literary research analysis

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What's a good topic?

Choosing a topic can be one of the hardest parts of writing a paper. There are so many possible things to write about, and even if you have a general idea, it can be hard to know whether your topic is a good one. 

Writing a literature paper is different from writing many other kinds of papers. In literary analysis, it's not the ideas of other people that matter as much as your own interpretation of the texts you're reading. The bulk of your paper will be made up of your analysis of the text: the use of language, imagery, rhythm and repetition, word choice, the structure of the plot, or the representations of characters, emotions, events, or places. Your job is to analyze these elements of the text and through your analysis to assert an idea, or a claim, about the text, the author, or the context in which the text was written.

So what makes a good topic? A good topic is a theme that you think is represented in the text you're reading. But how do you get from a good topic to a good research question? 

What's a good research question?

Once you recognize a theme in a text or texts, your next step is to determine what you think the texts are saying about that theme. Read the text again, paying particular attention to your theme. What does your interpretation lead you think about the theme or idea? This is your claim, and your paper is structured around using analysis of the text or texts to support your claim. 

For example, you may be interested in looking at community or society in Thoreau's "Walden." You may have read the text and noticed a contradiction between Thoreau's claims of self-reliance and his interaction with society. You would then re-read the text, asking yourself as you read "What is the representation of society and Thoreau's relationship to it in 'Walden'?" After reading the text closely and paying special attention to these aspects of "Walden," you may be ready to make the claim that while Thoreau believed he was self-reliant, in truth he was still part of a network of people, and still part of his society and community. Or you may discover that your initial thought was wrong, and that Thoreau really did separate himself from his community in the way he wrote about. 

Types of Sources

There are a lot of different kinds of sources that you can use in your analysis. This guide will show you how to find and use these by type. 

Primary Sources  are the main pieces of evidence you will use to make your claim. The texts you are reading are a primary source; they are the most important primary source you're working with. Other examples are newspaper and magazine articles, diaries and letters, photographs, maps, and reviews written or created at the same time as your text. These sources can help you put your subject into context. 

Reference Sources  give you a broad overview of a person, place, event, or idea. They provide commonly known facts. Reference sources are not cited in your paper, but can be very useful for grounding you in your subject and ensuring that you have solid background information.  Literary biographies   are a form of reference material, and give you lots of information about authors, with an emphasis on how their lives are related to their writing. 

Secondary Sources  are also sometimes referred to as  criticism.  These are books and articles that scholars have written about a particular work of literature, movement, or author. Criticism can help you get a sense of the themes that other scholars read in a particular text. They may help inform your own understanding of a text, either because they reinforce your interpretation, or differ from it. Criticism is usually published in books or as articles in scholarly journals. 

So how do I use sources?

Primary sources are the evidence that we use to support our claims. They aren't the articles that other scholars and researchers have written, but original source material that we can use to better understand our topic. Primary sources in literary research include the text or texts that you're analyzing, but might also include additional material like letters written by the author, photographs, reviews written when the text was published, newspapers articles. Many different kinds of things can be used as primary sources, depending on your subject. 

For example, if you're studying Thoreau's relationships with others, you may want to find out more about Thoreau's role in his community by reading primary source material (letters that he wrote to friends and colleagues, newspaper articles about him or about his community) or by reading more about the context of his life in Massachusetts (the political and artistic movements of which he was part, the actual location of his cabin in relation to the town of Concord). These additional sources are used to support your interpretation of the text you're analyzing. 

You may want to use secondary sources to discuss other scholars' ideas and interpretations of the topic and text you're writing about, especially if you don't agree with their interpretations. Pay especially close attention to aspects of your topic that scholars don't agree about, and to different interpretations or ideas about a text. If there are major debates about the authors or texts you're studying, you'll want to reference them in the paper to help inform your reader and provide context to your own interpretation. 

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Literature Research

Literary analysis.

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Tools for Literary Analysis

T here are several useful sites that help students analyze literature.  Look at the questions and think about the answers using your readings for content.

Visit these links for information on the PROCESS of Literary Analysis:

• A Short Guide to Close Reading for Literary Analysis Learn the practice of close reading, the first step in literary analysis.
• Literary Analysis Guide Discusses how to analyze a passage of text to strengthen your discussion of the literature.
• Writing a Literary Analysis A short slideshow from Purdue Owl introducing how to write a literary analysis.

Elements of Literature

Common elements of literature.

• Plot:  the sequence of events that occur through a work to produce a coherent narrative or story.
• Setting:  the time and place in which a story takes place.
• Protagonist:  the main character of story, novel or a play.
• Antagonist:  a character in conflict with the protagonist.
• Narrator: the voice telling the story or speaking to the audience.
• Dialogue :  spoken exchanges between characters in a dramatic or literary work, usually between two or more speakers.
• Conflict:  an issue in a narrative around which the whole story revolves.
• Tone:  a way of communicating information that conveys an attitude. Authors convey tone through a combination of word-choice, imagery, perspective, style, and subject matter.
• Theme:  the central idea or concept of a story.

For more literary elements, consult the links below.

• Literary Terms A list of literary terms that can help you interpret, critique, and respond to a variety of different written works from the Purdue Online Writing Lab.
• Narrative Techniques Lists, defines and provides examples of the many methods author's use to convey meaning in a story through setting, plot, perspective, style, theme, and character.
• Literary Devices A website dedicated to literary devices with definitions and examples. Also includes grammatical terms and definitions of types of essays.

This table offers some ways to think about the author, the context of the literature you are reading, even the themes that are being discussed.  

THINK ABOUT SEVERAL IMPORTANT ELEMENTS IN THE AUTHOR'S LIFE, SUCH AS THE CONCEPTS IN THIS LIST.

MIX AND MATCH.

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• A Research Guide
• Writing Guide
• Assignment Writing

How to Write a Literary Analysis

• What is the underlying or intentional message that the author hoped to deliver? Ask yourself what the primary theme or concept the author was hoping to create and what message they wanted to send to their readers.
• Who are the characters, not just on the outside – who are they really? Consider the external factors as well: The tone, the environment, the plot and any other literary devices that the author may have used to ensure that readers truly understand the characters and their value.
• Why did the author choose to use specific literary devices in the specific ways they were used? What did the author intend on teaching their readers? How have the characters or the setting or plot twist helped the reader to better understand the theme of the writing?

What is a Literary Analysis

Definition of literary analysis, literary analysis outline.

• Remember to include the full name of the author, the title of the piece that you will be analyzing and any supplementary information that will be helpful to strengthen your thesis and following thematic statements.
• Clearly deliver your thematic statement or statements. A thematic statement is the overall concept or main idea as it relates to life that the author is attempting to deliver. (This is the ‘why’)
• End your introduction with your thesis statement. Your thesis statement should include the who, what, why and Remember to include parts of the question that you intend to answer.
• Start each paragraph with a concise argument that relates to your initial thesis statement.
• Each paragraph must have a single point of view.
• Include relevant quotes to validate your argument. This should focus on “how” things work and also answer your question.
• Quotes should consist of both narrative and dialogue.
• Don’t simply uncover a literary technique and offer an example of it. Instead explain how using that particular technique relates to the question you are answering.
• End with a strong statement that reiterates the sole focus of the paragraph.
• Consider mentioning the theme in your body paragraphs, but do not divert from the question being answered.
• Start your conclusion by carefully and concisely restating your thesis – but do not do so verbatim.
• Clearly explain how the ideas and concepts presented in the body of the essay depict the theme. Simply put, your conclusion should also explain what message the author was hoping to deliver about life and how it relates to the examples you’ve included in your analysis.

Types of Literary Analysis

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Biographical information, context - historical/social/cultural, finding book reviews, literary criticism & analysis, background & theory, select literary criticism books.

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• More on writing a literary analysis from Purdue OWL

​ Writing & Research Help :

What Makes a Good Literature Paper?  Tips on writitng a good thesis from Purdue OWL

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LibSearch Guide : For more help, including how to request items in LibSearch that are not at St. Kate's.

Resources for looking up information about authors

• Dictionary of Literary Biography - Gale Literature This link opens in a new window Biographical and critical essays on the lives, works, and careers of the world's most influential literary figures from all eras and genres. more... less... Provides more than 16,000 biographical and critical essays on the lives, works, and careers of the world's most influential literary figures from all eras and genres. Includes full-page images from the entire Dictionary of Literary Biography series (the DLB Main Series, DLB Documentary Series, and DLB Yearbook Series), a mainstay in the reference collection for over 30 years.
• Contemporary Authors - Gale Literature This link opens in a new window Provides biographical and bibliographical information on more than 120,000 U.S. and international authors. more... less... This resource covers more than 120,000 novelists, poets, playwrights, nonfiction writers, journalists, and scriptwriters.
• Something About the Author - Gale Literature This link opens in a new window Read about the lives and works of authors and illustrators of literature for children and young adults, with full page images of Something About the Author from volume 1 through volume 300.

Resources to find primary and secondary sources to give you historical background on a work

• Historical Abstracts This link opens in a new window Good for international historical context. more... less... This resource excludes the history of United States and Canada, which are covered in America: History and Life. Indexes and abstracts articles from more than 2,000 journals. Also includes book reviews and dissertations. Indexing coverage begins in 1954. This database is produced by ABC-CLIO, but you access it through the EBSCO website.
• America: History & Life This link opens in a new window Good for US historical context. more... less... A complete bibliographic reference to the history of the United States and Canada from prehistory to the present. It indexes and abstracts more than 2,000 journals published worldwide. Book reviews, media reviews, and disserations are also indexed. Indexing began in 1954. This database is produced by ABC-CLIO, but you access it through EBSCO.
• Cambridge Histories Online This link opens in a new window Historical context.

A comprehensive social sciences research database with full text for more than 1,700 journals dating back to 1908. It also provides full text for more than 830 books and 14,000 conference papers. Subject indexing is based on a sociological thesaurus designed by subject experts and expert lexicographers.

• Communication and Mass Media Complete This link opens in a new window Good for mass media/popular culture analyses more... less... This database offers cover-to-cover indexing and abstracts for more than 440 journals in communication and mass media, selective coverage of nearly 200 additional journals, and full text for more than 500 journals. Many of the more important journals have indexing, abstracts, PDFs and searchable cited references from their first issues (dating as far back as 1915) to the present. Special features include a Communication Thesaurus, searchable cited references for peer-reviewed journals, and profiles providing biographical and bibliographical data for of more than 3,000 authors in the field. The database incorporates the content of CommSearch (formerly produced by the National Communication Association) and Mass Media Articles Index (formerly produced by Penn State) along with numerous other journals in communication, mass media, and other closely-related fields of study.
• New York Times (ProQuest Historical Newspapers) This link opens in a new window Source of primary historical information: New York Times newspaper articles from 1851-2007.
• OED Online This link opens in a new window Online version of The Oxford English Dictionary , the authoritative guide to the meaning, history, and pronunciation of over half a million words, both present and past, from across the English-speaking world more... less... The OED is the accepted authority on the evolution of the English language over the last millennium. It traces the usage of words through 2.5 million quotations from a wide range of international English language sources, from classic literature and specialist periodicals to film scripts and cookery books. The OED covers words from across the English-speaking world, from North America to South Africa, from Australia and New Zealand to the Caribbean. It also offers the best in etymological analysis and in listing of variant spellings, and it shows pronunciation using the International Phonetic Alphabet. As the OED is a historical dictionary, its entry structure is very different from that of a dictionary of current English, in which only present-day senses are covered, and in which the most common meanings or senses are described first. For each word in the OED, the various groupings of senses are dealt with in chronological order according to the quotation evidence. Updated quarterly with at least 2,500 new and revised entries.
• ArchiveGrid This link opens in a new window A finding aid to primary source materials held by thousands of libraries, museums, historical societies, and archives worldwide. more... less... ArchiveGrid allows you to search for historical documents, personal papers, family histories, and other primary source material held in archives throughout the world. It includes descriptions of archival collections held by thousands of libraries, museums, historical societies and archives. It also provides information useful for contacting an archive to arrange a visit to examine materials or to order copies.

• Literary Hub Use this website to find multiple reviews for books all in one place

Other excellent literary/book review sites:

• New York Times Book Reviews
• LA Review of Books
• Electric Literature
• Paris Review
• The Millions
• The Thread - MPR
• Literary Podcasts

Resources for looking up literary criticism written by scholars

• MLA International Bibliography This link opens in a new window A database of scholarly articles in the fields of literature, language, linguistics, and folklore produced by the Modern Language Association of America. more... less... Coverage begins in 1923.

An archive of scholarly full-text journals in the humanities, social sciences, and sciences.

The JSTOR archive holds the complete digitized back runs of core scholarly journals, starting with the very first issues, some dating as far back as the 1600s. Subject areas include the humanities, social sciences, and sciences. JSTOR includes various collections, and the St. Kate's Library has purchased the Arts and Sciences Collections I-VII and the Biological Sciences Collection. These collections include subsets for ecology & botany, language & literature, and music.  JSTOR's agreements with publishers often include a "moving wall," which means that the most recent years (typically 3-5 years) are not available.

An Internet search engine for scholarly literature, including peer-reviewed papers, theses, books, preprints, abstracts and technical reports.

Google Scholar enables you to search specifically for scholarly literature, including peer-reviewed papers, theses, books, preprints, abstracts and technical reports from all broad areas of research. Use Google Scholar to find articles from a wide variety of academic publishers, professional societies, preprint repositories and universities, as well as scholarly articles available across the web. Our link to Google Scholar enables you to see which documents you can access through St. Kate's.

Resources for looking up information on theories to apply in literary criticism

• Literary Theory and Schools of Criticism A breakdown of major literary schools of thought from Purdue OWL

A digital reference library of dictionaries, encyclopedias, thesauri, books of quotations, and subject-specific titles, covering many subjects.

Credo Reference is a digital reference library that places a world of factual information at your fingertips. Containing more than 500 high-quality reference books from some of the world's leading publishers, this is a great place to start your research. The database contains dictionaries, encyclopedias, thesauri, and books of quotations, as well as a variety of subject-specific titles covering everything from the arts to accountancy and law to literature. Besides textual information, you can also find thousands of images (including 17,000 art images from The Bridgeman Art Library Archive), sound files (especially pronunciations of words), and animations.

• Oxford Reference This link opens in a new window Full-text dictionaries and other reference books in many subject areas from Oxford University Press more... less... Our access includes: > More than 200 dictionary, language reference, and subject reference works > A growing growing collection of titles from the acclaimed Oxford Companions Series > The Oxford Dictionary of Quotations > Maps, illustrations, and timelines

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8.12: Essay Type- Literary Research

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• Heather Ringo & Athena Kashyap
• City College of San Francisco via ASCCC Open Educational Resources Initiative

The Research Essay

The research essay is basically a literary analysis essay supported by research. Usually, this research takes the form of literary criticism. For more on literary criticism, check out the literary criticism chapter.

Finding a Topic

Often times, instructors will assign a research topic. Be sure to consult with your instructor and/or the assignment prompt given by the instructor. If, however, the assignment is open-ended, then that is exciting news! You can write an essay about your interests in literature. It is like an adventure! However, too much choice can be debilitating. You want an essay that is large enough in scope that you can write an entire essay about it, but you don't want too large of a topic as you may not be able to feasibly cover it in the short amount of time you have. Therefore, a good way to find a "Goldilocks" topic—not too big and not too small—is to start with a simple formula and adjust as you go.

• Find the literary text you want to write about. For example, "Bajadas" by Francisco Cantu or Hamlet by William Shakespeare.
• Find an aspect or angle about that text which interests you. For example, when writing about "Bajadas," you might want to write about the ecology of the border desert, the use of animal symbolism, or the history of border politics and how it may have informed the story. When writing about Hamlet, you might be interested in the way female characters like Ophelia and Gertrude are treated, about the symbolism behind Ophelia's flowers, or about espionage on the part of Rosencrantz and Guildenstern.

Finding a topic formula

Topic = angle + text

Topic = Espionage + Hamlet

Working thesis statement: In this essay, I will be exploring espionage in Hamlet

Brainstorming

Once you come up with a topic, start brainstorming. Yes, brainstorm before you perform research. The reason for this is because your instructor is interested in your original ideas about the text, not the ideas of scholars. Secondary sources should only be used to support your own original ideas. If you start with research, it is much more difficult to come up with your own ideas, because all of your ideas are going to sound a lot like the articles you read. So start with your initial impressions of your topic.

Brainstorming Example:

Espionage in Hamlet is interesting to me because it seems like everyone in Elsinore is spying on each other. For example, Claudius sends Rosencrantz and Guildenstern and Ophelia to spy on Hamlet. Polonius dies because he is spying on Hamlet. In turn, Hamlet spies on Claudius during the play within the play. Spying seems to play a huge part in the play, and I wonder why. Was there espionage in Shakespeare's London? Might it have influenced or inspired his writing?

Once you get some of your basic ideas down, you might start outlining the "big ideas" of your essay. This will help when it comes to writing the essay and organizing your ideas. It will also help you when it comes time to research.

Outlining Example

• Background of espionage in Hamlet
• Espionage in Shakespeare's Time
• Analysis of Rosencrantz and Guildenstern Spying scene
• Analysis of Ophelia spying scene
• Analysis of Polonius spying scene
• Analysis of Claudius/Hamlet spying scene
• Analysis of Hamlet conducting counter-espionage against Claudius
• How might these scenes have been influenced by espionage in Shakespeare's time?

Before you begin researching, write what you can on your own. For example, write a literary analysis essay where you simply examine and analyze the literature without input from outside sources. This will allow you to solidify your ideas. It will also make it easier to find search terms when you are ready to research.

Researching

Once you have a solid topic and writing, it's time to research your essay topic. Starting with some questions about your topic is a great way to start. See the chapter on navigating scholarly sources for a more detailed look at how to find research material.

Annotated Bibliography

As you read, keep track of your sources using an Annotated Bibliography or Research Log. Basically, an Annotated Bibliography is just like a regular Works Cited page, except every source is summarized after the bibliographical entry. Because you will be reading a lot of different sources, some 20+ pages in length, it can be difficult to keep track of the ideas of each source. An Annotated Bibliography is a tool to help you keep track of your research. This also can help you avoid plagiarism! When taking notes on your sources, be sure to clearly mark summary, paraphrase, and quotation so that you ethically attribute words and ideas to their author.

Annotated Bibliography Example

Student first name Last name

Professor Soandso

30 March 2019

Annotated Bibliography: Espionage in Hamlet

Working title: Elizabethan Spy Culture Reflected in Hamlet

Working thesis : for this essay I will explore how Elizabethan spy culture might have influenced Hamlet. For example, I will look at how Rosencrantz and Guildenstern are used to spy on Hamlet, and how it seems like everyone is watching everyone in Elsinore: perhaps Claudius is a stand-in for the English monarchy, a critique of its corruption?

Honan, Park. Christopher Marlowe : Poet & Spy. OUP Oxford, 2005. EBSCOhost, ezproxy.solano.edu/login?url= http://search.ebscohost.com/login.as...&site=eds-live .

Christopher Marlowe was a contemporary of Shakespeare. He was also his competitor as a fellow playwright and poet. This text is a scholarly biography of Marlowe’s life as a spy and poet. This source gives a picture of the cultural context of Shakespeare: of particular relevance to my research is the chapter describing Marlowe and Shakespeare’s relationship (187-196). Though this gives interesting background information, it might solely be useful to note that Shakespeare regularly rubbed elbows with a spy, Marlowe, so he was at least somewhat familiar with Elizabethan spy culture, though how much he knew is a mystery. Otherwise, this text may not be very useful because it only briefly mentions Shakespeare.

Sample Student Research Essay

Text: "The Hunting of the Hare" by Margaret Cavendish

Topic: Symbol & Theme: Humanity's Attraction to Destruction and Violence

Rebekah’s research essay on Margaret Cavendish’s poem “The Hunting of the Hare” illustrates several of the principles discussed in this chapter:

• How to integrate scholarly secondary sources without relinquishing control of the argument
• How to make it clear whose ideas are whose through use of tag words and phrases
• How to employ parenthetical in-text citations according to MLA guidelines
• How to construct a Works Cited page according to MLA guidelines

Rebekah Fish

English 3460

Human Nature in Margaret Cavendish’s “The Hunting of the Hare”

Margaret Cavendish’s 1653 poem “The Hunting of the Hare” relates the cruel fate of a hare that has fallen prey to a group of hunters. A study of this poem suggests that Cavendish can be viewed as one of the first supporters for animal rights as she criticizes the cruelty of men who kill animals for sport. On a more personal level, Cavendish could have closely identified with the hare, which is ostensibly humanlike, and also with its fear. She might have even intended to parallel her critics to the dogs and the hunters within the poem. On a grander scale, Cavendish might be making the critical judgment that humankind seeks enjoyment through violent competition with others. Through a study of the many different thematic levels of the poem, Margaret Cavendish’s “The Hunting of the Hare” seems to have an overarching theme of humanity’s destructive attraction to violence in order to achieve supremacy.

It is evident through the poet’s portrayal of the hare that it is meant to be seen as a significant and even a symbolic figure, beginning in the first line of the poem where the hare is granted the name “Wat.” He is humanlike, “glaring” across the landscape as his “Haires blew up behind” him in the wind instead of his fur (4 and 6). The hare is also described as “wise” instead of merely being a sentient creature, and Cavendish makes its humanlike features even more evident as the hare “walks about” rather than hopping or crawling (Cavendish 7 and 11). Another way the rabbit is seemingly anthropomorphized is through the continual use of the personal pronoun “him” in the poem, which is used instead of “it.” To indicate her disapproval of the unethical treatment of all animals, near the very end of the poem Cavendish grants all creatures the same humanlike quality as the hare by saying that creatures are being “murdered” (100) by men instead of “killed.” The word “murder” connotes unlawfulness and makes a connection between that illicitness and the killing of animals, indicating that all sentient life, that of humans and animals, is important and worthy of being preserved. Some may even argue that Cavendish was trying to make a point that humankind should not express dominant authority over other creatures through the use of violence, because, within the last lines of the poem, man is portrayed not only as murderous but also as an oppressive tyrant that rules over all other living creatures.

Cavendish’s humanlike portrayal of the hare might raise concerns for some readers. Bruce Thomas Boehrer discusses some critics’ objection to an author’s anthropomorphizing nonhuman characters. To anthropomorphize is to project one’s own tendencies and traits onto another species. Some critics argue that this act ignores a nonhuman species’ real behaviors and traits and illustrates humans’ feeling of dominance over nature. However, as Boehrer explains, many animal characters in literature “challenge the human-animal divide” (5) and force people to examine their values, especially those related to nature. Donna Landry supports the view that “The Hunting of the Hare” raises these issues. She argues that in Cavendish’s work, she promotes the “democratizing of relations between humans and other species” (471). Rather than emphasizing the superiority of human emotions by anthropomorphizing the hare, Cavendish humanizes him in order to bridge the gap between the reader and the hare. Paul Salzman states that Cavendish’s main goal as a writer was “to enter into an empathetic relationship with the world around her” (142). In “The Hunting of the Hare,” Cavendish portrays the hare with empathy in order to persuade the reader that committing unnecessary violence on animals is cruel and terrible.

In addition, the description of the hare is used to form and emphasize the strong connection between the hare and Cavendish, who was similarly being pursued by her critics as a female writer. This criticism is clearly shown through the description of Cavendish by Mary Evelyn, who portrayed her as extravagant and vain and said that her discourse was “as airy, empty, whimsical, and rambling as her books” (Qtd. in Damrosch and Dettmar 2058-9). Many people of Cavendish’s time viewed her as outrageous, partly because publicly recognized women writers were rare during the seventeenth century. Although scholars seriously study Cavendish’s work now, Emma L.E. Rees says that because of the harsh critics of her time, “The impression which lasted for many years was of an eccentric, disturbed and arrogant woman” (11). “The Hunting of the Hare” could be interpreted as a response to this criticism. Her critics, paralleled by both the “cruel dogs” (16) and the men in the poem, are often referred to as merciless. The critics are described as nosy through common references to the dogs and how they always “thrust [their] snuffling nose[s]” into things (64). They are also described as loudmouths through the image of dogs who cry out with their “wide mouths” (19). While at times, Cavendish seems to be uncaring as to what the critics say about her, at other times, she seems terrified of the public’s opinion of her life and writing, much like the hare’s terror of being pursued. She suggests that, in public, she hides her fear of the critics, similarly to the hare when, “Licking his feet, he wiped his ears so clean / That none could tell that Wat had hunted been” (41-2). Although critics continued to pursue her, Cavendish emphasizes through the poem that she will continue to maintain her composure until the very end, like the hare does until his death. Yet, this continual pursuing and killing of hares, which parallels Cavendish’s experience, critiques human nature’s desire for supremacy over all living things—even each other.

Not only can Cavendish’s poem be seen as a response to animal cruelty and the cruelty of critics, but it can also be seen as an assessment of how humankind treats its brethren. In the poem, the men are portrayed as bloodthirsty monsters that thrive off cruelty to others. The men in Cavendish’s poem, who “destroy those lives that God did make” (98) solely for “sport or recreation’s sake” (97), seek to kill the rabbit, the symbol, through heavy personification, of a fellow human (Cavendish 2062).

Margaret Cavendish’s “The Hunting of the Hare” is a comment on human nature and the desire for obtaining dominion over others by any means necessary. Through her extensive use of pathos throughout the poem, her audience at the time was meant to feel a sense of culpability and a desire to change. Despite her portrayal of human nature as inherently evil, the guilt the audience is supposed to feel offers a sense of hope, as it indicates that human nature is capable of being altered and even changed.

Works Cited

Boehrer, Bruce Thomas. Animal Characters: Nonhuman Beings in Early Modern Literature, U of Pennsylvania P, 2010.

Cavendish, Margaret. “The Hunting of the Hare.” 1653. UC Press E-books Collection, publishing.cdlib. org/ucpressebooks/view?docId=kt7q2nc9xn&chunk. id=ss1.55&toc.depth=100&toc.id=ch09&brand=eschol. Accessed 3 October 2013.

Damrosch, David and Kevin J. H. Dettmar. “Margaret Cavendish, Duchess of Newcastle.” The Longman Anthology: British Literature, edited by David Damrosch and Kevin J. H. Dettmar, Longman, 2010, pp. 2060-63.

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Textual Analysis – Types, Examples and Guide

Table of Contents

Textual Analysis

Textual analysis is the process of examining a text in order to understand its meaning. It can be used to analyze any type of text, including literature , poetry, speeches, and scientific papers. Textual analysis involves analyzing the structure, content, and style of a text.

Textual analysis can be used to understand a text’s author, date, and audience. It can also reveal how a text was constructed and how it functions as a piece of communication.

Textual Analysis in Research

Textual analysis is a valuable tool in research because it allows researchers to examine and interpret text data in a systematic and rigorous way. Here are some ways that textual analysis can be used in research:

• To explore research questions: Textual analysis can be used to explore research questions in various fields, such as literature, media studies, and social sciences. It can provide insight into the meaning, interpretation, and communication patterns of text.
• To identify patterns and themes: Textual analysis can help identify patterns and themes within a set of text data, such as analyzing the representation of gender or race in media.
• To evaluate interventions: Textual analysis can be used to evaluate the effectiveness of interventions, such as analyzing the language and messaging of public health campaigns.
• To inform policy and practice: Textual analysis can provide insights that inform policy and practice, such as analyzing legal documents to inform policy decisions.
• To analyze historical data: Textual analysis can be used to analyze historical data, such as letters, diaries, and newspapers, to provide insights into historical events and social contexts.

Textual Analysis in Cultural and Media Studies

Textual analysis is a key tool in cultural and media studies as it enables researchers to analyze the meanings, representations, and discourses present in cultural and media texts. Here are some ways that textual analysis is used in cultural and media studies:

• To analyze representation: Textual analysis can be used to analyze the representation of different social groups, such as gender, race, and sexuality, in media and cultural texts. This analysis can provide insights into how these groups are constructed and represented in society.
• To analyze cultural meanings: Textual analysis can be used to analyze the cultural meanings and symbols present in media and cultural texts. This analysis can provide insights into how culture and society are constructed and understood.
• To analyze discourse: Textual analysis can be used to analyze the discourse present in cultural and media texts. This analysis can provide insights into how language is used to construct meaning and power relations.
• To analyze media content: Textual analysis can be used to analyze media content, such as news articles, TV shows, and films, to understand how they shape our understanding of the world around us.
• To analyze advertising : Textual analysis can be used to analyze advertising campaigns to understand how they construct meanings, identities, and desires.

Textual Analysis in the Social Sciences

Textual analysis is a valuable tool in the social sciences as it enables researchers to analyze and interpret text data in a systematic and rigorous way. Here are some ways that textual analysis is used in the social sciences:

• To analyze interview data: Textual analysis can be used to analyze interview data, such as transcribed interviews, to identify patterns and themes in the data.
• To analyze survey responses: Textual analysis can be used to analyze survey responses to identify patterns and themes in the data.
• To analyze social media data: Textual analysis can be used to analyze social media data, such as tweets and Facebook posts, to identify patterns and themes in the data.
• To analyze policy documents: Textual analysis can be used to analyze policy documents, such as government reports and legislation, to identify discourses and power relations present in the policy.
• To analyze historical data: Textual analysis can be used to analyze historical data, such as letters and diaries, to provide insights into historical events and social contexts.

Textual Analysis in Literary Studies

Textual analysis is a key tool in literary studies as it enables researchers to analyze and interpret literary texts in a systematic and rigorous way. Here are some ways that textual analysis is used in literary studies:

• To analyze narrative structure: Textual analysis can be used to analyze the narrative structure of a literary text, such as identifying the plot, character development, and point of view.
• To analyze language and style: Textual analysis can be used to analyze the language and style used in a literary text, such as identifying figurative language, symbolism, and rhetorical devices.
• To analyze themes and motifs: Textual analysis can be used to analyze the themes and motifs present in a literary text, such as identifying recurring symbols, themes, and motifs.
• To analyze historical and cultural context: Textual analysis can be used to analyze the historical and cultural context of a literary text, such as identifying how the text reflects the social and political context of its time.
• To analyze intertextuality: Textual analysis can be used to analyze the intertextuality of a literary text, such as identifying how the text references or is influenced by other literary works.

Textual Analysis Methods

Textual analysis methods are techniques used to analyze and interpret various types of text, including written documents, audio and video recordings, and online content. These methods are commonly used in fields such as linguistics, communication studies, sociology, psychology, and literature.

Some common textual analysis methods include:

Content Analysis

This involves identifying patterns and themes within a set of text data. This method is often used to analyze media content or other types of written materials, such as policy documents or legal briefs.

Discourse Analysis

This involves examining how language is used to construct meaning in social contexts. This method is often used to analyze political speeches or other types of public discourse.

Critical Discourse Analysis

This involves examining how power and social relations are constructed through language use, particularly in political and social contexts.

Narrative Analysis

This involves examining the structure and content of stories or narratives within a set of text data. This method is often used to analyze literary texts or oral histories.

This involves analyzing the meaning of signs and symbols within a set of text data. This method is often used to analyze advertising or other types of visual media.

Text mining

This involves using computational techniques to extract patterns and insights from large sets of text data. This method is often used in fields such as marketing and social media analysis.

Close Reading

This involves a detailed and in-depth analysis of a particular text, focusing on the language, style, and literary techniques used by the author.

How to Conduct Textual Analysis

Here are some general steps to conduct textual analysis:

• Choose your research question: Define your research question and identify the text or set of texts that you want to analyze.
• F amiliarize yourself with the text: Read and re-read the text, paying close attention to its language, structure, and content. Take notes on key themes, patterns, and ideas that emerge.
• Choose your analytical approach: Select the appropriate analytical approach for your research question, such as close reading, thematic analysis, content analysis, or discourse analysis.
• Create a coding scheme: If you are conducting content analysis, create a coding scheme to categorize and analyze the content of the text. This may involve identifying specific words, themes, or ideas to code.
• Code the text: Apply your coding scheme to the text and systematically categorize the content based on the identified themes or patterns.
• Analyze the data: Once you have coded the text, analyze the data to identify key patterns, themes, or trends. Use appropriate software or tools to help with this process if needed.
• Draw conclusions: Draw conclusions based on your analysis and answer your research question. Present your findings and provide evidence to support your conclusions.
• R eflect on limitations and implications: Reflect on the limitations of your analysis, such as any biases or limitations of the selected method. Also, discuss the implications of your findings and their relevance to the broader research field.

When to use Textual Analysis

Textual analysis can be used in various research fields and contexts. Here are some situations when textual analysis can be useful:

• Understanding meaning and interpretation: Textual analysis can help understand the meaning and interpretation of text, such as literature, media, and social media.
• Analyzing communication patterns: Textual analysis can be used to analyze communication patterns in different contexts, such as political speeches, social media conversations, and legal documents.
• Exploring cultural and social contexts: Textual analysis can be used to explore cultural and social contexts, such as the representation of gender, race, and identity in media.
• Examining historical documents: Textual analysis can be used to examine historical documents, such as letters, diaries, and newspapers.
• Evaluating marketing and advertising campaigns: Textual analysis can be used to evaluate marketing and advertising campaigns, such as analyzing the language, symbols, and imagery used.

Examples of Textual Analysis

Here are a few examples:

• Media Analysis: Textual analysis is frequently used in media studies to examine how news outlets and social media platforms frame and present news stories. Researchers can use textual analysis to examine the language and images used in news articles, tweets, and other forms of media to identify patterns and biases.
• Customer Feedback Analysis: Textual analysis is often used by businesses to analyze customer feedback, such as online reviews or social media posts, to identify common themes and areas for improvement. This allows companies to make data-driven decisions and improve their products or services.
• Political Discourse Analysis: Textual analysis is commonly used in political science to analyze political speeches, debates, and other forms of political communication. Researchers can use this method to identify the language and rhetoric used by politicians, as well as the strategies they employ to appeal to different audiences.
• Literary Analysis: Textual analysis is a fundamental tool in literary criticism, allowing scholars to examine the language, structure, and themes of literary works. This can involve close reading of individual texts or analysis of larger literary movements.
• Sentiment Analysis: Textual analysis is used to analyze social media posts, customer feedback, or other sources of text data to determine the sentiment of the text. This can be useful for businesses or organizations to understand how their brand or product is perceived in the market.

Purpose of Textual Analysis

There are several specific purposes for using textual analysis, including:

• To identify and interpret patterns in language use: Textual analysis can help researchers identify patterns in language use, such as common themes, recurring phrases, and rhetorical devices. This can provide insights into the values and beliefs that underpin the text.
• To explore the cultural context of the text: Textual analysis can help researchers understand the cultural context in which the text was produced, including the historical, social, and political factors that shaped the language and messages.
• To examine the intended and unintended meanings of the text: Textual analysis can help researchers uncover both the intended and unintended meanings of the text, and to explore how the language is used to convey certain messages or values.
• To understand how texts create and reinforce social and cultural identities: Textual analysis can help researchers understand how texts contribute to the creation and reinforcement of social and cultural identities, such as gender, race, ethnicity, and nationality.

Applications of Textual Analysis

Here are some common applications of textual analysis:

Media Studies

Textual analysis is frequently used in media studies to analyze news articles, advertisements, and social media posts to identify patterns and biases in media representation.

Literary Criticism

Textual analysis is a fundamental tool in literary criticism, allowing scholars to examine the language, structure, and themes of literary works.

Political Science

Textual analysis is commonly used in political science to analyze political speeches, debates, and other forms of political communication.

Marketing and Consumer Research

Textual analysis is used to analyze customer feedback, such as online reviews or social media posts, to identify common themes and areas for improvement.

Healthcare Research

Textual analysis is used to analyze patient feedback and medical records to identify patterns in patient experiences and improve healthcare services.

Social Sciences

Textual analysis is used in various fields within social sciences, such as sociology, anthropology, and psychology, to analyze various forms of data, including interviews, field notes, and documents.

Linguistics

Textual analysis is used in linguistics to study language use and its relationship to social and cultural contexts.

Advantages of Textual Analysis

There are several advantages of textual analysis in research. Here are some of the key advantages:

• Systematic and objective: Textual analysis is a systematic and objective method of analyzing text data. It enables researchers to analyze text data in a consistent and rigorous way, minimizing the risk of bias or subjectivity.
• Versatile : Textual analysis can be used to analyze a wide range of text data, including interview transcripts, survey responses, social media data, policy documents, and literary texts.
• Efficient : Textual analysis can be a more efficient method of data analysis compared to manual coding or other methods of qualitative analysis. With the help of software tools, researchers can process large volumes of text data more quickly and accurately.
• Allows for in-depth analysis: Textual analysis enables researchers to conduct in-depth analysis of text data, uncovering patterns and themes that may not be visible through other methods of data analysis.
• Can provide rich insights: Textual analysis can provide rich and detailed insights into complex social phenomena. It can uncover subtle nuances in language use, reveal underlying meanings and discourses, and shed light on the ways in which social structures and power relations are constructed and maintained.

Limitations of Textual Analysis

While textual analysis can provide valuable insights into the ways in which language is used to convey meaning and create social and cultural identities, it also has several limitations. Some of these limitations include:

• Limited Scope : Textual analysis is only able to analyze the content of written or spoken language, and does not provide insights into non-verbal communication such as facial expressions or body language.
• Subjectivity: Textual analysis is subject to the biases and interpretations of the researcher, as well as the context in which the language was produced. Different researchers may interpret the same text in different ways, leading to inconsistencies in the findings.
• Time-consuming: Textual analysis can be a time-consuming process, particularly if the researcher is analyzing a large amount of text. This can be a limitation in situations where quick analysis is necessary.
• Lack of Generalizability: Textual analysis is often used in qualitative research, which means that its findings cannot be generalized to larger populations. This limits the ability to draw conclusions that are applicable to a wider range of contexts.
• Limited Accessibility: Textual analysis requires specialized skills and training, which may limit its accessibility to researchers who are not trained in this method.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Published: 08 May 2024

A meta-analysis on global change drivers and the risk of infectious disease

• Michael B. Mahon   ORCID: orcid.org/0000-0002-9436-2998 1 , 2   na1 ,
• Alexandra Sack 1 , 3   na1 ,
• O. Alejandro Aleuy 1 ,
• Carly Barbera 1 ,
• Ethan Brown   ORCID: orcid.org/0000-0003-0827-4906 1 ,
• Heather Buelow   ORCID: orcid.org/0000-0003-3535-4151 1 ,
• David J. Civitello 4 ,
• Jeremy M. Cohen   ORCID: orcid.org/0000-0001-9611-9150 5 ,
• Luz A. de Wit   ORCID: orcid.org/0000-0002-3045-4017 1 ,
• Meghan Forstchen 1 , 3 ,
• Fletcher W. Halliday 6 ,
• Patrick Heffernan 1 ,
• Sarah A. Knutie 7 ,
• Alexis Korotasz 1 ,
• Joanna G. Larson   ORCID: orcid.org/0000-0002-1401-7837 1 ,
• Samantha L. Rumschlag   ORCID: orcid.org/0000-0003-3125-8402 1 , 2 ,
• Emily Selland   ORCID: orcid.org/0000-0002-4527-297X 1 , 3 ,
• Alexander Shepack 1 ,
• Nitin Vincent   ORCID: orcid.org/0000-0002-8593-1116 1 &
• Jason R. Rohr   ORCID: orcid.org/0000-0001-8285-4912 1 , 2 , 3   na1  

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• Infectious diseases

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors 1 . Studies have shown that infectious disease risk is modified by changes to biodiversity 2 , 3 , 4 , 5 , 6 , climate change 7 , 8 , 9 , 10 , 11 , chemical pollution 12 , 13 , 14 , landscape transformations 15 , 16 , 17 , 18 , 19 , 20 and species introductions 21 . However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host–parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

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Measuring the shape of the biodiversity-disease relationship across systems reveals new findings and key gaps

Data availability.

All the data for this Article have been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 .

Code availability

All the code for this Article has been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 . R markdown is provided in Supplementary Data 1 .

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Acknowledgements

We thank C. Mitchell for contributing data on enemy release; L. Albert and B. Shayhorn for assisting with data collection; J. Gurevitch, M. Lajeunesse and G. Stewart for providing comments on an earlier version of this manuscript; and C. Carlson and two anonymous reviewers for improving this paper. This research was supported by grants from the National Science Foundation (DEB-2109293, DEB-2017785, DEB-1518681, IOS-1754868), National Institutes of Health (R01TW010286) and US Department of Agriculture (2021-38420-34065) to J.R.R.; a US Geological Survey Powell grant to J.R.R. and S.L.R.; University of Connecticut Start-up funds to S.A.K.; grants from the National Science Foundation (IOS-1755002) and National Institutes of Health (R01 AI150774) to D.J.C.; and an Ambizione grant (PZ00P3_202027) from the Swiss National Science Foundation to F.W.H. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

These authors contributed equally: Michael B. Mahon, Alexandra Sack, Jason R. Rohr

Authors and Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Alexandra Sack, O. Alejandro Aleuy, Carly Barbera, Ethan Brown, Heather Buelow, Luz A. de Wit, Meghan Forstchen, Patrick Heffernan, Alexis Korotasz, Joanna G. Larson, Samantha L. Rumschlag, Emily Selland, Alexander Shepack, Nitin Vincent & Jason R. Rohr

Environmental Change Initiative, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Samantha L. Rumschlag & Jason R. Rohr

Eck Institute of Global Health, University of Notre Dame, Notre Dame, IN, USA

Alexandra Sack, Meghan Forstchen, Emily Selland & Jason R. Rohr

Department of Biology, Emory University, Atlanta, GA, USA

David J. Civitello

Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

Jeremy M. Cohen

Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA

Fletcher W. Halliday

Department of Ecology and Evolutionary Biology, Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA

Sarah A. Knutie

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Contributions

J.R.R. conceptualized the study. All of the authors contributed to the methodology. All of the authors contributed to investigation. Visualization was performed by M.B.M. The initial study list and related information were compiled by D.J.C., J.M.C., F.W.H., S.A.K., S.L.R. and J.R.R. Data extraction was performed by M.B.M., A.S., O.A.A., C.B., E.B., H.B., L.A.d.W., M.F., P.H., A.K., J.G.L., E.S., A.S. and N.V. Data were checked for accuracy by M.B.M. and A.S. Analyses were performed by M.B.M. and J.R.R. Funding was acquired by D.J.C., J.R.R., S.A.K. and S.L.R. Project administration was done by J.R.R. J.R.R. supervised the study. J.R.R. and M.B.M. wrote the original draft. All of the authors reviewed and edited the manuscript. J.R.R. and M.B.M. responded to reviewers.

Corresponding author

Correspondence to Jason R. Rohr .

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Competing interests.

The authors declare no competing interests.

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Nature thanks Colin Carlson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 prisma flowchart..

The PRISMA flow diagram of the search and selection of studies included in this meta-analysis. Note that 77 studies came from the Halliday et al. 3 database on biodiversity change.

Extended Data Fig. 2 Summary of the number of studies (A-F) and parasite taxa (G-L) in the infectious disease database across ecological contexts.

The contexts are global change driver ( A , G ), parasite taxa ( B , H ), host taxa ( C , I ), experimental venue ( D , J ), study habitat ( E , K ), and human parasite status ( F , L ).

Extended Data Fig. 3 Summary of the number of effect sizes (A-I), studies (J-R), and parasite taxa (S-a) in the infectious disease database for various parasite and host contexts.

Shown are parasite type ( A , J , S ), host thermy ( B , K , T ), vector status ( C , L , U ), vector-borne status ( D , M , V ), parasite transmission ( E , N , W ), free living stages ( F , O , X ), host (e.g. disease, host growth, host survival) or parasite (e.g. parasite abundance, prevalence, fecundity) endpoint ( G , P , Y ), micro- vs macroparasite ( H , Q , Z ), and zoonotic status ( I , R , a ).

Extended Data Fig. 4 The effects of global change drivers and subsequent subcategories on disease responses with Log Response Ratio instead of Hedge’s g.

Here, Log Response Ratio shows similar trends to that of Hedge’s g presented in the main text. The displayed points represent the mean predicted values (with 95% confidence intervals) from a meta-analytical model with separate random intercepts for study. Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. See Table S 3 for pairwise comparison results. Effects of the five common global change drivers ( A ) have the same directionality, similar magnitude, and significance as those presented in Fig. 2 . Global change driver effects are significant when confidence intervals do not overlap with zero and explicitly tested with two-tailed t-test (indicated by asterisks; t 80.62  = 2.16, p = 0.034 for CP; t 71.42  = 2.10, p = 0.039 for CC; t 131.79  = −3.52, p < 0.001 for HLC; t 61.9  = 2.10, p = 0.040 for IS). The subcategories ( B ) also show similar patterns as those presented in Fig. 3 . Subcategories are significant when confidence intervals do not overlap with zero and were explicitly tested with two-tailed one sample t-test (t 30.52  = 2.17, p = 0.038 for CO 2 ; t 40.03  = 4.64, p < 0.001 for Enemy Release; t 47.45  = 2.18, p = 0.034 for Mean Temperature; t 110.81  = −4.05, p < 0.001 for Urbanization); all other subcategories have p > 0.20. Note that effect size and study numbers are lower here than in Figs. 3 and 4 , because log response ratios cannot be calculated for studies that provide coefficients (e.g., odds ratio) rather than raw data; as such, all observations within BC did not have associated RR values. Despite strong differences in sample size, patterns are consistent across effect sizes, and therefore, we can be confident that the results presented in the main text are not biased because of effect size selection.

Extended Data Fig. 5 Average standard errors of the effect sizes (A) and sample sizes per effect size (B) for each of the five global change drivers.

The displayed points represent the mean predicted values (with 95% confidence intervals) from the generalized linear mixed effects models with separate random intercepts for study (Gaussian distribution for standard error model, A ; Poisson distribution for sample size model, B ). Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. Sample sizes (number of studies, n, and effect sizes, k) for each driver are as follows: n = 77, k = 392 for BC; n = 124, k = 364 for CP; n = 202, k = 380 for CC; n = 517, k = 1449 for HLC; n = 96, k = 355 for IS.

Extended Data Fig. 6 Forest plots of effect sizes, associated variances, and relative weights (A), Funnel plots (B), and Egger’s Test plots (C) for each of the five global change drivers and leave-one-out publication bias analyses (D).

In panel A , points are the individual effect sizes (Hedge’s G), error bars are standard errors of the effect size, and size of the points is the relative weight of the observation in the model, with larger points representing observations with higher weight in the model. Sample sizes are provided for each effect size in the meta-analytic database. Effect sizes were plotted in a random order. Egger’s tests indicated significant asymmetries (p < 0.05) in Biodiversity Change (worst asymmetry – likely not bias, just real effect of positive relationship between diversity and disease), Climate Change – (weak asymmetry, again likely not bias, climate change generally increases disease), and Introduced Species (relatively weak asymmetry – unclear whether this is a bias, may be driven by some outliers). No significant asymmetries (p > 0.05) were found in Chemical Pollution and Habitat Loss/Change, suggesting negligible publication bias in reported disease responses across these global change drivers ( B , C ). Egger’s test included publication year as moderator but found no significant relationship between Hedge’s g and publication year (p > 0.05) implying no temporal bias in effect size magnitude or direction. In panel D , the horizontal red lines denote the grand mean and SE of Hedge’s g and (g = 0.1009, SE = 0.0338). Grey points and error bars indicate the Hedge’s g and SEs, respectively, using the leave-one-out method (grand mean is recalculated after a given study is removed from dataset). While the removal of certain studies resulted in values that differed from the grand mean, all estimated Hedge’s g values fell well within the standard error of the grand mean. This sensitivity analysis indicates that our results were robust to the iterative exclusion of individual studies.

Extended Data Fig. 7 The effects of habitat loss/change on disease depend on parasite taxa and land use conversion contexts.

A) Enemy type influences the magnitude of the effect of urbanization on disease: helminths, protists, and arthropods were all negatively associated with urbanization, whereas viruses were non-significantly positively associated with urbanization. B) Reference (control) land use type influences the magnitude of the effect of urbanization on disease: disease was reduced in urban settings compared to rural and peri-urban settings, whereas there were no differences in disease along urbanization gradients or between urban and natural settings. C) The effect of forest fragmentation depends on whether a large/continuous habitat patch is compared to a small patch or whether disease it is measured along an increasing fragmentation gradient (Z = −2.828, p = 0.005). Conversely, the effect of deforestation on disease does not depend on whether the habitat has been destroyed and allowed to regrow (e.g., clearcutting, second growth forests, etc.) or whether it has been replaced with agriculture (e.g., row crop, agroforestry, livestock grazing; Z = 1.809, p = 0.0705). The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included enemy type (A), reference land use type (B), or land use conversion type (C). Data for (A) and (B) were only those studies that were within the “urbanization” subcategory; data for (C) were only those studies that were within the “deforestation” and “forest fragmentation” subcategories. Sample sizes (number of studies, n, and effect sizes, k) in (A) for each enemy are n = 48, k = 98 for Virus; n = 193, k = 343 for Protist; n = 159, k = 490 for Helminth; n = 10, k = 24 for Fungi; n = 103, k = 223 for Bacteria; and n = 30, k = 73 for Arthropod. Sample sizes in (B) for each reference land use type are n = 391, k = 1073 for Rural; n = 29, k = 74 for Peri-urban; n = 33, k = 83 for Natural; and n = 24, k = 58 for Urban Gradient. Sample sizes in (C) for each land use conversion type are n = 7, k = 47 for Continuous Gradient; n = 16, k = 44 for High/Low Fragmentation; n = 11, k = 27 for Clearcut/Regrowth; and n = 21, k = 43 for Agriculture.

Extended Data Fig. 8 The effects of common global change drivers on mean infectious disease responses in the literature depends on whether the endpoint is the host or parasite; whether the parasite is a vector, is vector-borne, has a complex or direct life cycle, or is a macroparasite; whether the host is an ectotherm or endotherm; or the venue and habitat in which the study was conducted.

A ) Parasite endpoints. B ) Vector-borne status. C ) Parasite transmission route. D ) Parasite size. E ) Venue. F ) Habitat. G ) Host thermy. H ) Parasite type (ecto- or endoparasite). See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and the focal independent variable (whether the endpoint measured was a host or parasite, whether the parasite is vector-borne, has a complex or direct life cycle, is a macroparasite, whether the study was conducted in the field or lab, habitat, the host is ectothermic, or the parasite is an ectoparasite).

Extended Data Fig. 9 The effects of five common global change drivers on mean infectious disease responses in the literature only occasionally depend on location, host taxon, and parasite taxon.

A ) Continent in which the field study occurred. Lack of replication in chemical pollution precluded us from including South America, Australia, and Africa in this analysis. B ) Host taxa. C ) Enemy taxa. See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and continent, host taxon, and enemy taxon.

Extended Data Fig. 10 The effects of human vs. non-human endpoints for the zoonotic disease subset of database and wild vs. domesticated animal endpoints for the non-human animal subset of database are consistent across global change drivers.

(A) Zoonotic disease responses measured on human hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on non-human (animal) hosts (Z = 2.306, p = 0.021). Note, IS studies were removed because of missing cells. (B) Disease responses measured on domestic animal hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on wild animal hosts (Z = 2.636, p = 0.008). These results were consistent across global change drivers (i.e., no significant interaction between endpoint and global change driver). As many of the global change drivers increase zoonotic parasites in non-human animals and all parasites in wild animals, this may suggest that anthropogenic change might increase the occurrence of parasite spillover from animals to humans and thus also pandemic risk. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variable of global change driver and human/non-human hosts. Data for (A) were only those diseases that are considered “zoonotic”; data for (B) were only those endpoints that were measured on non-human animals. Sample sizes in (A) for zoonotic disease measured on human endpoints across global change drivers are n = 3, k = 17 for BC; n = 2, k = 6 for CP; n = 25, k = 39 for CC; and n = 175, k = 331 for HLC. Sample sizes in (A) for zoonotic disease measured on non-human endpoints across global change drivers are n = 25, k = 52 for BC; n = 2, k = 3 for CP; n = 18, k = 29 for CC; n = 126, k = 289 for HLC. Sample sizes in (B) for wild animal endpoints across global change drivers are n = 28, k = 69 for BC; n = 21, k = 44 for CP; n = 50, k = 89 for CC; n = 121, k = 360 for HLC; and n = 29, k = 45 for IS. Sample sizes in (B) for domesticated animal endpoints across global change drivers are n = 2, k = 4 for BC; n = 4, k = 11 for CP; n = 7, k = 20 for CC; n = 78, k = 197 for HLC; and n = 1, k = 2 for IS.

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ChatGPT in higher education - a synthesis of the literature and a future research agenda

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• Published: 02 May 2024

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• Pritpal Singh Bhullar 1 ,
• Mahesh Joshi 2 &
• Ritesh Chugh   ORCID: orcid.org/0000-0003-0061-7206 3  

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ChatGPT has emerged as a significant subject of research and exploration, casting a critical spotlight on teaching and learning practices in the higher education domain. This study examines the most influential articles, leading journals, and productive countries concerning citations and publications related to ChatGPT in higher education, while also shedding light on emerging thematic and geographic clusters within research on ChatGPT’s role and challenges in teaching and learning at higher education institutions. Forty-seven research papers from the Scopus database were shortlisted for bibliometric analysis. The findings indicate that the use of ChatGPT in higher education, particularly issues of academic integrity and research, has been studied extensively by scholars in the United States, who have produced the largest volume of publications, alongside the highest number of citations. This study uncovers four distinct thematic clusters (academic integrity, learning environment, student engagement, and scholarly research) and highlights the predominant areas of focus in research related to ChatGPT in higher education, including student examinations, academic integrity, student learning, and field-specific research, through a country-based bibliographic analysis. Plagiarism is a significant concern in the use of ChatGPT, which may reduce students’ ability to produce imaginative, inventive, and original material. This study offers valuable insights into the current state of ChatGPT in higher education literature, providing essential guidance for scholars, researchers, and policymakers.

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1 Introduction

ChatGPT, or Chat Generative Pre-trained Transformer, is a popular generative Artificial Intelligence (AI) chatbot developed by OpenAI, employing natural language processing to deliver interactive human-like conversational experiences (Jeon et al., 2023 ; Angelis et al., 2023 ). ChatGPT utilises a pre-trained language learning model, derived from an extensive big-data corpus, to predict outcomes based on a given prompt (Crawford et al., 2023 ; Geerling et al., 2023 ; Li et al., 2023 ). Since its inception, ChatGPT has attracted widespread attention and popularity and has the potential to disrupt the education sector (Rana, 2023 ). According to a research survey of adults conducted by the Pew Research Centre, approximately 60% of adults in the United States and 78% of adults in Asia possess knowledge of ChatGPT; furthermore, men are more familiar with ChatGPT than women (Vogels, 2023 ). The study also found that among ethnic groups globally, individuals of Asian descent have the highest level of familiarity with AI-based large language models (LLMs).

People have found value in using ChatGPT for a wide range of purposes, including generating creative content, answering questions, providing explanations, offering suggestions, and even having casual conversations (Crawford et al., 2023 ; Throp, 2023 ; Wu et al., 2023 ). Furthermore, ChatGPT is an effective digital assistant for facilitating a thorough understanding of diverse and intricate subjects using simple and accessible language. Given these features, ChatGPT has the potential to bring about a paradigm shift in traditional methods of delivering instruction and revolutionise the future of education (Tlili et al., 2023 ). ChatGPT stands out as a promising tool for open education, enhancing the independence and autonomy of autodidactic learners through personalised support, guidance, and feedback, potentially fostering increased motivation and engagement (Firat, 2023 ). Its capabilities encompass facilitating complex learning, asynchronous communication, feedback provision, and cognitive offloading (Memarian & Doleck, 2023 ).

However, the rapid expansion of ChatGPT has also aroused apprehensions in the academic world, particularly after reports surfaced that the New York Department of Education had unexpectedly imposed a ban on access to the tool due to concerns about academic integrity violations (Sun et al., 2023 ; Neumann et al., 2023 ; Crawford et al., 2023 ). Students who use ChatGPT to produce superior written assignments may have an unfair advantage over peers who lack access (Farrokhnia et al., 2023 ; Cotton et al., 2023 ). Ethical concerns about the deployment of LLMs include the potential for bias, effects on employment, misuse and unethical deployment, and loss of integrity. However, there has been little research on the potential dangers that a sophisticated chatbot such as ChatGPT poses in the realm of higher education, particularly through the lens of a systematic literature review and bibliometric techniques.

In this light, this paper explores the literature on the application of ChatGPT in higher education institutions and the obstacles encountered in various disciplines from the perspectives of both faculty and students. The paper aims to analyse the current state of the field by addressing the following overarching research questions using bibliographic coupling, co-occurrence analysis, citation analysis, and co-authorship analysis:

What are the most influential articles in terms of citations in research related to ChatGPT in education?

What are the top journals and countries in terms of publication productivity related to the implications of ChatGPT in higher education institutions?

What are the emerging thematic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions?

What are the geographic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions?

2 Methodology

In conducting this study, publications on the impact of ChatGPT on various aspects of higher education institutions were systematically identified through an extensive search using Elsevier’s Scopus database, a comprehensive repository hosting over 20,000 globally ranked, peer-reviewed journals (Mishra et al., 2017 ; Palomo et al., 2017 ; Vijaya & Mathur, 2023 ). Scopus is a widely used database for bibliometric analyses and is considered one of the “largest curated databases covering scientific journals” (pg. 5116) in different subject areas (Singh et al., 2021 ). Widely acclaimed for its comprehensive coverage, Scopus has been extensively employed in bibliometric analyses across diverse disciplines, as evidenced by studies in capital structure theories, business research, entrepreneurial orientation and blockchain security (Bajaj et al., 2020 ; Donthu et al., 2020 ; Gupta et al., 2021 ; Patrício & Ferreira, 2020 ). Notably, despite the “extremely high” correlation between the Web of Science and Scopus databases, Scopus’s status as a superior and versatile data source for literature extraction is reinforced by its broader coverage of subject areas and categories compared to the narrower journal scope of Web of Science, facilitating scholars in locating literature most pertinent to the review area (Archambault et al., 2009 ; Paul et al., 2021 ). To ensure a systematic literature review, we adhered to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines (Page et al., 2021 ) for the search, identification, selection, reading, and data extraction from the articles retrieved through the Scopus database (Fig.  1 ). Reliance on a single database is acceptable within the PRISMA framework (Moher et al., 2009 ).

Employing Boolean-assisted search queries, we aimed to capture a comprehensive range of topics related to ChatGPT’s impact on higher education institutions. Specific search queries were carefully selected to ensure a broad yet relevant search scope and included the following:

“ChatGPT and Teaching learning in universities” OR “Effect of ChatGPT in higher education institution” OR “ChatGPT and student assessment in higher education” OR “ChatGPT and academic integrity” OR “ChatGPT and teaching pedagogy in higher education institution” OR “ChatGPT and cheating student course assignment” OR “ChatGPT and teaching in higher education” OR “Implications of ChatGPT in higher education institutions” OR “ChatGPT and evaluation criteria in higher education institution” OR “ChatGPT in universities” OR “ChatGPT and student learnings. ”

The study includes papers published and included in the Scopus database on or before May 26, 2023 on the theme of ChatGPT and higher education. This timeframe was chosen to encompass the most recent and relevant literature available up to the point of data retrieval. Papers identified through the search queries underwent inclusion or exclusion based on predetermined criteria. Specifically, only papers published in journals were considered for this study, as these undergo a peer-review process and are subject to stringent selection criteria set by the journals, ensuring their quality and reliability. Papers in conference proceedings were excluded from the start of the search. Only papers written in English were included to maintain consistency and clarity, whereas others were excluded. Of the 48 research papers that were initially identified, 47 were ultimately selected for the bibliometric analysis, which was conducted using VOSviewer, a bibliometric analysis tool.

PRISMA Flowchart

From the identified pool of 47 articles, the analysis uncovered a nuanced distribution of research methodologies. Specifically, 11 studies were grounded in quantitative research methodologies, underscoring a quantitative focus within the literature. In contrast, a substantial majority of 31 articles embraced a qualitative framework, showcasing a diverse spectrum that included pure qualitative research, editorials, letters to the editor, and opinion pieces. Furthermore, the review brought to light four literature reviews, signifying a synthesis of existing knowledge, and identified one study that strategically employed a mixed-methods approach, blending both qualitative and quantitative research techniques.

To address the research questions, the selected publications underwent analysis using various bibliometric techniques. For the first and second research questions, citation analysis was employed. For the third and fourth research questions, bibliographic analysis was performed in VOSviewer software to generate clusters.

3 Findings and discussion

3.1 publication trend.

Information from the Scopus database indicates that academics began focusing on investigating various aspects of ChatGPT’s potential in higher education in 2022, as they published their findings in 2023. All academic articles in reputable publications in the Scopus database were published in 2023.

3.2 Citation analysis

Table  1 presents the top ten articles according to the number of citations. The number of articles increased significantly in 2023, consistent with the emerging nature and growing relevance of the topic. Exploring the ramifications of ChatGPT in higher education is a recent focal point for scholars, with numerous aspects warranting deeper investigation. The limited citation count, as anticipated, underscores that publications from 2023 are in the early stages of gaining visibility and recognition within the academic community.

The article by Thorp ( 2023 ), entitled “ChatGPT is fun, but not an author”, has received the highest number of citations (79). Thorp stresses the risks associated with implementing ChatGPT in the classroom. Although ChatGPT is an innovative AI tool, significant barriers remain to its implementation in the field of education. According to Thorp, using ChatGPT in academic writing is still inefficient. Thorp also expresses concerns about the rising prevalence of ChatGPT in the fabrication of scientific publications. The second most-cited work, “How Does ChatGPT Perform on the United States Medical Licensing Examination?” by Gilson and colleagues, has received 27 citations. Gilson et al. ( 2023 ) evaluated the accuracy, speed and clarity of ChatGPT’s responses to questions on the United States Medical Licensing Examination’s Step 1 and Step 2 tests. The text responses generated by ChatGPT were evaluated using three qualitative metrics: the logical justification of the chosen answer, the inclusion of information relevant to the question, and the inclusion of information extraneous to the question. The model attained a level of proficiency comparable to that of a third-year medical student. The study demonstrates the potential utility of ChatGPT as an interactive educational resource in the field of medicine to facilitate the acquisition of knowledge and skills. Third is Kasneci et al.’s article “ChatGPT for good? On opportunities and challenges of large language models for education”, with 13 citations. This paper examines the benefits and drawbacks of using language models in the classroom from the perspectives of both teachers and students. The authors find that these comprehensive language models can serve as a supplement rather than a replacement for classroom instruction. Each of the remaining top-ten articles mentioned the impact of ChatGPT on academic integrity in education and had received fewer than ten citations at the time of analysis.

Table  2 presents the top 10 journals in terms of the number of citations of publications related to the topic of ChatGPT in higher education. The journal Science , which published “ChatGPT is fun, but not an author,” was deemed most influential because it received the highest number of citations (79). JMIR Medical Education has published two articles that have been cited by 30 other research articles on the same topic. Journal of University Teaching and Learning Practise has published the most articles: three. Innovations in Education and Teaching International has published two articles on this topic, which together have been cited by six articles.

As shown in Table  3 , the majority of research articles pertaining to ChatGPT and higher education have originated from countries in Asia. Six of the top 10 countries for publishing articles on this topic are located in the Asian continent. However, the most influential studies in terms of citations have been produced by the United States, Germany, Australia, and the United Kingdom. Combined, these countries have received a total of 63 citations, with individual counts of 36, 17, 7, and 7, respectively. These four countries have 90% of the total citations of the top 10 most productive countries in the field of research on higher education perspectives on ChatGPT.

3.3 Bibliographic coupling

3.3.1 thematic clusters.

Four thematic clusters (TCs) were identified from the included research articles, as shown in Table  4 . VOSviewer was used to perform clustering based on bibliographic coupling. This method identifies relations between documents by examining publications that cite the same sources (Boyack & Klavans, 2010 ). VOSviewer clusters articles with a common knowledge base, assigning each publication to exactly one cluster. To implement this clustering technique, we assessed the co-occurrence of bibliographic references among articles within our dataset. Co-occurrence was determined by identifying shared references between articles, indicating a thematic connection (Boyack & Klavans, 2010 ). Articles sharing common references were considered to co-occur, enabling us to quantify the extent of thematic relationships based on the frequency of shared references. We identified and categorised thematic clusters within our dataset through the combined approach of VOSviewer clustering and co-occurrence analysis. This method typically results in a distribution of clusters, with a limited number of larger clusters and a more substantial number of smaller clusters.

The clusters were derived through an analysis of subordinate articles extracted from the Scopus database. VOSviewer systematically organised similar articles into distinct clusters based on the shared patterns of bibliographic references (Van Eck & Waltman, 2010 ). To ensure methodological transparency and robustness, we established clear criteria and parameters for clustering. Specifically, keywords with a minimum frequency ( n  = 5) were included in the analysis, and co-occurrence was calculated based on a pairwise comparison method. This systematic approach ensured the meaningful representation of thematic relationships within the dataset, guided by insights from previous literature (Jarneving, 2007 ). Using cluster analysis techniques, the articles were organised into cohesive groups characterised by the degree of thematic homogeneity guided by the nature of the research findings. This approach ensured a robust representation of the underlying thematic structure (Jarneving, 2007 ).

Furthermore, to mitigate the risk of subjective bias in thematic categorisation, a counter-coding approach was employed. A second researcher independently categorised thematic clusters identified by VOSviewer to assess inter-rater agreement. The level of agreement between the two researchers was assessed using Cohen’s kappa coefficient, ensuring the reliability and validity of the thematic classification process. The resulting kappa coefficient (0.69) indicated substantial agreement, suggesting a high level of agreement beyond what would be expected by chance alone (Gisev et al., 2013 ). Furthermore, the nomenclature assigned to each cluster was finalised based on the predominant research theme emerging from the analysis, providing a concise and informative label for each group.

TC1: ChatGPT and Academic Integrity: Cotton et al. ( 2023 ) describe ChatGPT as a double-edged sword that potentially threatens academic integrity. AI essay writing systems are programmed to churn out essays based on specific guidelines or prompts, and it can be difficult to distinguish between human and machine-generated writing. Thus, students could potentially use these systems to cheat by submitting essays that are not their original work (Dehouche, 2021 ). Kasneci et al. ( 2023 ) argue that effective pedagogical practices must be developed in order to implement large language models in classrooms. These skills include not only a deep understanding of the technology but also an appreciation of its constraints and the vulnerability of complex systems in general. In addition, educational institutions need to develop a clearly articulated plan for the successful integration and optimal use of big language models in educational contexts and teaching curricula. In addition, students need to be taught how to verify information through a teaching strategy emphasising critical thinking effectively. Possible bias in the generated output, the need for continuous human supervision, and the likelihood of unforeseen effects are just a few of the challenges that come with the employment of AI systems. Continuous monitoring and transparency are necessary to ensure academic integrity while using ChatGPT. Lim et al. ( 2023 ) report that ChatGPT poses academic integrity challenges for the faculty of higher education institutions, who must verify whether academic work (assignments, research reports, etc.) submitted by students is derived from the fresh perspective of data analysis or plagiarised and recycled (copying and pasting original work) by ChatGPT. ChatGPT may threaten student learning and classroom engagement if students have access to information and course assignments without assessing their integrity. Perkins ( 2023 ) also expresses concerns regarding academic integrity in the use of ChatGPT. Students are utilising ChatGPT to complete their course assignments without attribution rather than producing original work. Higher education institutions must establish clear boundaries regarding academic integrity and plagiarism in light of the growing utilisation of AI tools in academic and research settings. In addition, the challenges posed by AI essay writing systems like ChatGPT necessitate a multifaceted approach to safeguard academic integrity. Educational institutions should invest in comprehensive educational programs that not only teach students the ethical use of technology but also incorporate rigorous assessments of critical thinking skills. Additionally, integrating AI literacy into the curriculum, with a focus on understanding the limitations and potential biases of big language models, can empower students to discern between human and machine-generated content.

TC2: ChatGPT and Learning Environment: According to Crawford et al. ( 2023 ), increased stress levels and peer pressure among university students have created a favourable environment for the use of AI tools. ChatGPT provides enhanced educational opportunities for college-level students. It can help students identify areas they may have overlooked, offer guidance on additional reading materials, and enhance existing peer and teacher connections. In addition, ChatGPT can propose alternative methods of evaluating students beyond conventional assignments. Crawford et al. ( 2023 ) recommend providing practical assignments incorporating ChatGPT as a supplementary tool to reduce plagiarism. Su ( 2023 ) documents that ChatGPT can provide students with a personalised learning experience based on their specific needs. In addition, the ChatGPT platform can be used to create a virtual coaching system that offers prompt feedback to educators during their classroom evaluations. This approach fosters critical thinking and supports early childhood educators in refining their teaching methodologies to optimise interactive learning outcomes for students. Tang ( 2023b ) proposes that bolstering research integrity can be achieved by imposing restrictions on the utilisation of NLP-generated content in research papers. Additionally, the author advocates for transparency from researchers, emphasising the importance of explicitly stating the proportion of NLP-generated content incorporated in their papers. This recommendation prompts a critical examination of the role of AI-generated content in scholarly work, emphasising the importance of nurturing independent research and writing skills for both students and researchers.

TC3: ChatGPT and Student Engagement: Lee ( 2023 ) examines the ability of ChatGPT to provide an interactive learning experience and boost student engagement beyond textbook pedagogy. Iskender ( 2023 ) explains that ChatGPT provides a mechanism for students to generate and investigate diverse concepts expeditiously, thereby helping them engage in imaginative and evaluative thinking on specific subject matter. This approach has the potential to optimise time management for students and allow them to concentrate on more advanced cognitive activities. AI tools such as ChatGPT can potentially enhance the personalisation of learning materials by providing visual aids and summaries that can aid the learning process and significantly improve students’ competencies. Hence, leveraging ChatGPT in education can revolutionise learning by facilitating interactive experiences, nurturing imaginative thinking, and optimising time management for students.

TC4: ChatGPT and Scholarly Research: Ivanov and Soliman ( 2023 ) and Yan ( 2023 ) focus on the practical applications and implications of LLMs like ChatGPT in educational settings and scholarly research within the context of language learning, writing, and tourism. Yan’s investigation into ChatGPT’s application in second-language writing examines its effectiveness in addressing specific writing tasks at the undergraduate level. The findings underscore the nuanced balance between the strengths of ChatGPT and the inherent limitations in handling demanding academic writing tasks. Nevertheless, ChatGPT is also labelled as an ‘all-in-one’ solution for scholarly research and writing (Yan, 2023 ). In parallel, Ivanov and Soliman ( 2023 ) highlight that ChatGPT can assist scholars in the field of tourism research by composing preliminary literature reviews, substantiating their chosen methodologies, and creating visual aids such as tables and charts. Furthermore, the researchers outline that ChatGPT could provide valuable methodological ideas and insights by helping researchers generate questions and corresponding scales for inclusion in questionnaires. Hence, ChatGPT has the potential to become a valuable ally as a facilitator in academic writing processes and has the potential to transform the research workflow.

3.3.2 Geographic clusters

The results of the country-based bibliographic analysis are summarised in Table  5 . The present study utilised the prevailing research theme in the existing literature as a framework for categorising the countries into four distinct clusters on the basis of the number of documents published from different countries.

Cluster 1: Implications of ChatGPT for Student Examinations and Education : Cluster 1 is composed of five countries: Germany, Ireland, South Korea, Taiwan, and the United States. Researchers in these countries have emphasised the potential role of ChatGPT in higher education within the context of AI language models. Eleven research articles related to this theme were published by researchers based in the United States, the most in this cluster. The top three articles in Table  1 are from the United States. The study entitled “Opportunities and Challenges of Large Language Models for Education,” was authored by German researchers (Kasneci et al., 2023 ) and has been widely cited in the academic community (13 citations). The remaining studies were conducted by researchers from South Korea and Taiwan and focused on the impact of ChatGPT on the education sector and its associated opportunities and challenges. This cluster demonstrates that students could benefit greatly from using ChatGPT in performing various academic tasks, such as reviewing and revising their work, verifying the accuracy of homework answers, and improving the quality of their essays. It has also aided postgraduates whose first language is not English improve their writing, as ChatGPT can be instructed to rewrite a paragraph in a scholarly tone from scratch. The outcomes have demonstrated significant efficacy, thereby alleviating the cognitive load associated with translation for these students, enabling them to concentrate on the substance of their writing rather than the intricacies of composing in an unfamiliar language. To harness the potential benefits, future research could focus on developing targeted training programs for students and educators that emphasise the effective utilisation of ChatGPT to enhance not only academic tasks but also language proficiency for non-native English speakers, addressing both cognitive load and language intricacies.

Cluster 2: ChatGPT and Academic Integrity : Cluster 2 comprises research studies conducted by authors from Japan, Bangladesh, Hong Kong, Nigeria, Pakistan, UAE, the UK, Vietnam and the Netherlands. The most influential study in this cluster, “Unlocking the power of ChatGPT: A framework for applying Generative AI in education”, was authored by researchers from Hong Kong (Su & Yang, 2023 ). They document that ChatGPT can be used to respond to student inquiries, reducing the time and effort required of educators and allowing them to focus their resources on other activities, such as scholarly investigations. Farrokhnia et al. ( 2023 ) and Yeadon et al. ( 2023 ) state that ChatGPT can write scientific abstracts with fabricated data and essays that can evade detection by reviewers. According to Liebrenz et al. ( 2023 ), ChatGPT tends to produce erroneous and incoherent responses, thereby raising the potential for disseminating inaccurate information in scholarly literature. The higher-order cognitive abilities of ChatGPT are relatively low, especially in areas related to creativity, critical thinking, reasoning, and problem-solving. ChatGPT could reduce students’ motivation to explore topics independently, draw their own conclusions, and solve problems independently (Kasneci et al., 2023 ). Ibrahim et al. ( 2023 ) find that ChatGPT can engage students in their academic pursuits. ChatGPT can enhance the writing abilities of non-native English speakers to allow them to concentrate on higher-order cognitive processes. This technological development allows faculty members to allocate more attention to conceptualisation and writing rather than focusing on the mechanics of grammar and spelling. However, there is a debate among intellectuals regarding the implications of AI for content creation, with some asserting that it detracts from innovative content development. The possibility that ChatGPT threatens academic honesty by facilitating essay plagiarism is being acknowledged. In addition, in the absence of appropriate citations, this textual content may violate copyright regulations. Cotton et al. ( 2023 ) express concerns about the potential impact of ChatGPT on academic integrity and plagiarism. Their work corroborates Dehouche’s ( 2021 ) assertion that students may use ChatGPT to engage in academic dishonesty by submitting essays that are not their original work. According to Cotton et al. ( 2023 ), ChatGPT users have a competitive advantage over non-users and can achieve higher grades on their coursework assignments by utilising the AI-based language tool. They classify ChatGPT as a versatile instrument with the potential to pose a threat to academic integrity, noting that AI essay writing systems are specifically programmed to generate content based on specific parameters or prompts, thereby challenging the discernment between human-authored and machine-generated content. Distinguishing between the academic work produced by students and the content of ChatGPT when evaluating assignments is a significant challenge for faculty. It is recommended that academic staff continually monitor student assignments for academic misconduct infractions, coupled with transparent communication about the potential risks associated with AI-generated content.

Cluster 3: ChatGPT and Students’ Learning : Cluster 3 comprises Malaysia, China and Australia. This cluster mainly includes studies of the role of AI-based models in student learning. Researchers from Australia (Crawford et al., 2023 ; Lim et al., 2023 ; Lawrie, 2023 ; Li et al., 2023 ; Seth et al., 2023 ; Cingillioglu, 2023 ; Skavronskaya, 2023 ; and Johinke, 2023 ) have contributed the most (8 studies) to this cluster and put their weight behind the role of AI and student learning in various disciplines. One of the most influential papers, “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators”, was authored by researchers from both Australia and Malaysia (Lim et al., 2023 ) and reflected on the role of AI in classroom learning and teaching. Rather than banning AI tools, the authors advocate for the productive use of these tools in classrooms to facilitate more engaging student learning. Another Australian study titled, “Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI)” (Crawford et al., 2023 ) highlights AI as an alternative path of learning for students. ChatGPT can promptly evaluate students’ assignments and help them identify areas of weakness. Educators have the option to provide innovative assessments to their students instead of adhering solely to conventional assessments. ChatGPT can augment pedagogical approaches, evaluation structures, and the comprehensive educational milieu by reinforcing the trilateral association among instructors, learners, and technology. The implementation of ChatGPT can provide students with a personalised and interactive learning and research experience facilitated by virtual tutors and customised recommendations. In light of the research in this cluster, the integration of ChatGPT into education should inspire a paradigm shift towards a more dynamic and personalised learning environment. Institutions can explore strategic partnerships with AI researchers to develop context-specific applications of ChatGPT that cater to diverse educational needs, promoting a symbiotic relationship between human instructors, students, and technology for an enriched learning experience.

Cluster 4: ChatGPT and Field-specific Research : This cluster includes research by authors in Asian and European countries (India, Oman, Bulgaria and New Zealand) that has emphasised the potential role of ChatGPT in the medical and tourism industries. Authors from India explored the role of ChatGPT in the medical field (Seetharaman, 2023 ; Subramani et al., 2023 ). Seetharaman ( 2023 ) reports that ChatGPT offers supplementary language assistance to students who are not proficient in English, enabling them to enhance their language proficiency and effectively communicate in English, the principal language of instruction in medical establishments. The ChatGPT platform has the potential to serve as a tool for medical students to replicate patient interactions in a simulated environment, such as accurately obtaining medical histories and documenting symptoms. According to Subramani et al. ( 2023 ), ChatGPT is a highly efficient and user-friendly AI technology that can aid healthcare professionals in various aspects, such as diagnosis, critical decision-making, and devising appropriate treatment plans. ChatGPT has demonstrated impressive performance on medical exams, indicating its potential as a valuable resource for enhancing medical education and assessment (Subramani et al., 2023 ) and can support interdisciplinarity in tourism research (Nautiyal et al., 2023 ). Ivanov and Soliman ( 2023 ) note the potential of ChatGPT to serve as a digital instructor to provide students with enhanced and effective learning experiences and outcomes. Digital instructors can impart knowledge in diverse languages and thus can be used to educate individuals of varying nationalities and backgrounds in the field of tourism. Furthermore, LLM-based chatbots, including ChatGPT, can assess written assignments and provide direction on linguistic proficiency, syntax, and composition, ultimately enhancing students’ scholarly writing proficiency. In exploring the intersection of ChatGPT with medical education, institutions can pioneer innovative approaches by using the platform to create immersive, simulated patient interactions that go beyond language assistance, allowing medical students to practice nuanced skills such as medical history gathering and symptom documentation. Simultaneously, leveraging ChatGPT as a versatile digital instructor offers a unique opportunity to provide cross-cultural and multilingual education, contributing to a more inclusive and globally competent workforce within the tourism industry.

3.4 Challenges of ChatGPT in higher education

In addition to some previously mentioned challenges, such as the potential for plagiarism, the investigation also identified other key challenges in implementing ChatGPT within the context of higher education’s teaching and learning environment. Wu and Yu ( 2023 ) found that the benefits of AI-based ChatGPT are more in higher education as compared to primary and secondary education. The study also reported that the novelty effects of AI chatbots may enhance learning outcomes in brief interventions, but their efficacy diminishes in longer interventions.

First, the implementation of ChatGPT within the educational context engenders learning impediments. In the absence of adequate monitoring and regulation, the technology could lead to human unintelligence and unlearning, but teachers will become more adaptive and create authentic assessments to enhance student learning (Alafnan et al., 2023 ; Lawrie, 2023 ). Second, the technology could be used in a manner that violates students’ privacy. If the model is not adequately secured, it could surreptitiously gather confidential data from students without their explicit awareness or authorisation (Kanseci, 2023). Third, the technology could facilitate discrimination against particular students. If the model is not trained on a dataset that accurately represents the entire student population, it has the potential to create disparities in educational access (Cingillioglu, 2023 ; Lin et al., 2023 ). Fourth, according to Ivanov and Soloman (2023), ChatGPT lacks access to real-time data. Therefore, its responses may be inconsequential, inaccurate, or outdated. The information provided in response to a specific query may also be insufficient. Gao et al. (2022) highlight the need for further investigation of the precision and scholarly authenticity of ChatGPT. Fifth, it may be difficult for ChatGPT to comprehend the context and subtleties of complex academic subjects and answer complex questions (Adetayo, 2023 ; Eysenbach, 2023 ; Neumann et al., 2023 ). The system can misinterpret inquiries, offer inadequate or inaccurate responses, or struggle to comprehend the fundamental purpose behind questions (Clark, 2023 ). In particular, ChatGPT may not have the requisite expertise in highly specialised or advanced subjects such as advanced mathematics or specific sciences. Hence, it may not deliver precise and accurate answers (Neumann et al., 2023 ; Fergus et al., 2023 ). Karaali ( 2023 ) claimed that the primary emphasis in the field of AI is currently directed towards the enhancement of advanced cognitive abilities and mental processes associated with quantitative literacy and quantitative reasoning. However, it is important to acknowledge that fundamental skills such as writing, critical thinking, and numeracy continue to serve as essential foundational components among students. Although AI is making significant progress in fundamental domains, it appears that students are experiencing a decline in performance in the context of fundamental skills. Consequently, NLP-based adaptive learner support and education require further investigation (Bauer et al., 2023 ).

In addressing the challenges of ChatGPT in education, educators need to adapt and develop authentic assessments that mitigate the risk of human unlearning, ensuring that technology enhances, rather than hinders, student learning experiences. Simultaneously, recognising the limitations of ChatGPT in comprehending the nuances of highly specialised subjects underscores the importance of balancing advancements in AI’s cognitive abilities with continued emphasis on fundamental skills like critical thinking, writing, and numeracy, urging a reevaluation of priorities in AI-driven educational research towards comprehensive learner support.

4 Conclusion, implications and agenda for future research

This study identified the most influential articles and top journals and countries in terms of citations and publication productivity related to ChatGPT in higher education, as well as highlighted emerging thematic clusters and geographic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions. Articles on the topic of ChatGPT in higher education published up to May 2023 were identified by searching the Scopus database. Given the emergent nature of ChatGPT starting in late 2022, all the included articles were published in 2023. Thus, this specific research domain remains relatively unexplored. The findings of this analysis reveal that the United States is the most productive country in terms of research on the role of ChatGPT in higher education, especially relating to academic integrity and research. US researchers also emerged as the most influential in terms of number of citations in the literature. Our findings corroborate those of previous research (Crompton & Burke, 2023 ). However, 60% of the articles in our shortlisted literature emanated from Asian countries.

Four thematic clusters (academic integrity, student engagement, learning environment and research) were identified. Furthermore, the country-based bibliographic analysis revealed that research has focused on student examinations, academic integrity, student learning and field-specific research in medical and tourism education (Nautiyal et al., 2023 ; Subramani et al., 2023 ). Plagiarism is recognised as a major challenge that hinders students’ creativity, innovativeness and originality when using ChatGPT in their academic pursuits. To mitigate the potential drawbacks of using ChatGPT in educational and research settings, proactive measures should be taken to educate students and researchers alike on the nature of plagiarism, its negative impacts and academic integrity (Shoufan, 2023 ; Teixeira, 2023 ) Educators may ask students to provide a written acknowledgement of the authenticity of their assignments and their non-reliance on ChatGPT. Such an acknowledgement would discourage students from utilising ChatGPT in their academic and research endeavours and establish accountability for their academic pursuits. In addition, educators should develop authentic assessments that are ChatGPT-proof.

ChatGPT lacks emotional intelligence and empathy, both of which are crucial in effectively addressing the emotional and psychological dimensions of the learning process (Farrokhnia et al., 2023 ; Neumann et al., 2023 ). Higher education institutions may encounter challenges in using ChatGPT to deliver suitable assistance, comprehension, or direction to students needing emotional or mental health support. The significance of human interaction in learning cannot be overstated. Achieving a balance between using AI and the advantages of human guidance and mentorship is a persistent challenge that requires attention (Neumann et al., 2023 ; Rahman et al., 2023 ). Strzelecki ( 2023 ) observed in his research that behavioural intention and personal innovativeness are the two major determinants behind the adoption of ChatGPT among students.

4.1 Implications

The findings of the present study have numerous important implications. This study provides insight into the current state of ChatGPT in higher education and thus can serve as valuable guidance for academics, practitioners, and policymakers. The study’s findings contribute to the literature by providing new insights into the role of ChatGPT and strategies for mitigating its negative aspects and emphasising its positive attributes.

First, the implementation of AI in education can improve academic performance and student motivation, particularly by facilitating personalised learning. Educational institutions should monitor and regulate students’ use of such technologies proactively. Higher education institutions also ought to prioritise the training of their educators in effectively utilising AI technologies, including ChatGPT. Concurrently, it is imperative for these institutions to equip students with comprehensive academic integrity training, shedding light on the appropriate and inappropriate applications of AI tools like ChatGPT. This includes creating awareness about the potential consequences of utilising these technologies for dishonest practices. Furthermore, educational establishments need to urgently revisit and refine their academic integrity policies to address the evolving landscape shaped by the integration of artificial intelligence tools in various academic facets. This proactive approach will foster a learning environment that embraces technological advancements and upholds the principles of honesty and responsible use. Institutional regulations on accountability and transparency should guide the frameworks that govern the use of AI in the campus environment (Pechenkina, 2023 ; Sun & Hoelscher, 2023 ; Dencik & Sanchez-Monedero, 2022 ).

Second, faculty members must proactively replace traditional coursework with modern alternatives that foster elevated levels of critical thinking among students, as suggested by Zhai ( 2022 ). Educators and learners can augment the academic material produced by ChatGPT with their own insights and information obtained from credible scholarly resources (Emenike & Emenike, 2023 ).

Third, ChatGPT should not be considered a threat to the education sector but a supplementary tool for human instruction that can enhance teaching and learning. It is imperative to acknowledge that the vital role of human educators cannot be replaced (Karaali, 2023 ) Moreover, ChatGPT can potentially enhance the accessibility and inclusivity of higher education. Alternative formats, linguistic support, and individualised explanations can help students who are studying English as a second language, are not native English speakers, or have other unique learning needs. Furthermore, Alnaqbi and Fouda ( 2023 ) highlight the implications of AI in evaluating the teaching style of faculty in higher education by collecting the feedback of students through social media and ChatGPT.

Fourth, the faculty in higher education institutions could address ethical concerns by providing students with explicit and comprehensive guidelines about the prescribed structure of academic assignments (Cotton et al., 2023 ; Gardner & Giordano, 2023 ). This practice can facilitate the production of more cohesive assignments. In addition, teachers can use rubrics to assess assignments and blend automated and manual assessment methodologies to evaluate students’ comprehension of the subject matter (Cotton et al., 2023 ; Shoufan, 2023 ).

In summary, using ChatGPT is recommended for enhancing creativity, refining writing proficiency, and improving research abilities. Nonetheless, it is crucial to emphasise that ChatGPT should not be employed as a substitute for critical thinking and producing original work. While it serves as a valuable tool for augmentation, upholding the integrity of independent thought and authentic content creation in academic endeavours is essential.

4.2 Limitations

The present study acknowledges several limitations. Firstly, the reliance on Scopus as the primary data source for bibliometric analysis may have limitations in capturing the full landscape of relevant literature. Future research may consider incorporating additional databases like Web of Science to ensure a comprehensive assessment. Secondly, due to the English language restriction in the review, potentially relevant studies may have been omitted. Future research could enhance inclusivity by extending its scope to encompass papers written in languages other than English. Thirdly, the current study exclusively focused on journal articles. Expanding the scope to include diverse sources, such as conference proceedings or book chapters, could offer a more comprehensive overview.

Additionally, as a rapidly evolving field, literature published after our inclusion dates need capturing, and future studies should consider adjusting their inclusion criteria to accommodate the dynamic nature of the subject matter. Lastly, the specificity of the bibliometric data search, centred around terms like ChatGPT, AI, higher education, and academic integrity, may have excluded certain relevant articles. Future studies should consider employing more generalised search parameters to encompass synonyms associated with these terms.

4.3 Future scope

The findings of the study suggest new avenues for future research. The effectiveness of evaluation criteria for assessments incorporating ChatGPT-generated text needs to be investigated. Specifically, the appropriate level of ChatGPT-produced text that students may use in academic tasks or assessments has not been established. Research on the ethical implications of using AI tools such as ChatGPT in higher education is also needed. Issues pertaining to data confidentiality, bias, and transparency in algorithms used for decision-making remain to be addressed. Feasible approaches for mitigating the excessive reliance of scholars and learners on ChatGPT or similar AI models are needed. Researchers could also explore the implementation of verification processes that go beyond traditional plagiarism detection methods, accounting for the unique challenges posed by AI systems. Future research in this domain could focus on establishing guidelines and best practices for the integration of AI tools like ChatGPT in academic settings, ensuring a balance between technological innovation and the preservation of academic rigour. Finally, the literature on ChatGPT in higher education has largely focused on the medical and tourism sectors. Future researchers must explore applications of ChatGPT in other disciplines.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Bhullar, P.S., Joshi, M. & Chugh, R. ChatGPT in higher education - a synthesis of the literature and a future research agenda. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12723-x

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