with readers. If writers feel like they are not sharing anything valuable through their writing, they should reconsider their stance on the assignment or schedule a meeting with their instructor so that they can orient themselves more meaningfully to the assignment. Often, five-paragraph format writing is uninspired. Writers race to jot down what they know on three loosely related subjects so that they can finish the essay. The writer is not learning through the writing and neither is the reader. The main problem with the five-paragraph format is that it discourages writers from discovering what they could write on one focused and specific topic.

When to use the five-paragraph format

Published essays, in any genre, that use the five-paragraph organization are very rare. It might be interesting for writers to pay attention to how published material that they read on their own time is organized. Because first-year writing is a context where writers are encouraged to learn and teach through their writing, the five-paragraph format might not be the best choice for organization. However, when writers are in situations that demand them to relay information quickly, the five-paragraph format can be useful. Formulaic writing is not uninventive or inherently bad. Different genres use various kinds of formulaic writing, and it’s important for writers to adhere to conventions and pay attention to how essays are organized in the genre they are writing in.

Works Cited

Murray, Donald. “Teach writing as a process not product.” The Leaflet, vol .71, no.3 (1972), pp. 11-14.

A Guide to Rhetoric, Genre, and Success in First-Year Writing by Julie A. Townsend is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Structuring the Five-Paragraph Essay: Examples of Five-Paragraph Essays

• Examples of Five-Paragraph Essays

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Sample of a Persuasive / Argumentative Five-Paragraph Essay

A cat is a man's best friend.

This model essay is a good example of an Argumentative (or Persuasive) Essay. 

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How to Write a Critique in Five Paragraphs

Last Updated: January 20, 2024 Fact Checked

This article was co-authored by Diane Stubbs . Diane Stubbs is a Secondary English Teacher with over 22 years of experience teaching all high school grade levels and AP courses. She specializes in secondary education, classroom management, and educational technology. Diane earned a Bachelor of Arts in English from the University of Delaware and a Master of Education from Wesley College. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 974,442 times.

A critique is usually written in response to a creative work, such as a novel, a film, poetry, or a painting. However, critiques are also sometimes assigned for research articles and media items, such as news articles or features. A critique is slightly different than a traditional 5-paragraph theme, as it is usually focused on the overall effectiveness and usefulness of the work it is critiquing, rather than making a strictly analytical argument about it. Organizing your critique into 5 paragraphs can help you structure your thoughts.

Laying the Groundwork

• Does the creator clearly state her/his main point or goal? If not, why do you think that is?
• Who do you think is the creator’s intended audience? This can be crucial to determining the success of a work; for example, a movie intended for young children might work well for its intended audience but not for adult viewers.
• What reactions do you have when reading or viewing this work? Does it provoke emotional responses? Do you feel confused?
• What questions does the work make you think of? Does it suggest other avenues of exploration or observation to you?

• For example, if you're critiquing a research article about a new treatment for the flu, a little research about other flu treatments currently available could be helpful to you when situating the work in context.
• As another example, if you're writing about a movie, you might want to briefly discuss the director's other films, or other important movies in this particular genre (indie, action, drama, etc.).
• Your school or university library is usually a good place to start when conducting research, as their databases provide verified, expert sources. Google Scholar can also be a good source for research.

Writing the Introductory Paragraph

• For a work of fiction or a published work of journalism or research, this information is usually available in the publication itself, such as on the copyright page for a novel.
• For a film, you may wish to refer to a source such as IMDb to get the information you need. If you're critiquing a famous artwork, an encyclopedia of art would be a good place to find information on the creator, the title, and important dates (date of creation, date of exhibition, etc.).

• For example, if you’re assessing a research article in the sciences, a quick overview of its place in the academic discussion could be useful (e.g., “Professor X’s work on fruit flies is part of a long research tradition on Blah Blah Blah.”)
• If you are evaluating a painting, giving some brief information on where it was first displayed, for whom it was painted, etc., would be useful.
• If you are assessing a novel, it could be good to talk about what genre or literary tradition the novel is written within (e.g., fantasy, High Modernism, romance). You may also want to include details about the author’s biography that seem particularly relevant to your critique.
• For a media item, such as a news article, consider the social and/or political context of the media outlet the item came from (e.g., Fox News, BBC, etc.) and of the issue it is dealing with (e.g., immigration, education, entertainment).

• The authors of research articles will often state very clearly in the abstract and in the introduction to their work what they are investigating, often with sentences that say something like this: "In this article we provide a new framework for analyzing X and argue that it is superior to previous methods because of reason A and reason B."
• For creative works, you may not have an explicit statement from the author or creator about their purpose, but you can often infer one from the context the work occupies. For example, if you were examining the movie The Shining, you might argue that the filmmaker Stanley Kubrick's goal is to call attention to the poor treatment of Native Americans because of the strong Native American themes present in the movie. You could then present the reasons why you think that in the rest of the essay.

• For example, if you were writing about The Shining, you could summarize the main points this way: "Stanley Kubrick uses strong symbolism, such as the placement of the movie's hotel on an Indian burial ground, the naming of the hotel "Overlook," and the constant presence of Native American artwork and representation, to call viewers' attention to America's treatment of Native Americans in history."

• For a research article, you will probably want to focus your thesis on whether the research and discussion supported the authors' claims. You may also wish to critique the research methodology, if there are obvious flaws present.
• For creative works, consider what you believe the author or creator's goal was in making the work, and then present your assessment of whether or not they achieved that goal.

Writing the 3 Body Paragraphs

• If you have three clear points about your work, you can organize each paragraph by point. For example, if you are analyzing a painting, you might critique the painter’s use of color, light, and composition, devoting a paragraph to each topic.
• If you have more than three points about your work, you can organize each paragraph thematically. For example, if you are critiquing a movie and want to talk about its treatment of women, its screenwriting, its pacing, its use of color and framing, and its acting, you might think about the broader categories that these points fall into, such as “production” (pacing, color and framing, screenwriting), “social commentary” (treatment of women), and “performance” (acting).
• Alternatively, you could organize your critique by “strengths” and “weaknesses.” The aim of a critique is not merely to criticize, but to point out what the creator or author has done well and what s/he has not.

• For example, if you are critiquing a song, you could consider how the beat or tone of the music supports or detracts from the lyrics.
• For a research article or a media item, you may want to consider questions such as how the data was gathered in an experiment, or what method a journalist used to discover information.

• Does the author use primary sources (e.g., historical documents, interviews, etc.)? Secondary sources? Quantitative data? Qualitative data? Are these sources appropriate for the argument?
• Has evidence been presented fairly, without distortion or selectivity?
• Does the argument proceed logically from the evidence used?

• If the work is a creative work, consider whether it presents its ideas in an original or interesting way. You can also consider whether it engages with key concepts or ideas in popular culture or society.
• If the work is a research article, you can consider whether the work enhances your understanding of a particular theory or idea in its discipline. Research articles often include a section on “further research” where they discuss the contributions their research has made and what future contributions they hope to make.

Writing the Conclusion Paragraph and References

Sample Critiques

Community Q&A

• Before you begin writing, take notes while you are watching or reading the subject of your critique. Keep to mind certain aspects such as how it made you feel. What was your first impression? With deeper examination, what is your overall opinion? How did you come to this opinion? Thanks Helpful 0 Not Helpful 0
• While the 5-paragraph form can work very well to help you organize your ideas, some instructors do not allow this type of essay. Be sure that you understand the assignment. If you’re not sure whether a 5-paragraph format is acceptable to your teacher, ask! Thanks Helpful 0 Not Helpful 0

• Avoid using first and second person pronouns such as, “you”, “your”, “I”, “my”, or “mine.” State your opinion objectively for a more credible approach. Thanks Helpful 39 Not Helpful 14

You Might Also Like

• ↑ https://www.indeed.com/career-advice/career-development/how-to-write-a-critique
• ↑ https://writingcenter.uagc.edu/writing-article-critique
• ↑ https://www.citewrite.qut.edu.au/write/writing-well/critique.html
• ↑ http://www.writing.utoronto.ca/advice/specific-types-of-writing/book-review
• ↑ https://www.hunter.cuny.edu/rwc/handouts/the-writing-process-1/invention/Writing-a-Critique
• ↑ https://writingcenter.unc.edu/esl/resources/writing-critiques/

About This Article

To write a 5-paragraph critique, provide the basic information about the work you're critiquing in the first paragraph, including the author, when it was published, and what its key themes are. Then, conclude this paragraph with a statement of your opinion of the work. Next, identify 3 central positive or negative issues in the work and write a paragraph about each one. For example, you could focus on the color, light, and composition of a painting. In the final paragraph, state your overall assessment of the work, and give reasons to back it up. For tips on how to take notes on the piece your critiquing, read on! Did this summary help you? Yes No

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Home — Essay Samples — Life — Basketball — My First Encounter with Basketball: A Personal Narrative

My First Encounter with Basketball: a Personal Narrative

• Categories: Basketball Competitive Sports

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Published: Jun 6, 2024

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Introduction, body paragraph 1, body paragraph 2, body paragraph 3.

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• Published: 03 June 2024

Applying large language models for automated essay scoring for non-native Japanese

• Wenchao Li 1 &
• Haitao Liu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  723 ( 2024 ) Cite this article

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• Language and linguistics

Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

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Conventional machine learning technology in aes.

AES has experienced significant growth with the advancement of machine learning technologies in recent decades. In the earlier stages of AES development, conventional machine learning-based approaches were commonly used. These approaches involved the following procedures: a) feeding the machine with a dataset. In this step, a dataset of essays is provided to the machine learning system. The dataset serves as the basis for training the model and establishing patterns and correlations between linguistic features and human ratings. b) the machine learning model is trained using linguistic features that best represent human ratings and can effectively discriminate learners’ writing proficiency. These features include lexical richness (Lu, 2012 ; Kyle and Crossley, 2015 ; Kyle et al. 2021 ), syntactic complexity (Lu, 2010 ; Liu, 2008 ), text cohesion (Crossley and McNamara, 2016 ), and among others. Conventional machine learning approaches in AES require human intervention, such as manual correction and annotation of essays. This human involvement was necessary to create a labeled dataset for training the model. Several AES systems have been developed using conventional machine learning technologies. These include the Intelligent Essay Assessor (Landauer et al. 2003 ), the e-rater engine by Educational Testing Service (Attali and Burstein, 2006 ; Burstein, 2003 ), MyAccess with the InterlliMetric scoring engine by Vantage Learning (Elliot, 2003 ), and the Bayesian Essay Test Scoring system (Rudner and Liang, 2002 ). These systems have played a significant role in automating the essay scoring process and providing quick and consistent feedback to learners. However, as touched upon earlier, conventional machine learning approaches rely on predetermined linguistic features and often require manual intervention, making them less flexible and potentially limiting their generalizability to different contexts.

In the context of the Japanese language, conventional machine learning-incorporated AES tools include Jess (Ishioka and Kameda, 2006 ) and JWriter (Lee and Hasebe, 2017 ). Jess assesses essays by deducting points from the perfect score, utilizing the Mainichi Daily News newspaper as a database. The evaluation criteria employed by Jess encompass various aspects, such as rhetorical elements (e.g., reading comprehension, vocabulary diversity, percentage of complex words, and percentage of passive sentences), organizational structures (e.g., forward and reverse connection structures), and content analysis (e.g., latent semantic indexing). JWriter employs linear regression analysis to assign weights to various measurement indices, such as average sentence length and total number of characters. These weights are then combined to derive the overall score. A pilot study involving the Jess model was conducted on 1320 essays at different proficiency levels, including primary, intermediate, and advanced. However, the results indicated that the Jess model failed to significantly distinguish between these essay levels. Out of the 16 measures used, four measures, namely median sentence length, median clause length, median number of phrases, and maximum number of phrases, did not show statistically significant differences between the levels. Additionally, two measures exhibited between-level differences but lacked linear progression: the number of attributives declined words and the Kanji/kana ratio. On the other hand, the remaining measures, including maximum sentence length, maximum clause length, number of attributive conjugated words, maximum number of consecutive infinitive forms, maximum number of conjunctive-particle clauses, k characteristic value, percentage of big words, and percentage of passive sentences, demonstrated statistically significant between-level differences and displayed linear progression.

Both Jess and JWriter exhibit notable limitations, including the manual selection of feature parameters and weights, which can introduce biases into the scoring process. The reliance on human annotators to label non-native language essays also introduces potential noise and variability in the scoring. Furthermore, an important concern is the possibility of system manipulation and cheating by learners who are aware of the regression equation utilized by the models (Hirao et al. 2020 ). These limitations emphasize the need for further advancements in AES systems to address these challenges.

Deep learning technology in AES

Deep learning has emerged as one of the approaches for improving the accuracy and effectiveness of AES. Deep learning-based AES methods utilize artificial neural networks that mimic the human brain’s functioning through layered algorithms and computational units. Unlike conventional machine learning, deep learning autonomously learns from the environment and past errors without human intervention. This enables deep learning models to establish nonlinear correlations, resulting in higher accuracy. Recent advancements in deep learning have led to the development of transformers, which are particularly effective in learning text representations. Noteworthy examples include bidirectional encoder representations from transformers (BERT) (Devlin et al. 2019 ) and the generative pretrained transformer (GPT) (OpenAI).

BERT is a linguistic representation model that utilizes a transformer architecture and is trained on two tasks: masked linguistic modeling and next-sentence prediction (Hirao et al. 2020 ; Vaswani et al. 2017 ). In the context of AES, BERT follows specific procedures, as illustrated in Fig. 1 : (a) the tokenized prompts and essays are taken as input; (b) special tokens, such as [CLS] and [SEP], are added to mark the beginning and separation of prompts and essays; (c) the transformer encoder processes the prompt and essay sequences, resulting in hidden layer sequences; (d) the hidden layers corresponding to the [CLS] tokens (T[CLS]) represent distributed representations of the prompts and essays; and (e) a multilayer perceptron uses these distributed representations as input to obtain the final score (Hirao et al. 2020 ).

AES system with BERT (Hirao et al. 2020 ).

The training of BERT using a substantial amount of sentence data through the Masked Language Model (MLM) allows it to capture contextual information within the hidden layers. Consequently, BERT is expected to be capable of identifying artificial essays as invalid and assigning them lower scores (Mizumoto and Eguchi, 2023 ). In the context of AES for nonnative Japanese learners, Hirao et al. ( 2020 ) combined the long short-term memory (LSTM) model proposed by Hochreiter and Schmidhuber ( 1997 ) with BERT to develop a tailored automated Essay Scoring System. The findings of their study revealed that the BERT model outperformed both the conventional machine learning approach utilizing character-type features such as “kanji” and “hiragana”, as well as the standalone LSTM model. Takeuchi et al. ( 2021 ) presented an approach to Japanese AES that eliminates the requirement for pre-scored essays by relying solely on reference texts or a model answer for the essay task. They investigated multiple similarity evaluation methods, including frequency of morphemes, idf values calculated on Wikipedia, LSI, LDA, word-embedding vectors, and document vectors produced by BERT. The experimental findings revealed that the method utilizing the frequency of morphemes with idf values exhibited the strongest correlation with human-annotated scores across different essay tasks. The utilization of BERT in AES encounters several limitations. Firstly, essays often exceed the model’s maximum length limit. Second, only score labels are available for training, which restricts access to additional information.

Mizumoto and Eguchi ( 2023 ) were pioneers in employing the GPT model for AES in non-native English writing. Their study focused on evaluating the accuracy and reliability of AES using the GPT-3 text-davinci-003 model, analyzing a dataset of 12,100 essays from the corpus of nonnative written English (TOEFL11). The findings indicated that AES utilizing the GPT-3 model exhibited a certain degree of accuracy and reliability. They suggest that GPT-3-based AES systems hold the potential to provide support for human ratings. However, applying GPT model to AES presents a unique natural language processing (NLP) task that involves considerations such as nonnative language proficiency, the influence of the learner’s first language on the output in the target language, and identifying linguistic features that best indicate writing quality in a specific language. These linguistic features may differ morphologically or syntactically from those present in the learners’ first language, as observed in (1)–(3).

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

1 sg .-give. past- him-one .cl- book

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

Kare-ni-hon-o-age-mashi-ta

3 sg .- dat -hon- acc- give.honorification. past

Inflectional

give, give-s, gave, given, giving

Additionally, the morphological agglutination and subject-object-verb (SOV) order in Japanese, along with its idiomatic expressions, pose additional challenges for applying language models in AES tasks (4).

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

“My leg became like a stick (I am extremely tired).”

The example sentence provided demonstrates the morpho-syntactic structure of Japanese and the presence of an idiomatic expression. In this sentence, the verb “なる” (naru), meaning “to become”, appears at the end of the sentence. The verb stem “なり” (nari) is attached with morphemes indicating honorification (“ます” - mashu) and tense (“た” - ta), showcasing agglutination. While the sentence can be literally translated as “my leg became like a stick”, it carries an idiomatic interpretation that implies “I am extremely tired”.

To overcome this issue, CyberAgent Inc. ( 2023 ) has developed the Open-Calm series of language models specifically designed for Japanese. Open-Calm consists of pre-trained models available in various sizes, such as Small, Medium, Large, and 7b. Figure 2 depicts the fundamental structure of the Open-Calm model. A key feature of this architecture is the incorporation of the Lora Adapter and GPT-NeoX frameworks, which can enhance its language processing capabilities.

GPT-NeoX Model Architecture (Okgetheng and Takeuchi 2024 ).

In a recent study conducted by Okgetheng and Takeuchi ( 2024 ), they assessed the efficacy of Open-Calm language models in grading Japanese essays. The research utilized a dataset of approximately 300 essays, which were annotated by native Japanese educators. The findings of the study demonstrate the considerable potential of Open-Calm language models in automated Japanese essay scoring. Specifically, among the Open-Calm family, the Open-Calm Large model (referred to as OCLL) exhibited the highest performance. However, it is important to note that, as of the current date, the Open-Calm Large model does not offer public access to its server. Consequently, users are required to independently deploy and operate the environment for OCLL. In order to utilize OCLL, users must have a PC equipped with an NVIDIA GeForce RTX 3060 (8 or 12 GB VRAM).

In summary, while the potential of LLMs in automated scoring of nonnative Japanese essays has been demonstrated in two studies—BERT-driven AES (Hirao et al. 2020 ) and OCLL-based AES (Okgetheng and Takeuchi, 2024 )—the number of research efforts in this area remains limited.

Another significant challenge in applying LLMs to AES lies in prompt engineering and ensuring its reliability and effectiveness (Brown et al. 2020 ; Rae et al. 2021 ; Zhang et al. 2021 ). Various prompting strategies have been proposed, such as the zero-shot chain of thought (CoT) approach (Kojima et al. 2022 ), which involves manually crafting diverse and effective examples. However, manual efforts can lead to mistakes. To address this, Zhang et al. ( 2021 ) introduced an automatic CoT prompting method called Auto-CoT, which demonstrates matching or superior performance compared to the CoT paradigm. Another prompt framework is trees of thoughts, enabling a model to self-evaluate its progress at intermediate stages of problem-solving through deliberate reasoning (Yao et al. 2023 ).

Beyond linguistic studies, there has been a noticeable increase in the number of foreign workers in Japan and Japanese learners worldwide (Ministry of Health, Labor, and Welfare of Japan, 2022 ; Japan Foundation, 2021 ). However, existing assessment methods, such as the Japanese Language Proficiency Test (JLPT), J-CAT, and TTBJ Footnote 1 , primarily focus on reading, listening, vocabulary, and grammar skills, neglecting the evaluation of writing proficiency. As the number of workers and language learners continues to grow, there is a rising demand for an efficient AES system that can reduce costs and time for raters and be utilized for employment, examinations, and self-study purposes.

This study aims to explore the potential of LLM-based AES by comparing the effectiveness of five models: two LLMs (GPT Footnote 2 and BERT), one Japanese local LLM (OCLL), and two conventional machine learning-based methods (linguistic feature-based scoring tools - Jess and JWriter).

The research questions addressed in this study are as follows:

To what extent do the LLM-driven AES and linguistic feature-based AES, when used as automated tools to support human rating, accurately reflect test takers’ actual performance?

What influence does the prompt have on the accuracy and performance of LLM-based AES methods?

The subsequent sections of the manuscript cover the methodology, including the assessment measures for nonnative Japanese writing proficiency, criteria for prompts, and the dataset. The evaluation section focuses on the analysis of annotations and rating scores generated by LLM-driven and linguistic feature-based AES methods.

Methodology

The dataset utilized in this study was obtained from the International Corpus of Japanese as a Second Language (I-JAS) Footnote 3 . This corpus consisted of 1000 participants who represented 12 different first languages. For the study, the participants were given a story-writing task on a personal computer. They were required to write two stories based on the 4-panel illustrations titled “Picnic” and “The key” (see Appendix A). Background information for the participants was provided by the corpus, including their Japanese language proficiency levels assessed through two online tests: J-CAT and SPOT. These tests evaluated their reading, listening, vocabulary, and grammar abilities. The learners’ proficiency levels were categorized into six levels aligned with the Common European Framework of Reference for Languages (CEFR) and the Reference Framework for Japanese Language Education (RFJLE): A1, A2, B1, B2, C1, and C2. According to Lee et al. ( 2015 ), there is a high level of agreement (r = 0.86) between the J-CAT and SPOT assessments, indicating that the proficiency certifications provided by J-CAT are consistent with those of SPOT. However, it is important to note that the scores of J-CAT and SPOT do not have a one-to-one correspondence. In this study, the J-CAT scores were used as a benchmark to differentiate learners of different proficiency levels. A total of 1400 essays were utilized, representing the beginner (aligned with A1), A2, B1, B2, C1, and C2 levels based on the J-CAT scores. Table 1 provides information about the learners’ proficiency levels and their corresponding J-CAT and SPOT scores.

A dataset comprising a total of 1400 essays from the story writing tasks was collected. Among these, 714 essays were utilized to evaluate the reliability of the LLM-based AES method, while the remaining 686 essays were designated as development data to assess the LLM-based AES’s capability to distinguish participants with varying proficiency levels. The GPT 4 API was used in this study. A detailed explanation of the prompt-assessment criteria is provided in Section Prompt . All essays were sent to the model for measurement and scoring.

Measures of writing proficiency for nonnative Japanese

Japanese exhibits a morphologically agglutinative structure where morphemes are attached to the word stem to convey grammatical functions such as tense, aspect, voice, and honorifics, e.g. (5).

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

[eat (stem)-causative-passive voice-honorification-tense. past-question marker]

Japanese employs nine case particles to indicate grammatical functions: the nominative case particle が (ga), the accusative case particle を (o), the genitive case particle の (no), the dative case particle に (ni), the locative/instrumental case particle で (de), the ablative case particle から (kara), the directional case particle へ (e), and the comitative case particle と (to). The agglutinative nature of the language, combined with the case particle system, provides an efficient means of distinguishing between active and passive voice, either through morphemes or case particles, e.g. 食べる taberu “eat concusive . ” (active voice); 食べられる taberareru “eat concusive . ” (passive voice). In the active voice, “パン を 食べる” (pan o taberu) translates to “to eat bread”. On the other hand, in the passive voice, it becomes “パン が 食べられた” (pan ga taberareta), which means “(the) bread was eaten”. Additionally, it is important to note that different conjugations of the same lemma are considered as one type in order to ensure a comprehensive assessment of the language features. For example, e.g., 食べる taberu “eat concusive . ”; 食べている tabeteiru “eat progress .”; 食べた tabeta “eat past . ” as one type.

To incorporate these features, previous research (Suzuki, 1999 ; Watanabe et al. 1988 ; Ishioka, 2001 ; Ishioka and Kameda, 2006 ; Hirao et al. 2020 ) has identified complexity, fluency, and accuracy as crucial factors for evaluating writing quality. These criteria are assessed through various aspects, including lexical richness (lexical density, diversity, and sophistication), syntactic complexity, and cohesion (Kyle et al. 2021 ; Mizumoto and Eguchi, 2023 ; Ure, 1971 ; Halliday, 1985 ; Barkaoui and Hadidi, 2020 ; Zenker and Kyle, 2021 ; Kim et al. 2018 ; Lu, 2017 ; Ortega, 2015 ). Therefore, this study proposes five scoring categories: lexical richness, syntactic complexity, cohesion, content elaboration, and grammatical accuracy. A total of 16 measures were employed to capture these categories. The calculation process and specific details of these measures can be found in Table 2 .

T-unit, first introduced by Hunt ( 1966 ), is a measure used for evaluating speech and composition. It serves as an indicator of syntactic development and represents the shortest units into which a piece of discourse can be divided without leaving any sentence fragments. In the context of Japanese language assessment, Sakoda and Hosoi ( 2020 ) utilized T-unit as the basic unit to assess the accuracy and complexity of Japanese learners’ speaking and storytelling. The calculation of T-units in Japanese follows the following principles:

A single main clause constitutes 1 T-unit, regardless of the presence or absence of dependent clauses, e.g. (6).

ケンとマリはピクニックに行きました (main clause): 1 T-unit.

If a sentence contains a main clause along with subclauses, each subclause is considered part of the same T-unit, e.g. (7).

天気が良かった の で (subclause)、ケンとマリはピクニックに行きました (main clause): 1 T-unit.

In the case of coordinate clauses, where multiple clauses are connected, each coordinated clause is counted separately. Thus, a sentence with coordinate clauses may have 2 T-units or more, e.g. (8).

ケンは地図で場所を探して (coordinate clause)、マリはサンドイッチを作りました (coordinate clause): 2 T-units.

Lexical diversity refers to the range of words used within a text (Engber, 1995 ; Kyle et al. 2021 ) and is considered a useful measure of the breadth of vocabulary in L n production (Jarvis, 2013a , 2013b ).

The type/token ratio (TTR) is widely recognized as a straightforward measure for calculating lexical diversity and has been employed in numerous studies. These studies have demonstrated a strong correlation between TTR and other methods of measuring lexical diversity (e.g., Bentz et al. 2016 ; Čech and Miroslav, 2018 ; Çöltekin and Taraka, 2018 ). TTR is computed by considering both the number of unique words (types) and the total number of words (tokens) in a given text. Given that the length of learners’ writing texts can vary, this study employs the moving average type-token ratio (MATTR) to mitigate the influence of text length. MATTR is calculated using a 50-word moving window. Initially, a TTR is determined for words 1–50 in an essay, followed by words 2–51, 3–52, and so on until the end of the essay is reached (Díez-Ortega and Kyle, 2023 ). The final MATTR scores were obtained by averaging the TTR scores for all 50-word windows. The following formula was employed to derive MATTR:

\({\rm{MATTR}}({\rm{W}})=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{N}}-{\rm{W}}+1}{{\rm{F}}}_{{\rm{i}}}}{{\rm{W}}({\rm{N}}-{\rm{W}}+1)}\)

Here, N refers to the number of tokens in the corpus. W is the randomly selected token size (W < N). \({F}_{i}\) is the number of types in each window. The \({\rm{MATTR}}({\rm{W}})\) is the mean of a series of type-token ratios (TTRs) based on the word form for all windows. It is expected that individuals with higher language proficiency will produce texts with greater lexical diversity, as indicated by higher MATTR scores.

Lexical density was captured by the ratio of the number of lexical words to the total number of words (Lu, 2012 ). Lexical sophistication refers to the utilization of advanced vocabulary, often evaluated through word frequency indices (Crossley et al. 2013 ; Haberman, 2008 ; Kyle and Crossley, 2015 ; Laufer and Nation, 1995 ; Lu, 2012 ; Read, 2000 ). In line of writing, lexical sophistication can be interpreted as vocabulary breadth, which entails the appropriate usage of vocabulary items across various lexicon-grammatical contexts and registers (Garner et al. 2019 ; Kim et al. 2018 ; Kyle et al. 2018 ). In Japanese specifically, words are considered lexically sophisticated if they are not included in the “Japanese Education Vocabulary List Ver 1.0”. Footnote 4 Consequently, lexical sophistication was calculated by determining the number of sophisticated word types relative to the total number of words per essay. Furthermore, it has been suggested that, in Japanese writing, sentences should ideally have a length of no more than 40 to 50 characters, as this promotes readability. Therefore, the median and maximum sentence length can be considered as useful indices for assessment (Ishioka and Kameda, 2006 ).

Syntactic complexity was assessed based on several measures, including the mean length of clauses, verb phrases per T-unit, clauses per T-unit, dependent clauses per T-unit, complex nominals per clause, adverbial clauses per clause, coordinate phrases per clause, and mean dependency distance (MDD). The MDD reflects the distance between the governor and dependent positions in a sentence. A larger dependency distance indicates a higher cognitive load and greater complexity in syntactic processing (Liu, 2008 ; Liu et al. 2017 ). The MDD has been established as an efficient metric for measuring syntactic complexity (Jiang, Quyang, and Liu, 2019 ; Li and Yan, 2021 ). To calculate the MDD, the position numbers of the governor and dependent are subtracted, assuming that words in a sentence are assigned in a linear order, such as W1 … Wi … Wn. In any dependency relationship between words Wa and Wb, Wa is the governor and Wb is the dependent. The MDD of the entire sentence was obtained by taking the absolute value of governor – dependent:

MDD = \(\frac{1}{n}{\sum }_{i=1}^{n}|{\rm{D}}{{\rm{D}}}_{i}|\)

In this formula, \(n\) represents the number of words in the sentence, and \({DD}i\) is the dependency distance of the \({i}^{{th}}\) dependency relationship of a sentence. Building on this, the annotation of sentence ‘Mary-ga-John-ni-keshigomu-o-watashita was [Mary- top -John- dat -eraser- acc -give- past] ’. The sentence’s MDD would be 2. Table 3 provides the CSV file as a prompt for GPT 4.

Cohesion (semantic similarity) and content elaboration aim to capture the ideas presented in test taker’s essays. Cohesion was assessed using three measures: Synonym overlap/paragraph (topic), Synonym overlap/paragraph (keywords), and word2vec cosine similarity. Content elaboration and development were measured as the number of metadiscourse markers (type)/number of words. To capture content closely, this study proposed a novel-distance based representation, by encoding the cosine distance between the essay (by learner) and essay task’s (topic and keyword) i -vectors. The learner’s essay is decoded into a word sequence, and aligned to the essay task’ topic and keyword for log-likelihood measurement. The cosine distance reveals the content elaboration score in the leaners’ essay. The mathematical equation of cosine similarity between target-reference vectors is shown in (11), assuming there are i essays and ( L i , …. L n ) and ( N i , …. N n ) are the vectors representing the learner and task’s topic and keyword respectively. The content elaboration distance between L i and N i was calculated as follows:

\(\cos \left(\theta \right)=\frac{{\rm{L}}\,\cdot\, {\rm{N}}}{\left|{\rm{L}}\right|{\rm{|N|}}}=\frac{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}{N}_{i}}{\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}^{2}}\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{N}_{i}^{2}}}\)

A high similarity value indicates a low difference between the two recognition outcomes, which in turn suggests a high level of proficiency in content elaboration.

To evaluate the effectiveness of the proposed measures in distinguishing different proficiency levels among nonnative Japanese speakers’ writing, we conducted a multi-faceted Rasch measurement analysis (Linacre, 1994 ). This approach applies measurement models to thoroughly analyze various factors that can influence test outcomes, including test takers’ proficiency, item difficulty, and rater severity, among others. The underlying principles and functionality of multi-faceted Rasch measurement are illustrated in (12).

\(\log \left(\frac{{P}_{{nijk}}}{{P}_{{nij}(k-1)}}\right)={B}_{n}-{D}_{i}-{C}_{j}-{F}_{k}\)

(12) defines the logarithmic transformation of the probability ratio ( P nijk /P nij(k-1) )) as a function of multiple parameters. Here, n represents the test taker, i denotes a writing proficiency measure, j corresponds to the human rater, and k represents the proficiency score. The parameter B n signifies the proficiency level of test taker n (where n ranges from 1 to N). D j represents the difficulty parameter of test item i (where i ranges from 1 to L), while C j represents the severity of rater j (where j ranges from 1 to J). Additionally, F k represents the step difficulty for a test taker to move from score ‘k-1’ to k . P nijk refers to the probability of rater j assigning score k to test taker n for test item i . P nij(k-1) represents the likelihood of test taker n being assigned score ‘k-1’ by rater j for test item i . Each facet within the test is treated as an independent parameter and estimated within the same reference framework. To evaluate the consistency of scores obtained through both human and computer analysis, we utilized the Infit mean-square statistic. This statistic is a chi-square measure divided by the degrees of freedom and is weighted with information. It demonstrates higher sensitivity to unexpected patterns in responses to items near a person’s proficiency level (Linacre, 2002 ). Fit statistics are assessed based on predefined thresholds for acceptable fit. For the Infit MNSQ, which has a mean of 1.00, different thresholds have been suggested. Some propose stricter thresholds ranging from 0.7 to 1.3 (Bond et al. 2021 ), while others suggest more lenient thresholds ranging from 0.5 to 1.5 (Eckes, 2009 ). In this study, we adopted the criterion of 0.70–1.30 for the Infit MNSQ.

Moving forward, we can now proceed to assess the effectiveness of the 16 proposed measures based on five criteria for accurately distinguishing various levels of writing proficiency among non-native Japanese speakers. To conduct this evaluation, we utilized the development dataset from the I-JAS corpus, as described in Section Dataset . Table 4 provides a measurement report that presents the performance details of the 14 metrics under consideration. The measure separation was found to be 4.02, indicating a clear differentiation among the measures. The reliability index for the measure separation was 0.891, suggesting consistency in the measurement. Similarly, the person separation reliability index was 0.802, indicating the accuracy of the assessment in distinguishing between individuals. All 16 measures demonstrated Infit mean squares within a reasonable range, ranging from 0.76 to 1.28. The Synonym overlap/paragraph (topic) measure exhibited a relatively high outfit mean square of 1.46, although the Infit mean square falls within an acceptable range. The standard error for the measures ranged from 0.13 to 0.28, indicating the precision of the estimates.

Table 5 further illustrated the weights assigned to different linguistic measures for score prediction, with higher weights indicating stronger correlations between those measures and higher scores. Specifically, the following measures exhibited higher weights compared to others: moving average type token ratio per essay has a weight of 0.0391. Mean dependency distance had a weight of 0.0388. Mean length of clause, calculated by dividing the number of words by the number of clauses, had a weight of 0.0374. Complex nominals per T-unit, calculated by dividing the number of complex nominals by the number of T-units, had a weight of 0.0379. Coordinate phrases rate, calculated by dividing the number of coordinate phrases by the number of clauses, had a weight of 0.0325. Grammatical error rate, representing the number of errors per essay, had a weight of 0.0322.

Criteria (output indicator)

The criteria used to evaluate the writing ability in this study were based on CEFR, which follows a six-point scale ranging from A1 to C2. To assess the quality of Japanese writing, the scoring criteria from Table 6 were utilized. These criteria were derived from the IELTS writing standards and served as assessment guidelines and prompts for the written output.

A prompt is a question or detailed instruction that is provided to the model to obtain a proper response. After several pilot experiments, we decided to provide the measures (Section Measures of writing proficiency for nonnative Japanese ) as the input prompt and use the criteria (Section Criteria (output indicator) ) as the output indicator. Regarding the prompt language, considering that the LLM was tasked with rating Japanese essays, would prompt in Japanese works better Footnote 5 ? We conducted experiments comparing the performance of GPT-4 using both English and Japanese prompts. Additionally, we utilized the Japanese local model OCLL with Japanese prompts. Multiple trials were conducted using the same sample. Regardless of the prompt language used, we consistently obtained the same grading results with GPT-4, which assigned a grade of B1 to the writing sample. This suggested that GPT-4 is reliable and capable of producing consistent ratings regardless of the prompt language. On the other hand, when we used Japanese prompts with the Japanese local model “OCLL”, we encountered inconsistent grading results. Out of 10 attempts with OCLL, only 6 yielded consistent grading results (B1), while the remaining 4 showed different outcomes, including A1 and B2 grades. These findings indicated that the language of the prompt was not the determining factor for reliable AES. Instead, the size of the training data and the model parameters played crucial roles in achieving consistent and reliable AES results for the language model.

The following is the utilized prompt, which details all measures and requires the LLM to score the essays using holistic and trait scores.

Please evaluate Japanese essays written by Japanese learners and assign a score to each essay on a six-point scale, ranging from A1, A2, B1, B2, C1 to C2. Additionally, please provide trait scores and display the calculation process for each trait score. The scoring should be based on the following criteria:

Moving average type-token ratio.

Number of lexical words (token) divided by the total number of words per essay.

Number of sophisticated word types divided by the total number of words per essay.

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

Number of metadiscourse markers (types) divided by the total number of words.

Number of errors per essay.

Japanese essay text

出かける前に二人が地図を見ている間に、サンドイッチを入れたバスケットに犬が入ってしまいました。それに気づかずに二人は楽しそうに出かけて行きました。やがて突然犬がバスケットから飛び出し、二人は驚きました。バスケット の 中を見ると、食べ物はすべて犬に食べられていて、二人は困ってしまいました。(ID_JJJ01_SW1)

The score of the example above was B1. Figure 3 provides an example of holistic and trait scores provided by GPT-4 (with a prompt indicating all measures) via Bing Footnote 6 .

Example of GPT-4 AES and feedback (with a prompt indicating all measures).

Statistical analysis

The aim of this study is to investigate the potential use of LLM for nonnative Japanese AES. It seeks to compare the scoring outcomes obtained from feature-based AES tools, which rely on conventional machine learning technology (i.e. Jess, JWriter), with those generated by AI-driven AES tools utilizing deep learning technology (BERT, GPT, OCLL). To assess the reliability of a computer-assisted annotation tool, the study initially established human-human agreement as the benchmark measure. Subsequently, the performance of the LLM-based method was evaluated by comparing it to human-human agreement.

To assess annotation agreement, the study employed standard measures such as precision, recall, and F-score (Brants 2000 ; Lu 2010 ), along with the quadratically weighted kappa (QWK) to evaluate the consistency and agreement in the annotation process. Assume A and B represent human annotators. When comparing the annotations of the two annotators, the following results are obtained. The evaluation of precision, recall, and F-score metrics was illustrated in equations (13) to (15).

\({\rm{Recall}}(A,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,A}\)

\({\rm{Precision}}(A,\,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,B}\)

The F-score is the harmonic mean of recall and precision:

\({\rm{F}}-{\rm{score}}=\frac{2* ({\rm{Precision}}* {\rm{Recall}})}{{\rm{Precision}}+{\rm{Recall}}}\)

The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.

In accordance with Taghipour and Ng ( 2016 ), the calculation of QWK involves two steps:

Step 1: Construct a weight matrix W as follows:

\({W}_{{ij}}=\frac{{(i-j)}^{2}}{{(N-1)}^{2}}\)

i represents the annotation made by the tool, while j represents the annotation made by a human rater. N denotes the total number of possible annotations. Matrix O is subsequently computed, where O_( i, j ) represents the count of data annotated by the tool ( i ) and the human annotator ( j ). On the other hand, E refers to the expected count matrix, which undergoes normalization to ensure that the sum of elements in E matches the sum of elements in O.

Step 2: With matrices O and E, the QWK is obtained as follows:

K = 1- \(\frac{\sum i,j{W}_{i,j}\,{O}_{i,j}}{\sum i,j{W}_{i,j}\,{E}_{i,j}}\)

The value of the quadratic weighted kappa increases as the level of agreement improves. Further, to assess the accuracy of LLM scoring, the proportional reductive mean square error (PRMSE) was employed. The PRMSE approach takes into account the variability observed in human ratings to estimate the rater error, which is then subtracted from the variance of the human labels. This calculation provides an overall measure of agreement between the automated scores and true scores (Haberman et al. 2015 ; Loukina et al. 2020 ; Taghipour and Ng, 2016 ). The computation of PRMSE involves the following steps:

Step 1: Calculate the mean squared errors (MSEs) for the scoring outcomes of the computer-assisted tool (MSE tool) and the human scoring outcomes (MSE human).

Step 2: Determine the PRMSE by comparing the MSE of the computer-assisted tool (MSE tool) with the MSE from human raters (MSE human), using the following formula:

\({\rm{PRMSE}}=1-\frac{({\rm{MSE}}\,{\rm{tool}})\,}{({\rm{MSE}}\,{\rm{human}})\,}=1-\,\frac{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-{\hat{{\rm{y}}}}_{{\rm{i}}})}^{2}}{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-\hat{{\rm{y}}})}^{2}}\)

In the numerator, ŷi represents the scoring outcome predicted by a specific LLM-driven AES system for a given sample. The term y i − ŷ i represents the difference between this predicted outcome and the mean value of all LLM-driven AES systems’ scoring outcomes. It quantifies the deviation of the specific LLM-driven AES system’s prediction from the average prediction of all LLM-driven AES systems. In the denominator, y i − ŷ represents the difference between the scoring outcome provided by a specific human rater for a given sample and the mean value of all human raters’ scoring outcomes. It measures the discrepancy between the specific human rater’s score and the average score given by all human raters. The PRMSE is then calculated by subtracting the ratio of the MSE tool to the MSE human from 1. PRMSE falls within the range of 0 to 1, with larger values indicating reduced errors in LLM’s scoring compared to those of human raters. In other words, a higher PRMSE implies that LLM’s scoring demonstrates greater accuracy in predicting the true scores (Loukina et al. 2020 ). The interpretation of kappa values, ranging from 0 to 1, is based on the work of Landis and Koch ( 1977 ). Specifically, the following categories are assigned to different ranges of kappa values: −1 indicates complete inconsistency, 0 indicates random agreement, 0.0 ~ 0.20 indicates extremely low level of agreement (slight), 0.21 ~ 0.40 indicates moderate level of agreement (fair), 0.41 ~ 0.60 indicates medium level of agreement (moderate), 0.61 ~ 0.80 indicates high level of agreement (substantial), 0.81 ~ 1 indicates almost perfect level of agreement. All statistical analyses were executed using Python script.

Results and discussion

Annotation reliability of the llm.

This section focuses on assessing the reliability of the LLM’s annotation and scoring capabilities. To evaluate the reliability, several tests were conducted simultaneously, aiming to achieve the following objectives:

Assess the LLM’s ability to differentiate between test takers with varying levels of oral proficiency.

Determine the level of agreement between the annotations and scoring performed by the LLM and those done by human raters.

The evaluation of the results encompassed several metrics, including: precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement.

Inter-annotator agreement (human–human annotator agreement)

We started with an agreement test of the two human annotators. Two trained annotators were recruited to determine the writing task data measures. A total of 714 scripts, as the test data, was utilized. Each analysis lasted 300–360 min. Inter-annotator agreement was evaluated using the standard measures of precision, recall, and F-score and QWK. Table 7 presents the inter-annotator agreement for the various indicators. As shown, the inter-annotator agreement was fairly high, with F-scores ranging from 1.0 for sentence and word number to 0.666 for grammatical errors.

The findings from the QWK analysis provided further confirmation of the inter-annotator agreement. The QWK values covered a range from 0.950 ( p  = 0.000) for sentence and word number to 0.695 for synonym overlap number (keyword) and grammatical errors ( p  = 0.001).

Agreement of annotation outcomes between human and LLM

To evaluate the consistency between human annotators and LLM annotators (BERT, GPT, OCLL) across the indices, the same test was conducted. The results of the inter-annotator agreement (F-score) between LLM and human annotation are provided in Appendix B-D. The F-scores ranged from 0.706 for Grammatical error # for OCLL-human to a perfect 1.000 for GPT-human, for sentences, clauses, T-units, and words. These findings were further supported by the QWK analysis, which showed agreement levels ranging from 0.807 ( p  = 0.001) for metadiscourse markers for OCLL-human to 0.962 for words ( p  = 0.000) for GPT-human. The findings demonstrated that the LLM annotation achieved a significant level of accuracy in identifying measurement units and counts.

Reliability of LLM-driven AES’s scoring and discriminating proficiency levels

This section examines the reliability of the LLM-driven AES scoring through a comparison of the scoring outcomes produced by human raters and the LLM ( Reliability of LLM-driven AES scoring ). It also assesses the effectiveness of the LLM-based AES system in differentiating participants with varying proficiency levels ( Reliability of LLM-driven AES discriminating proficiency levels ).

Reliability of LLM-driven AES scoring

Table 8 summarizes the QWK coefficient analysis between the scores computed by the human raters and the GPT-4 for the individual essays from I-JAS Footnote 7 . As shown, the QWK of all measures ranged from k  = 0.819 for lexical density (number of lexical words (tokens)/number of words per essay) to k  = 0.644 for word2vec cosine similarity. Table 9 further presents the Pearson correlations between the 16 writing proficiency measures scored by human raters and GPT 4 for the individual essays. The correlations ranged from 0.672 for syntactic complexity to 0.734 for grammatical accuracy. The correlations between the writing proficiency scores assigned by human raters and the BERT-based AES system were found to range from 0.661 for syntactic complexity to 0.713 for grammatical accuracy. The correlations between the writing proficiency scores given by human raters and the OCLL-based AES system ranged from 0.654 for cohesion to 0.721 for grammatical accuracy. These findings indicated an alignment between the assessments made by human raters and both the BERT-based and OCLL-based AES systems in terms of various aspects of writing proficiency.

Reliability of LLM-driven AES discriminating proficiency levels

After validating the reliability of the LLM’s annotation and scoring, the subsequent objective was to evaluate its ability to distinguish between various proficiency levels. For this analysis, a dataset of 686 individual essays was utilized. Table 10 presents a sample of the results, summarizing the means, standard deviations, and the outcomes of the one-way ANOVAs based on the measures assessed by the GPT-4 model. A post hoc multiple comparison test, specifically the Bonferroni test, was conducted to identify any potential differences between pairs of levels.

As the results reveal, seven measures presented linear upward or downward progress across the three proficiency levels. These were marked in bold in Table 10 and comprise one measure of lexical richness, i.e. MATTR (lexical diversity); four measures of syntactic complexity, i.e. MDD (mean dependency distance), MLC (mean length of clause), CNT (complex nominals per T-unit), CPC (coordinate phrases rate); one cohesion measure, i.e. word2vec cosine similarity and GER (grammatical error rate). Regarding the ability of the sixteen measures to distinguish adjacent proficiency levels, the Bonferroni tests indicated that statistically significant differences exist between the primary level and the intermediate level for MLC and GER. One measure of lexical richness, namely LD, along with three measures of syntactic complexity (VPT, CT, DCT, ACC), two measures of cohesion (SOPT, SOPK), and one measure of content elaboration (IMM), exhibited statistically significant differences between proficiency levels. However, these differences did not demonstrate a linear progression between adjacent proficiency levels. No significant difference was observed in lexical sophistication between proficiency levels.

To summarize, our study aimed to evaluate the reliability and differentiation capabilities of the LLM-driven AES method. For the first objective, we assessed the LLM’s ability to differentiate between test takers with varying levels of oral proficiency using precision, recall, F-Score, and quadratically-weighted kappa. Regarding the second objective, we compared the scoring outcomes generated by human raters and the LLM to determine the level of agreement. We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency measures for the individual essays. The results confirmed the feasibility of using the LLM for annotation and scoring in AES for nonnative Japanese. As a result, Research Question 1 has been addressed.

Comparison of BERT-, GPT-, OCLL-based AES, and linguistic-feature-based computation methods

This section aims to compare the effectiveness of five AES methods for nonnative Japanese writing, i.e. LLM-driven approaches utilizing BERT, GPT, and OCLL, linguistic feature-based approaches using Jess and JWriter. The comparison was conducted by comparing the ratings obtained from each approach with human ratings. All ratings were derived from the dataset introduced in Dataset . To facilitate the comparison, the agreement between the automated methods and human ratings was assessed using QWK and PRMSE. The performance of each approach was summarized in Table 11 .

The QWK coefficient values indicate that LLMs (GPT, BERT, OCLL) and human rating outcomes demonstrated higher agreement compared to feature-based AES methods (Jess and JWriter) in assessing writing proficiency criteria, including lexical richness, syntactic complexity, content, and grammatical accuracy. Among the LLMs, the GPT-4 driven AES and human rating outcomes showed the highest agreement in all criteria, except for syntactic complexity. The PRMSE values suggest that the GPT-based method outperformed linguistic feature-based methods and other LLM-based approaches. Moreover, an interesting finding emerged during the study: the agreement coefficient between GPT-4 and human scoring was even higher than the agreement between different human raters themselves. This discovery highlights the advantage of GPT-based AES over human rating. Ratings involve a series of processes, including reading the learners’ writing, evaluating the content and language, and assigning scores. Within this chain of processes, various biases can be introduced, stemming from factors such as rater biases, test design, and rating scales. These biases can impact the consistency and objectivity of human ratings. GPT-based AES may benefit from its ability to apply consistent and objective evaluation criteria. By prompting the GPT model with detailed writing scoring rubrics and linguistic features, potential biases in human ratings can be mitigated. The model follows a predefined set of guidelines and does not possess the same subjective biases that human raters may exhibit. This standardization in the evaluation process contributes to the higher agreement observed between GPT-4 and human scoring. Section Prompt strategy of the study delves further into the role of prompts in the application of LLMs to AES. It explores how the choice and implementation of prompts can impact the performance and reliability of LLM-based AES methods. Furthermore, it is important to acknowledge the strengths of the local model, i.e. the Japanese local model OCLL, which excels in processing certain idiomatic expressions. Nevertheless, our analysis indicated that GPT-4 surpasses local models in AES. This superior performance can be attributed to the larger parameter size of GPT-4, estimated to be between 500 billion and 1 trillion, which exceeds the sizes of both BERT and the local model OCLL.

Prompt strategy

In the context of prompt strategy, Mizumoto and Eguchi ( 2023 ) conducted a study where they applied the GPT-3 model to automatically score English essays in the TOEFL test. They found that the accuracy of the GPT model alone was moderate to fair. However, when they incorporated linguistic measures such as cohesion, syntactic complexity, and lexical features alongside the GPT model, the accuracy significantly improved. This highlights the importance of prompt engineering and providing the model with specific instructions to enhance its performance. In this study, a similar approach was taken to optimize the performance of LLMs. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. Model 1 was used as the baseline, representing GPT-4 without any additional prompting. Model 2, on the other hand, involved GPT-4 prompted with 16 measures that included scoring criteria, efficient linguistic features for writing assessment, and detailed measurement units and calculation formulas. The remaining models (Models 3 to 18) utilized GPT-4 prompted with individual measures. The performance of these 18 different models was assessed using the output indicators described in Section Criteria (output indicator) . By comparing the performances of these models, the study aimed to understand the impact of prompt engineering on the accuracy and effectiveness of GPT-4 in AES tasks.

Based on the PRMSE scores presented in Fig. 4 , it was observed that Model 1, representing GPT-4 without any additional prompting, achieved a fair level of performance. However, Model 2, which utilized GPT-4 prompted with all measures, outperformed all other models in terms of PRMSE score, achieving a score of 0.681. These results indicate that the inclusion of specific measures and prompts significantly enhanced the performance of GPT-4 in AES. Among the measures, syntactic complexity was found to play a particularly significant role in improving the accuracy of GPT-4 in assessing writing quality. Following that, lexical diversity emerged as another important factor contributing to the model’s effectiveness. The study suggests that a well-prompted GPT-4 can serve as a valuable tool to support human assessors in evaluating writing quality. By utilizing GPT-4 as an automated scoring tool, the evaluation biases associated with human raters can be minimized. This has the potential to empower teachers by allowing them to focus on designing writing tasks and guiding writing strategies, while leveraging the capabilities of GPT-4 for efficient and reliable scoring.

PRMSE scores of the 18 AES models.

This study aimed to investigate two main research questions: the feasibility of utilizing LLMs for AES and the impact of prompt engineering on the application of LLMs in AES.

To address the first objective, the study compared the effectiveness of five different models: GPT, BERT, the Japanese local LLM (OCLL), and two conventional machine learning-based AES tools (Jess and JWriter). The PRMSE values indicated that the GPT-4-based method outperformed other LLMs (BERT, OCLL) and linguistic feature-based computational methods (Jess and JWriter) across various writing proficiency criteria. Furthermore, the agreement coefficient between GPT-4 and human scoring surpassed the agreement among human raters themselves, highlighting the potential of using the GPT-4 tool to enhance AES by reducing biases and subjectivity, saving time, labor, and cost, and providing valuable feedback for self-study. Regarding the second goal, the role of prompt design was investigated by comparing 18 models, including a baseline model, a model prompted with all measures, and 16 models prompted with one measure at a time. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. The PRMSE scores of the models showed that GPT-4 prompted with all measures achieved the best performance, surpassing the baseline and other models.

In conclusion, this study has demonstrated the potential of LLMs in supporting human rating in assessments. By incorporating automation, we can save time and resources while reducing biases and subjectivity inherent in human rating processes. Automated language assessments offer the advantage of accessibility, providing equal opportunities and economic feasibility for individuals who lack access to traditional assessment centers or necessary resources. LLM-based language assessments provide valuable feedback and support to learners, aiding in the enhancement of their language proficiency and the achievement of their goals. This personalized feedback can cater to individual learner needs, facilitating a more tailored and effective language-learning experience.

There are three important areas that merit further exploration. First, prompt engineering requires attention to ensure optimal performance of LLM-based AES across different language types. This study revealed that GPT-4, when prompted with all measures, outperformed models prompted with fewer measures. Therefore, investigating and refining prompt strategies can enhance the effectiveness of LLMs in automated language assessments. Second, it is crucial to explore the application of LLMs in second-language assessment and learning for oral proficiency, as well as their potential in under-resourced languages. Recent advancements in self-supervised machine learning techniques have significantly improved automatic speech recognition (ASR) systems, opening up new possibilities for creating reliable ASR systems, particularly for under-resourced languages with limited data. However, challenges persist in the field of ASR. First, ASR assumes correct word pronunciation for automatic pronunciation evaluation, which proves challenging for learners in the early stages of language acquisition due to diverse accents influenced by their native languages. Accurately segmenting short words becomes problematic in such cases. Second, developing precise audio-text transcriptions for languages with non-native accented speech poses a formidable task. Last, assessing oral proficiency levels involves capturing various linguistic features, including fluency, pronunciation, accuracy, and complexity, which are not easily captured by current NLP technology.

Data availability

The dataset utilized was obtained from the International Corpus of Japanese as a Second Language (I-JAS). The data URLs: [ https://www2.ninjal.ac.jp/jll/lsaj/ihome2.html ].

J-CAT and TTBJ are two computerized adaptive tests used to assess Japanese language proficiency.

SPOT is a specific component of the TTBJ test.

J-CAT: https://www.j-cat2.org/html/ja/pages/interpret.html

SPOT: https://ttbj.cegloc.tsukuba.ac.jp/p1.html#SPOT .

The study utilized a prompt-based GPT-4 model, developed by OpenAI, which has an impressive architecture with 1.8 trillion parameters across 120 layers. GPT-4 was trained on a vast dataset of 13 trillion tokens, using two stages: initial training on internet text datasets to predict the next token, and subsequent fine-tuning through reinforcement learning from human feedback.

https://www2.ninjal.ac.jp/jll/lsaj/ihome2-en.html .

http://jhlee.sakura.ne.jp/JEV/ by Japanese Learning Dictionary Support Group 2015.

We express our sincere gratitude to the reviewer for bringing this matter to our attention.

On February 7, 2023, Microsoft began rolling out a major overhaul to Bing that included a new chatbot feature based on OpenAI’s GPT-4 (Bing.com).

Appendix E-F present the analysis results of the QWK coefficient between the scores computed by the human raters and the BERT, OCLL models.

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This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.

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Wenchao Li is in charge of conceptualization, validation, formal analysis, investigation, data curation, visualization and writing the draft. Haitao Liu is in charge of supervision.

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Li, W., Liu, H. Applying large language models for automated essay scoring for non-native Japanese. Humanit Soc Sci Commun 11 , 723 (2024). https://doi.org/10.1057/s41599-024-03209-9

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Received : 02 February 2024

Accepted : 16 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1057/s41599-024-03209-9

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