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Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

• Article contents
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• Supplementary Data

Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications . If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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How to Write a Research Paper

Use the links below to jump directly to any section of this guide:

Research Paper Fundamentals

How to choose a topic or question, how to create a working hypothesis or thesis, common research paper methodologies, how to gather and organize evidence , how to write an outline for your research paper, how to write a rough draft, how to revise your draft, how to produce a final draft, resources for teachers .

It is not fair to say that no one writes anymore. Just about everyone writes text messages, brief emails, or social media posts every single day. Yet, most people don't have a lot of practice with the formal, organized writing required for a good academic research paper. This guide contains links to a variety of resources that can help demystify the process. Some of these resources are intended for teachers; they contain exercises, activities, and teaching strategies. Other resources are intended for direct use by students who are struggling to write papers, or are looking for tips to make the process go more smoothly.

The resources in this section are designed to help students understand the different types of research papers, the general research process, and how to manage their time. Below, you'll find links from university writing centers, the trusted Purdue Online Writing Lab, and more.

What is an Academic Research Paper?

"Genre and the Research Paper" (Purdue OWL)

There are different types of research papers. Different types of scholarly questions will lend themselves to one format or another. This is a brief introduction to the two main genres of research paper: analytic and argumentative. 

"7 Most Popular Types of Research Papers" (Personal-writer.com)

This resource discusses formats that high school students commonly encounter, such as the compare and contrast essay and the definitional essay. Please note that the inclusion of this link is not an endorsement of this company's paid service.

How to Prepare and Plan Out Writing a Research Paper

Teachers can give their students a step-by-step guide like these to help them understand the different steps of the research paper process. These guides can be combined with the time management tools in the next subsection to help students come up with customized calendars for completing their papers.

"Ten Steps for Writing Research Papers" (American University)  

This resource from American University is a comprehensive guide to the research paper writing process, and includes examples of proper research questions and thesis topics.

"Steps in Writing a Research Paper" (SUNY Empire State College)

This guide breaks the research paper process into 11 steps. Each "step" links to a separate page, which describes the work entailed in completing it.

How to Manage Time Effectively

The links below will help students determine how much time is necessary to complete a paper. If your sources are not available online or at your local library, you'll need to leave extra time for the Interlibrary Loan process. Remember that, even if you do not need to consult secondary sources, you'll still need to leave yourself ample time to organize your thoughts.

"Research Paper Planner: Timeline" (Baylor University)

This interactive resource from Baylor University creates a suggested writing schedule based on how much time a student has to work on the assignment.

"Research Paper Planner" (UCLA)

UCLA's library offers this step-by-step guide to the research paper writing process, which also includes a suggested planning calendar.

There's a reason teachers spend a long time talking about choosing a good topic. Without a good topic and a well-formulated research question, it is almost impossible to write a clear and organized paper. The resources below will help you generate ideas and formulate precise questions.

"How to Select a Research Topic" (Univ. of Michigan-Flint)

This resource is designed for college students who are struggling to come up with an appropriate topic. A student who uses this resource and still feels unsure about his or her topic should consult the course instructor for further personalized assistance.

"25 Interesting Research Paper Topics to Get You Started" (Kibin)

This resource, which is probably most appropriate for high school students, provides a list of specific topics to help get students started. It is broken into subsections, such as "paper topics on local issues."

"Writing a Good Research Question" (Grand Canyon University)

This introduction to research questions includes some embedded videos, as well as links to scholarly articles on research questions. This resource would be most appropriate for teachers who are planning lessons on research paper fundamentals.

"How to Write a Research Question the Right Way" (Kibin)

This student-focused resource provides more detail on writing research questions. The language is accessible, and there are embedded videos and examples of good and bad questions.

It is important to have a rough hypothesis or thesis in mind at the beginning of the research process. People who have a sense of what they want to say will have an easier time sorting through scholarly sources and other information. The key, of course, is not to become too wedded to the draft hypothesis or thesis. Just about every working thesis gets changed during the research process.

CrashCourse Video: "Sociology Research Methods" (YouTube)

Although this video is tailored to sociology students, it is applicable to students in a variety of social science disciplines. This video does a good job demonstrating the connection between the brainstorming that goes into selecting a research question and the formulation of a working hypothesis.

"How to Write a Thesis Statement for an Analytical Essay" (YouTube)

Students writing analytical essays will not develop the same type of working hypothesis as students who are writing research papers in other disciplines. For these students, developing the working thesis may happen as a part of the rough draft (see the relevant section below). 

"Research Hypothesis" (Oakland Univ.)

This resource provides some examples of hypotheses in social science disciplines like Political Science and Criminal Justice. These sample hypotheses may also be useful for students in other soft social sciences and humanities disciplines like History.

When grading a research paper, instructors look for a consistent methodology. This section will help you understand different methodological approaches used in research papers. Students will get the most out of these resources if they use them to help prepare for conversations with teachers or discussions in class.

"Types of Research Designs" (USC)

A "research design," used for complex papers, is related to the paper's method. This resource contains introductions to a variety of popular research designs in the social sciences. Although it is not the most intuitive site to read, the information here is very valuable. 

"Major Research Methods" (YouTube)

Although this video is a bit on the dry side, it provides a comprehensive overview of the major research methodologies in a format that might be more accessible to students who have struggled with textbooks or other written resources.

"Humanities Research Strategies" (USC)

This is a portal where students can learn about four methodological approaches for humanities papers: Historical Methodologies, Textual Criticism, Conceptual Analysis, and the Synoptic method.

"Selected Major Social Science Research Methods: Overview" (National Academies Press)

This appendix from the book  Using Science as Evidence in Public Policy , printed by National Academies Press, introduces some methods used in social science papers.

"Organizing Your Social Sciences Research Paper: 6. The Methodology" (USC)

This resource from the University of Southern California's library contains tips for writing a methodology section in a research paper.

How to Determine the Best Methodology for You

Anyone who is new to writing research papers should be sure to select a method in consultation with their instructor. These resources can be used to help prepare for that discussion. They may also be used on their own by more advanced students.

"Choosing Appropriate Research Methodologies" (Palgrave Study Skills)

This friendly and approachable resource from Palgrave Macmillan can be used by students who are just starting to think about appropriate methodologies.

"How to Choose Your Research Methods" (NFER (UK))

This is another approachable resource students can use to help narrow down the most appropriate methods for their research projects.

The resources in this section introduce the process of gathering scholarly sources and collecting evidence. You'll find a range of material here, from introductory guides to advanced explications best suited to college students. Please consult the LitCharts  How to Do Academic Research guide for a more comprehensive list of resources devoted to finding scholarly literature.

Google Scholar

Students who have access to library websites with detailed research guides should start there, but people who do not have access to those resources can begin their search for secondary literature here.

"Gathering Appropriate Information" (Texas Gateway)

This resource from the Texas Gateway for online resources introduces students to the research process, and contains interactive exercises. The level of complexity is suitable for middle school, high school, and introductory college classrooms.

"An Overview of Quantitative and Qualitative Data Collection Methods" (NSF)

This PDF from the National Science Foundation goes into detail about best practices and pitfalls in data collection across multiple types of methodologies.

"Social Science Methods for Data Collection and Analysis" (Swiss FIT)

This resource is appropriate for advanced undergraduates or teachers looking to create lessons on research design and data collection. It covers techniques for gathering data via interviews, observations, and other methods.

"Collecting Data by In-depth Interviewing" (Leeds Univ.)

This resource contains enough information about conducting interviews to make it useful for teachers who want to create a lesson plan, but is also accessible enough for college juniors or seniors to make use of it on their own.

There is no "one size fits all" outlining technique. Some students might devote all their energy and attention to the outline in order to avoid the paper. Other students may benefit from being made to sit down and organize their thoughts into a lengthy sentence outline. The resources in this section include strategies and templates for multiple types of outlines. 

"Topic vs. Sentence Outlines" (UC Berkeley)

This resource introduces two basic approaches to outlining: the shorter topic-based approach, and the longer, more detailed sentence-based approach. This resource also contains videos on how to develop paper paragraphs from the sentence-based outline.

"Types of Outlines and Samples" (Purdue OWL)

The Purdue Online Writing Lab's guide is a slightly less detailed discussion of different types of outlines. It contains several sample outlines.

"Writing An Outline" (Austin C.C.)

This resource from a community college contains sample outlines from an American history class that students can use as models.

"How to Structure an Outline for a College Paper" (YouTube)

This brief (sub-2 minute) video from the ExpertVillage YouTube channel provides a model of outline writing for students who are struggling with the idea.

"Outlining" (Harvard)

This is a good resource to consult after completing a draft outline. It offers suggestions for making sure your outline avoids things like unnecessary repetition.

As with outlines, rough drafts can take on many different forms. These resources introduce teachers and students to the various approaches to writing a rough draft. This section also includes resources that will help you cite your sources appropriately according to the MLA, Chicago, and APA style manuals.

"Creating a Rough Draft for a Research Paper" (Univ. of Minnesota)

This resource is useful for teachers in particular, as it provides some suggested exercises to help students with writing a basic rough draft. 

Rough Draft Assignment (Duke of Definition)

This sample assignment, with a brief list of tips, was developed by a high school teacher who runs a very successful and well-reviewed page of educational resources.

"Creating the First Draft of Your Research Paper" (Concordia Univ.)

This resource will be helpful for perfectionists or procrastinators, as it opens by discussing the problem of avoiding writing. It also provides a short list of suggestions meant to get students writing.

Using Proper Citations

There is no such thing as a rough draft of a scholarly citation. These links to the three major citation guides will ensure that your citations follow the correct format. Please consult the LitCharts How to Cite Your Sources guide for more resources.

Chicago Manual of Style Citation Guide

Some call  The Chicago Manual of Style , which was first published in 1906, "the editors' Bible." The manual is now in its 17th edition, and is popular in the social sciences, historical journals, and some other fields in the humanities.

APA Citation Guide

According to the American Psychological Association, this guide was developed to aid reading comprehension, clarity of communication, and to reduce bias in language in the social and behavioral sciences. Its first full edition was published in 1952, and it is now in its sixth edition.

MLA Citation Guide

The Modern Language Association style is used most commonly within the liberal arts and humanities. The  MLA Style Manual and Guide to Scholarly Publishing  was first published in 1985 and (as of 2008) is in its third edition.

Any professional scholar will tell you that the best research papers are made in the revision stage. No matter how strong your research question or working thesis, it is not possible to write a truly outstanding paper without devoting energy to revision. These resources provide examples of revision exercises for the classroom, as well as tips for students working independently.

"The Art of Revision" (Univ. of Arizona)

This resource provides a wealth of information and suggestions for both students and teachers. There is a list of suggested exercises that teachers might use in class, along with a revision checklist that is useful for teachers and students alike.

"Script for Workshop on Revision" (Vanderbilt University)

Vanderbilt's guide for leading a 50-minute revision workshop can serve as a model for teachers who wish to guide students through the revision process during classtime. 

"Revising Your Paper" (Univ. of Washington)

This detailed handout was designed for students who are beginning the revision process. It discusses different approaches and methods for revision, and also includes a detailed list of things students should look for while they revise.

"Revising Drafts" (UNC Writing Center)

This resource is designed for students and suggests things to look for during the revision process. It provides steps for the process and has a FAQ for students who have questions about why it is important to revise.

Conferencing with Writing Tutors and Instructors

No writer is so good that he or she can't benefit from meeting with instructors or peer tutors. These resources from university writing, learning, and communication centers provide suggestions for how to get the most out of these one-on-one meetings.

"Getting Feedback" (UNC Writing Center)

This very helpful resource talks about how to ask for feedback during the entire writing process. It contains possible questions that students might ask when developing an outline, during the revision process, and after the final draft has been graded.

"Prepare for Your Tutoring Session" (Otis College of Art and Design)

This guide from a university's student learning center contains a lot of helpful tips for getting the most out of working with a writing tutor.

"The Importance of Asking Your Professor" (Univ. of Waterloo)

This article from the university's Writing and Communication Centre's blog contains some suggestions for how and when to get help from professors and Teaching Assistants.

Once you've revised your first draft, you're well on your way to handing in a polished paper. These resources—each of them produced by writing professionals at colleges and universities—outline the steps required in order to produce a final draft. You'll find proofreading tips and checklists in text and video form.

"Developing a Final Draft of a Research Paper" (Univ. of Minnesota)

While this resource contains suggestions for revision, it also features a couple of helpful checklists for the last stages of completing a final draft.

Basic Final Draft Tips and Checklist (Univ. of Maryland-University College)

This short and accessible resource, part of UMUC's very thorough online guide to writing and research, contains a very basic checklist for students who are getting ready to turn in their final drafts.

Final Draft Checklist (Everett C.C.)

This is another accessible final draft checklist, appropriate for both high school and college students. It suggests reading your essay aloud at least once.

"How to Proofread Your Final Draft" (YouTube)

This video (approximately 5 minutes), produced by Eastern Washington University, gives students tips on proofreading final drafts.

"Proofreading Tips" (Georgia Southern-Armstrong)

This guide will help students learn how to spot common errors in their papers. It suggests focusing on content and editing for grammar and mechanics.

This final set of resources is intended specifically for high school and college instructors. It provides links to unit plans and classroom exercises that can help improve students' research and writing skills. You'll find resources that give an overview of the process, along with activities that focus on how to begin and how to carry out research. 

"Research Paper Complete Resources Pack" (Teachers Pay Teachers)

This packet of assignments, rubrics, and other resources is designed for high school students. The resources in this packet are aligned to Common Core standards.

"Research Paper—Complete Unit" (Teachers Pay Teachers)

This packet of assignments, notes, PowerPoints, and other resources has a 4/4 rating with over 700 ratings. It is designed for high school teachers, but might also be useful to college instructors who work with freshmen.

"Teaching Students to Write Good Papers" (Yale)

This resource from Yale's Center for Teaching and Learning is designed for college instructors, and it includes links to appropriate activities and exercises.

"Research Paper Writing: An Overview" (CUNY Brooklyn)

CUNY Brooklyn offers this complete lesson plan for introducing students to research papers. It includes an accompanying set of PowerPoint slides.

"Lesson Plan: How to Begin Writing a Research Paper" (San Jose State Univ.)

This lesson plan is designed for students in the health sciences, so teachers will have to modify it for their own needs. It includes a breakdown of the brainstorming, topic selection, and research question process. 

"Quantitative Techniques for Social Science Research" (Univ. of Pittsburgh)

This is a set of PowerPoint slides that can be used to introduce students to a variety of quantitative methods used in the social sciences.

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Focus: Education — Career Advice

How to write your first research paper.

Writing a research manuscript is an intimidating process for many novice writers in the sciences. One of the stumbling blocks is the beginning of the process and creating the first draft. This paper presents guidelines on how to initiate the writing process and draft each section of a research manuscript. The paper discusses seven rules that allow the writer to prepare a well-structured and comprehensive manuscript for a publication submission. In addition, the author lists different strategies for successful revision. Each of those strategies represents a step in the revision process and should help the writer improve the quality of the manuscript. The paper could be considered a brief manual for publication.

It is late at night. You have been struggling with your project for a year. You generated an enormous amount of interesting data. Your pipette feels like an extension of your hand, and running western blots has become part of your daily routine, similar to brushing your teeth. Your colleagues think you are ready to write a paper, and your lab mates tease you about your “slow” writing progress. Yet days pass, and you cannot force yourself to sit down to write. You have not written anything for a while (lab reports do not count), and you feel you have lost your stamina. How does the writing process work? How can you fit your writing into a daily schedule packed with experiments? What section should you start with? What distinguishes a good research paper from a bad one? How should you revise your paper? These and many other questions buzz in your head and keep you stressed. As a result, you procrastinate. In this paper, I will discuss the issues related to the writing process of a scientific paper. Specifically, I will focus on the best approaches to start a scientific paper, tips for writing each section, and the best revision strategies.

1. Schedule your writing time in Outlook

Whether you have written 100 papers or you are struggling with your first, starting the process is the most difficult part unless you have a rigid writing schedule. Writing is hard. It is a very difficult process of intense concentration and brain work. As stated in Hayes’ framework for the study of writing: “It is a generative activity requiring motivation, and it is an intellectual activity requiring cognitive processes and memory” [ 1 ]. In his book How to Write a Lot: A Practical Guide to Productive Academic Writing , Paul Silvia says that for some, “it’s easier to embalm the dead than to write an article about it” [ 2 ]. Just as with any type of hard work, you will not succeed unless you practice regularly. If you have not done physical exercises for a year, only regular workouts can get you into good shape again. The same kind of regular exercises, or I call them “writing sessions,” are required to be a productive author. Choose from 1- to 2-hour blocks in your daily work schedule and consider them as non-cancellable appointments. When figuring out which blocks of time will be set for writing, you should select the time that works best for this type of work. For many people, mornings are more productive. One Yale University graduate student spent a semester writing from 8 a.m. to 9 a.m. when her lab was empty. At the end of the semester, she was amazed at how much she accomplished without even interrupting her regular lab hours. In addition, doing the hardest task first thing in the morning contributes to the sense of accomplishment during the rest of the day. This positive feeling spills over into our work and life and has a very positive effect on our overall attitude.

Rule 1: Create regular time blocks for writing as appointments in your calendar and keep these appointments.

2. start with an outline.

Now that you have scheduled time, you need to decide how to start writing. The best strategy is to start with an outline. This will not be an outline that you are used to, with Roman numerals for each section and neat parallel listing of topic sentences and supporting points. This outline will be similar to a template for your paper. Initially, the outline will form a structure for your paper; it will help generate ideas and formulate hypotheses. Following the advice of George M. Whitesides, “. . . start with a blank piece of paper, and write down, in any order, all important ideas that occur to you concerning the paper” [ 3 ]. Use Table 1 as a starting point for your outline. Include your visuals (figures, tables, formulas, equations, and algorithms), and list your findings. These will constitute the first level of your outline, which will eventually expand as you elaborate.

The next stage is to add context and structure. Here you will group all your ideas into sections: Introduction, Methods, Results, and Discussion/Conclusion ( Table 2 ). This step will help add coherence to your work and sift your ideas.

Now that you have expanded your outline, you are ready for the next step: discussing the ideas for your paper with your colleagues and mentor. Many universities have a writing center where graduate students can schedule individual consultations and receive assistance with their paper drafts. Getting feedback during early stages of your draft can save a lot of time. Talking through ideas allows people to conceptualize and organize thoughts to find their direction without wasting time on unnecessary writing. Outlining is the most effective way of communicating your ideas and exchanging thoughts. Moreover, it is also the best stage to decide to which publication you will submit the paper. Many people come up with three choices and discuss them with their mentors and colleagues. Having a list of journal priorities can help you quickly resubmit your paper if your paper is rejected.

Rule 2: Create a detailed outline and discuss it with your mentor and peers.

3. continue with drafts.

After you get enough feedback and decide on the journal you will submit to, the process of real writing begins. Copy your outline into a separate file and expand on each of the points, adding data and elaborating on the details. When you create the first draft, do not succumb to the temptation of editing. Do not slow down to choose a better word or better phrase; do not halt to improve your sentence structure. Pour your ideas into the paper and leave revision and editing for later. As Paul Silvia explains, “Revising while you generate text is like drinking decaffeinated coffee in the early morning: noble idea, wrong time” [ 2 ].

Many students complain that they are not productive writers because they experience writer’s block. Staring at an empty screen is frustrating, but your screen is not really empty: You have a template of your article, and all you need to do is fill in the blanks. Indeed, writer’s block is a logical fallacy for a scientist ― it is just an excuse to procrastinate. When scientists start writing a research paper, they already have their files with data, lab notes with materials and experimental designs, some visuals, and tables with results. All they need to do is scrutinize these pieces and put them together into a comprehensive paper.

3.1. Starting with Materials and Methods

If you still struggle with starting a paper, then write the Materials and Methods section first. Since you have all your notes, it should not be problematic for you to describe the experimental design and procedures. Your most important goal in this section is to be as explicit as possible by providing enough detail and references. In the end, the purpose of this section is to allow other researchers to evaluate and repeat your work. So do not run into the same problems as the writers of the sentences in (1):

1a. Bacteria were pelleted by centrifugation. 1b. To isolate T cells, lymph nodes were collected.

As you can see, crucial pieces of information are missing: the speed of centrifuging your bacteria, the time, and the temperature in (1a); the source of lymph nodes for collection in (b). The sentences can be improved when information is added, as in (2a) and (2b), respectfully:

2a. Bacteria were pelleted by centrifugation at 3000g for 15 min at 25°C. 2b. To isolate T cells, mediastinal and mesenteric lymph nodes from Balb/c mice were collected at day 7 after immunization with ovabumin.

If your method has previously been published and is well-known, then you should provide only the literature reference, as in (3a). If your method is unpublished, then you need to make sure you provide all essential details, as in (3b).

3a. Stem cells were isolated, according to Johnson [23]. 3b. Stem cells were isolated using biotinylated carbon nanotubes coated with anti-CD34 antibodies.

Furthermore, cohesion and fluency are crucial in this section. One of the malpractices resulting in disrupted fluency is switching from passive voice to active and vice versa within the same paragraph, as shown in (4). This switching misleads and distracts the reader.

4. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. We took ratings of enjoyment, mood, and arousal as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness [ 4 ].

The problem with (4) is that the reader has to switch from the point of view of the experiment (passive voice) to the point of view of the experimenter (active voice). This switch causes confusion about the performer of the actions in the first and the third sentences. To improve the coherence and fluency of the paragraph above, you should be consistent in choosing the point of view: first person “we” or passive voice [ 5 ]. Let’s consider two revised examples in (5).

5a. We programmed behavioral computer-based experiments of Study 1 by using E-Prime. We took ratings of enjoyment, mood, and arousal by using Visual Analogue Scales (SI Methods) as the patients listened to preferred pleasant music and unpreferred music. We operationalized the preferred and unpreferred status of the music along a continuum of pleasantness. 5b. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. Ratings of enjoyment, mood, and arousal were taken as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness.

If you choose the point of view of the experimenter, then you may end up with repetitive “we did this” sentences. For many readers, paragraphs with sentences all beginning with “we” may also sound disruptive. So if you choose active sentences, you need to keep the number of “we” subjects to a minimum and vary the beginnings of the sentences [ 6 ].

Interestingly, recent studies have reported that the Materials and Methods section is the only section in research papers in which passive voice predominantly overrides the use of the active voice [ 5 , 7 , 8 , 9 ]. For example, Martínez shows a significant drop in active voice use in the Methods sections based on the corpus of 1 million words of experimental full text research articles in the biological sciences [ 7 ]. According to the author, the active voice patterned with “we” is used only as a tool to reveal personal responsibility for the procedural decisions in designing and performing experimental work. This means that while all other sections of the research paper use active voice, passive voice is still the most predominant in Materials and Methods sections.

Writing Materials and Methods sections is a meticulous and time consuming task requiring extreme accuracy and clarity. This is why when you complete your draft, you should ask for as much feedback from your colleagues as possible. Numerous readers of this section will help you identify the missing links and improve the technical style of this section.

Rule 3: Be meticulous and accurate in describing the Materials and Methods. Do not change the point of view within one paragraph.

3.2. writing results section.

For many authors, writing the Results section is more intimidating than writing the Materials and Methods section . If people are interested in your paper, they are interested in your results. That is why it is vital to use all your writing skills to objectively present your key findings in an orderly and logical sequence using illustrative materials and text.

Your Results should be organized into different segments or subsections where each one presents the purpose of the experiment, your experimental approach, data including text and visuals (tables, figures, schematics, algorithms, and formulas), and data commentary. For most journals, your data commentary will include a meaningful summary of the data presented in the visuals and an explanation of the most significant findings. This data presentation should not repeat the data in the visuals, but rather highlight the most important points. In the “standard” research paper approach, your Results section should exclude data interpretation, leaving it for the Discussion section. However, interpretations gradually and secretly creep into research papers: “Reducing the data, generalizing from the data, and highlighting scientific cases are all highly interpretive processes. It should be clear by now that we do not let the data speak for themselves in research reports; in summarizing our results, we interpret them for the reader” [ 10 ]. As a result, many journals including the Journal of Experimental Medicine and the Journal of Clinical Investigation use joint Results/Discussion sections, where results are immediately followed by interpretations.

Another important aspect of this section is to create a comprehensive and supported argument or a well-researched case. This means that you should be selective in presenting data and choose only those experimental details that are essential for your reader to understand your findings. You might have conducted an experiment 20 times and collected numerous records, but this does not mean that you should present all those records in your paper. You need to distinguish your results from your data and be able to discard excessive experimental details that could distract and confuse the reader. However, creating a picture or an argument should not be confused with data manipulation or falsification, which is a willful distortion of data and results. If some of your findings contradict your ideas, you have to mention this and find a plausible explanation for the contradiction.

In addition, your text should not include irrelevant and peripheral information, including overview sentences, as in (6).

6. To show our results, we first introduce all components of experimental system and then describe the outcome of infections.

Indeed, wordiness convolutes your sentences and conceals your ideas from readers. One common source of wordiness is unnecessary intensifiers. Adverbial intensifiers such as “clearly,” “essential,” “quite,” “basically,” “rather,” “fairly,” “really,” and “virtually” not only add verbosity to your sentences, but also lower your results’ credibility. They appeal to the reader’s emotions but lower objectivity, as in the common examples in (7):

7a. Table 3 clearly shows that … 7b. It is obvious from figure 4 that …

Another source of wordiness is nominalizations, i.e., nouns derived from verbs and adjectives paired with weak verbs including “be,” “have,” “do,” “make,” “cause,” “provide,” and “get” and constructions such as “there is/are.”

8a. We tested the hypothesis that there is a disruption of membrane asymmetry. 8b. In this paper we provide an argument that stem cells repopulate injured organs.

In the sentences above, the abstract nominalizations “disruption” and “argument” do not contribute to the clarity of the sentences, but rather clutter them with useless vocabulary that distracts from the meaning. To improve your sentences, avoid unnecessary nominalizations and change passive verbs and constructions into active and direct sentences.

9a. We tested the hypothesis that the membrane asymmetry is disrupted. 9b. In this paper we argue that stem cells repopulate injured organs.

Your Results section is the heart of your paper, representing a year or more of your daily research. So lead your reader through your story by writing direct, concise, and clear sentences.

Rule 4: Be clear, concise, and objective in describing your Results.

3.3. now it is time for your introduction.

Now that you are almost half through drafting your research paper, it is time to update your outline. While describing your Methods and Results, many of you diverged from the original outline and re-focused your ideas. So before you move on to create your Introduction, re-read your Methods and Results sections and change your outline to match your research focus. The updated outline will help you review the general picture of your paper, the topic, the main idea, and the purpose, which are all important for writing your introduction.

The best way to structure your introduction is to follow the three-move approach shown in Table 3 .

Adapted from Swales and Feak [ 11 ].

The moves and information from your outline can help to create your Introduction efficiently and without missing steps. These moves are traffic signs that lead the reader through the road of your ideas. Each move plays an important role in your paper and should be presented with deep thought and care. When you establish the territory, you place your research in context and highlight the importance of your research topic. By finding the niche, you outline the scope of your research problem and enter the scientific dialogue. The final move, “occupying the niche,” is where you explain your research in a nutshell and highlight your paper’s significance. The three moves allow your readers to evaluate their interest in your paper and play a significant role in the paper review process, determining your paper reviewers.

Some academic writers assume that the reader “should follow the paper” to find the answers about your methodology and your findings. As a result, many novice writers do not present their experimental approach and the major findings, wrongly believing that the reader will locate the necessary information later while reading the subsequent sections [ 5 ]. However, this “suspense” approach is not appropriate for scientific writing. To interest the reader, scientific authors should be direct and straightforward and present informative one-sentence summaries of the results and the approach.

Another problem is that writers understate the significance of the Introduction. Many new researchers mistakenly think that all their readers understand the importance of the research question and omit this part. However, this assumption is faulty because the purpose of the section is not to evaluate the importance of the research question in general. The goal is to present the importance of your research contribution and your findings. Therefore, you should be explicit and clear in describing the benefit of the paper.

The Introduction should not be long. Indeed, for most journals, this is a very brief section of about 250 to 600 words, but it might be the most difficult section due to its importance.

Rule 5: Interest your reader in the Introduction section by signalling all its elements and stating the novelty of the work.

3.4. discussion of the results.

For many scientists, writing a Discussion section is as scary as starting a paper. Most of the fear comes from the variation in the section. Since every paper has its unique results and findings, the Discussion section differs in its length, shape, and structure. However, some general principles of writing this section still exist. Knowing these rules, or “moves,” can change your attitude about this section and help you create a comprehensive interpretation of your results.

The purpose of the Discussion section is to place your findings in the research context and “to explain the meaning of the findings and why they are important, without appearing arrogant, condescending, or patronizing” [ 11 ]. The structure of the first two moves is almost a mirror reflection of the one in the Introduction. In the Introduction, you zoom in from general to specific and from the background to your research question; in the Discussion section, you zoom out from the summary of your findings to the research context, as shown in Table 4 .

Adapted from Swales and Feak and Hess [ 11 , 12 ].

The biggest challenge for many writers is the opening paragraph of the Discussion section. Following the moves in Table 1 , the best choice is to start with the study’s major findings that provide the answer to the research question in your Introduction. The most common starting phrases are “Our findings demonstrate . . .,” or “In this study, we have shown that . . .,” or “Our results suggest . . .” In some cases, however, reminding the reader about the research question or even providing a brief context and then stating the answer would make more sense. This is important in those cases where the researcher presents a number of findings or where more than one research question was presented. Your summary of the study’s major findings should be followed by your presentation of the importance of these findings. One of the most frequent mistakes of the novice writer is to assume the importance of his findings. Even if the importance is clear to you, it may not be obvious to your reader. Digesting the findings and their importance to your reader is as crucial as stating your research question.

Another useful strategy is to be proactive in the first move by predicting and commenting on the alternative explanations of the results. Addressing potential doubts will save you from painful comments about the wrong interpretation of your results and will present you as a thoughtful and considerate researcher. Moreover, the evaluation of the alternative explanations might help you create a logical step to the next move of the discussion section: the research context.

The goal of the research context move is to show how your findings fit into the general picture of the current research and how you contribute to the existing knowledge on the topic. This is also the place to discuss any discrepancies and unexpected findings that may otherwise distort the general picture of your paper. Moreover, outlining the scope of your research by showing the limitations, weaknesses, and assumptions is essential and adds modesty to your image as a scientist. However, make sure that you do not end your paper with the problems that override your findings. Try to suggest feasible explanations and solutions.

If your submission does not require a separate Conclusion section, then adding another paragraph about the “take-home message” is a must. This should be a general statement reiterating your answer to the research question and adding its scientific implications, practical application, or advice.

Just as in all other sections of your paper, the clear and precise language and concise comprehensive sentences are vital. However, in addition to that, your writing should convey confidence and authority. The easiest way to illustrate your tone is to use the active voice and the first person pronouns. Accompanied by clarity and succinctness, these tools are the best to convince your readers of your point and your ideas.

Rule 6: Present the principles, relationships, and generalizations in a concise and convincing tone.

4. choosing the best working revision strategies.

Now that you have created the first draft, your attitude toward your writing should have improved. Moreover, you should feel more confident that you are able to accomplish your project and submit your paper within a reasonable timeframe. You also have worked out your writing schedule and followed it precisely. Do not stop ― you are only at the midpoint from your destination. Just as the best and most precious diamond is no more than an unattractive stone recognized only by trained professionals, your ideas and your results may go unnoticed if they are not polished and brushed. Despite your attempts to present your ideas in a logical and comprehensive way, first drafts are frequently a mess. Use the advice of Paul Silvia: “Your first drafts should sound like they were hastily translated from Icelandic by a non-native speaker” [ 2 ]. The degree of your success will depend on how you are able to revise and edit your paper.

The revision can be done at the macrostructure and the microstructure levels [ 13 ]. The macrostructure revision includes the revision of the organization, content, and flow. The microstructure level includes individual words, sentence structure, grammar, punctuation, and spelling.

The best way to approach the macrostructure revision is through the outline of the ideas in your paper. The last time you updated your outline was before writing the Introduction and the Discussion. Now that you have the beginning and the conclusion, you can take a bird’s-eye view of the whole paper. The outline will allow you to see if the ideas of your paper are coherently structured, if your results are logically built, and if the discussion is linked to the research question in the Introduction. You will be able to see if something is missing in any of the sections or if you need to rearrange your information to make your point.

The next step is to revise each of the sections starting from the beginning. Ideally, you should limit yourself to working on small sections of about five pages at a time [ 14 ]. After these short sections, your eyes get used to your writing and your efficiency in spotting problems decreases. When reading for content and organization, you should control your urge to edit your paper for sentence structure and grammar and focus only on the flow of your ideas and logic of your presentation. Experienced researchers tend to make almost three times the number of changes to meaning than novice writers [ 15 , 16 ]. Revising is a difficult but useful skill, which academic writers obtain with years of practice.

In contrast to the macrostructure revision, which is a linear process and is done usually through a detailed outline and by sections, microstructure revision is a non-linear process. While the goal of the macrostructure revision is to analyze your ideas and their logic, the goal of the microstructure editing is to scrutinize the form of your ideas: your paragraphs, sentences, and words. You do not need and are not recommended to follow the order of the paper to perform this type of revision. You can start from the end or from different sections. You can even revise by reading sentences backward, sentence by sentence and word by word.

One of the microstructure revision strategies frequently used during writing center consultations is to read the paper aloud [ 17 ]. You may read aloud to yourself, to a tape recorder, or to a colleague or friend. When reading and listening to your paper, you are more likely to notice the places where the fluency is disrupted and where you stumble because of a very long and unclear sentence or a wrong connector.

Another revision strategy is to learn your common errors and to do a targeted search for them [ 13 ]. All writers have a set of problems that are specific to them, i.e., their writing idiosyncrasies. Remembering these problems is as important for an academic writer as remembering your friends’ birthdays. Create a list of these idiosyncrasies and run a search for these problems using your word processor. If your problem is demonstrative pronouns without summary words, then search for “this/these/those” in your text and check if you used the word appropriately. If you have a problem with intensifiers, then search for “really” or “very” and delete them from the text. The same targeted search can be done to eliminate wordiness. Searching for “there is/are” or “and” can help you avoid the bulky sentences.

The final strategy is working with a hard copy and a pencil. Print a double space copy with font size 14 and re-read your paper in several steps. Try reading your paper line by line with the rest of the text covered with a piece of paper. When you are forced to see only a small portion of your writing, you are less likely to get distracted and are more likely to notice problems. You will end up spotting more unnecessary words, wrongly worded phrases, or unparallel constructions.

After you apply all these strategies, you are ready to share your writing with your friends, colleagues, and a writing advisor in the writing center. Get as much feedback as you can, especially from non-specialists in your field. Patiently listen to what others say to you ― you are not expected to defend your writing or explain what you wanted to say. You may decide what you want to change and how after you receive the feedback and sort it in your head. Even though some researchers make the revision an endless process and can hardly stop after a 14th draft; having from five to seven drafts of your paper is a norm in the sciences. If you can’t stop revising, then set a deadline for yourself and stick to it. Deadlines always help.

Rule 7: Revise your paper at the macrostructure and the microstructure level using different strategies and techniques. Receive feedback and revise again.

5. it is time to submit.

It is late at night again. You are still in your lab finishing revisions and getting ready to submit your paper. You feel happy ― you have finally finished a year’s worth of work. You will submit your paper tomorrow, and regardless of the outcome, you know that you can do it. If one journal does not take your paper, you will take advantage of the feedback and resubmit again. You will have a publication, and this is the most important achievement.

What is even more important is that you have your scheduled writing time that you are going to keep for your future publications, for reading and taking notes, for writing grants, and for reviewing papers. You are not going to lose stamina this time, and you will become a productive scientist. But for now, let’s celebrate the end of the paper.

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Writing a Research Paper

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The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to write your paper

On this page, writing for a nature journal, how to write a scientific paper.

Before writing a paper, authors are advised to visit the author information pages of the journal to which they wish to submit (see this link for a  full list of Nature Portfolio publications ). Each journal has slightly different format requirements depending on readership, space, style and so on. The journal's website will contain detailed information about format, length limits, figure preparation, and similar matters. If your questions are not answered on these pages or through our recommended guidelines below, we suggest you contact the journal’s editorial office for further guidance before submitting. Contact information for the editorial offices can be found on the journal websites.

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Many papers submitted for publication in a Nature journal contain unnecessary technical terminology, unreadable descriptions of the work that has been done, and convoluted figure legends. Our journal subeditors and copyeditors edit the manuscript so that it is grammatically correct, logical, clear and concise. They also ensure that manuscripts use consistent search terms and terminology that is consistent with what is used in previous articles published in the journal. Of course, this process is assisted greatly if the authors have written the manuscript in a simple and accessible style, as the author is the best person to convey the message of the paper and to persuade readers that it is important enough to spend time on.

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Titles need to be comprehensible and enticing to a potential reader quickly scanning a table of contents or performing an online search, while at the same time not being so general or vague as to obscure what the paper is about. We ask authors to be aware of abstracting and indexing services when devising a title for the paper: providing one or two essential keywords within a title will be beneficial for web-search results.

Within the text of papers, Nature journals use a numbering (Vancouver) system for references, not the Harvard method whereby the authors and year of publication are included in the text in parentheses. We adopt this numbering style because we believe the text flows more smoothly, and hence is quicker for the reader to absorb.

Our experience has shown that a paper's impact is maximized if it is as short as is consistent with providing a focused message, with a few crucial figures or tables. Authors can place technical information (figures, protocols, methods, tables, additional data) necessary to support their conclusion into Supplementary Information (SI), which is published online-only to accompany the published print/online paper. SI is peer-reviewed, and we believe that its use means that the impact of the conclusions of the study is enhanced by being presented in concise and focused form in the print/online journal, emphasizing the key conclusions of the research and yet providing the full supporting details required by others in the field in online-only form. We encourage authors to use SI  in this way to enhance the impact of the print/online version, and hence to increase its readership. Authors are asked to provide short "signposts" at appropriate points in their paper to indicate that SI is present to expand on a particular point (for example "for more details, see figure x in SI) so that readers can navigate easily to the relevant information.  We also encourage authors who are describing methods and protocols to provide the full details as SI.

We all face the challenge of how to make the best use of our time in an era of information overload. Judicious use of SI to ensure that the printed version of a paper is clear, comprehensible and as short as is consistent with this goal, is very likely to increase the paper's readership, impact and the number of times others cite it.

Nature Physics: the Editorial  Elements of style  explains the importance of clear and accessible writing. The advice contained within this Editorial applies to all the Nature journals.

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A number of articles and websites provide detailed guidelines and advice about writing and submitting scientific papers. Some suggested sources are:

• SciDev.Net's  Practical guides section  (including  How to submit a paper to a scientific journal  and  How to write a scientific paper )
• The Human Frontier Science Program's report  Websites and Searching for Collaborations  also contains useful writing guidelines for non-native-English speakers, as well as other helpful advice related to scientific publishing
• The classic book Elements of Style by William J. Strunk, Jr (Humphrey, New York, 1918) is now published by Bartleby.com (New York, 1999) and is  freely available on the web  in searchable format.
• Advice about how to write a Nature journal paper is provided in the Nature Physics Editorial  Elements of style .
• Advice about how to write a summary paragraph (abstract) in Nature Letter format is available as a  one-page downloadable information sheet .
• An amusing but pertinent algorithm,  How to write a paper (one possible answer) is at Nature Network's New York blog.

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Essential Rules for Academic Writing: A Beginner’s Guide

Unlock the key rules for academic writing: from structure to citations. Master scholarly communication with expert insights.

Mastering the art of academic writing is a fundamental skill that empowers students and researchers to express their ideas, convey complex concepts, and contribute meaningfully to their respective fields. However, for beginners venturing into the realm of scholarly writing, navigating the intricacies of this formal discourse can be a daunting task.

“Essential Rules for Academic Writing: A Beginner’s Guide” serves as a beacon of guidance, illuminating the path for aspiring scholars as they embark on their academic journey. This comprehensive article offers invaluable insights into the fundamental principles and key rules that underpin successful academic writing, providing a strong foundation for those new to the craft.

What Is Academic Writing?

Academic writing refers to a formal style of writing that is prevalent in academic settings such as universities, research institutions, and scholarly publications. It is a mode of communication used by students, researchers, and scholars to convey their ideas, present research findings, and engage in intellectual discourse within their respective fields of study.

Related article: 11 Best Grammar Checker Tools For Academic Writing

Unlike other forms of writing, academic writing adheres to specific conventions and standards that prioritize clarity, precision, objectivity, and critical thinking. It is characterized by a rigorous approach to presenting arguments, supporting claims with evidence, and adhering to the principles of logic and reasoning.

Academic writing encompasses a wide range of genres, including essays, research papers, literature reviews, theses, dissertations, conference papers, and journal articles. Regardless of the specific genre, academic writing typically follows a structured format, includes proper citation and referencing, and adheres to established academic style guides such as APA (American Psychological Association) or MLA (Modern Language Association).

Types Of Academic Writing

Here’s a table summarizing the different types of academic writing, along with their definitions, purposes and typical structures:

Also read: Words To Use In Essays: Amplifying Your Academic Writing

General Rules For Academic Writing

Here are some general rules for academic writing: by adhering to these general guidelines, you can enhance the clarity, effectiveness, and professionalism of your academic writing, ensuring that your ideas are communicated with precision and impact.

Clarity and Precision

Academic writing demands clarity and precision in the expression of ideas. Use clear and concise language to communicate your thoughts effectively. Avoid ambiguous or vague statements, and strive for a logical flow of ideas within your writing.

Audience Awareness

Consider your intended audience when writing academically. Be aware of their background knowledge and familiarity with the topic. Adapt your writing style and level of technicality accordingly, ensuring that your content is accessible and understandable to your readers.

Use Formal Language

Academic writing requires a formal tone and language. Avoid colloquialisms, slang, and overly informal expressions. Instead, employ a vocabulary appropriate to the academic context, using specialized terms when necessary.

Structure and Organization

Structure your writing in a logical and coherent manner. Use clear headings, subheadings, and paragraphs to guide the reader through your work. Ensure that your ideas are well-organized and presented in a cohesive manner, with each paragraph or section contributing to the overall argument or discussion.

Evidence-Based Reasoning

Support your arguments and claims with credible evidence. Reference authoritative sources and cite them appropriately to establish the foundation for your ideas. Use empirical data, scholarly research, and reputable references to strengthen the validity and reliability of your work.

Critical Thinking

Academic writing encourages critical thinking and analysis. Engage with the existing literature, identify strengths and weaknesses in the arguments, and develop your own well-reasoned perspective. Challenge assumptions, evaluate alternative viewpoints, and provide well-supported arguments.

Proper Referencing and Citation

Maintain academic integrity by properly referencing and citing all sources used in your writing. Follow the specific citation style required by your academic institution or field, such as APA , MLA , or Chicago style . Accurate referencing gives credit to the original authors, allows readers to verify your sources, and demonstrates your commitment to scholarly integrity.

Revision and Proofreading

Academic writing involves a process of revision and proofreading. Review your work for clarity, coherence, grammar, and spelling errors. Ensure that your writing is free from typographical mistakes and inconsistencies. Seek feedback from peers, instructors, or writing centers to enhance the quality of your work.

Also read: What Is Proofreading And How To Harness Its Benefits?

How To Improve The Academic Writing

To enhance your academic writing skills, it is crucial to engage in regular practice and give careful consideration to various aspects. Here are some essential focal points to pay attention to in order to improve your academic writing:

Punctuation

• Proper use of commas, periods, question marks, and exclamation marks to enhance clarity and meaning in sentences.
• Effective use of semicolons and colons to join related independent clauses and introduce lists or explanations.
• Understanding the role of dashes and hyphens to indicate interruptions or join words in compound adjectives.

Capitalization

• Capitalize proper nouns, including names of people, places, institutions, and specific titles or terms.
• Follow capitalization rules for titles, capitalizing the first and last words, as well as major words within the title.
• Ensure consistency in capitalization within headings and subheadings.

Grammar and Sentence Structure

• Ensure subject-verb agreement, ensuring that the subject and verb agree in number and person.
• Use proper tenses and maintain consistency in verb tense usage within a paragraph or section.
• Write clear and unambiguous sentences, avoiding run-on sentences, fragments, or unclear pronoun references.

Academic Conventions

• Apply appropriate formatting and font style as per the guidelines of the specific academic institution or style guide.
• Use headings and subheadings correctly, following a consistent hierarchy and formatting style.
• Use abbreviations appropriately and consistently, following the accepted conventions in the field.
• Adhere to specific guidelines for tables, figures, and graphs, including proper numbering, labeling, and citation.

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A Must-see for Researchers! How to Ensure Inclusivity in Your Scientific Writing

• 4 minute read

Table of Contents

Highly influential research findings have several real-world implications that affect the public’s perception of individuals and communities to some extent. The way science is communicated shapes people’s behavior, interactions, and even related policies. As a result, in recent years there has been a growing recognition of the need to foster inclusive language within scholarly communication, which can help avoid bias or misunderstanding.  

Researchers, especially the younger generation, are becoming increasingly aware of the significance of using inclusive language in academic writing. This approach helps create a collaborative global academic landscape, while fostering respect for diverse perspectives. It is also conducive to the wide dissemination of papers and supports researchers in their long-term academic endeavors.  

This article will explain why and how to use inclusive language in your manuscript. Also, it will help researchers improve their ability to choose words with precision when writing by providing examples of appropriate inclusive terms. Let’s have a look!  

1. Referring to Persons with Disabilities¹  

When referring to someone with a disability, it is important to focus on the person first, not highlight their condition . Avoid using the terms “disabled person” or “handicapped person.” Instead, use person-first language, such as “a person with disability,” “a person with hearing loss,” etc.  

Additionally, when referring to individuals without disabilities, avoid using terms such as “normal” or “typical.” Instead, use phrases like “individuals without disabilities” or “people without disabilities.”  

Example of inclusive language : Students with disabilities often encounter distinct challenges in academic settings. These can range from physical barriers like inaccessible buildings to difficulties accessing educational materials in suitable formats. In contrast, students without disabilities typically navigate the educational environment with fewer hindrances.  

2. Using Gendered Nouns  

Gendered nouns such as “man” or words ending in “-man” can exclude certain groups, so it is best to avoid them² . Fortunately, they can be easily substituted with neutral terms . For instance, instead of writing “man”, write “person” or “individual” and instead of writing “mankind,” write “humanity” or “human beings.”  

When discussing people’s occupational roles, using neutral language is essential. For example, instead of writing “policeman,” write “police officer,” and instead of writing “chairman,” write “chairperson.”  

Example of gendered noun use:  The chairman oversees the company’s operations.  

Example of inclusive language:  The chairperson oversees the company’s operations.  

3. Using Pronouns  

When you know a person’s preferred pronoun, it is easy to incorporate it into writing. For example, most people use the pronouns he/him or she/her. However, using pronouns can become tricky in neutral or ambiguous contexts. In the past, it was common to use the  generic he   in these situations³ . However, it is best to avoid this practice as it can be exclusionary .  

Here are some tips to avoid the  generic he³ :  

Don’t only use he/his, add she/her  

For example, do not write:  An early career researcher needs mentors. He can learn the secrets to making an impact in academia with someone more experienced.  

Instead, write:  An early career researcher needs mentors. He or she can learn the secrets to making an impact in academia with someone more experienced.  

Eliminate the pronoun if possible  

For example, do not write:  We returned his manuscript two days after submission.  

Instead, write:  We returned the manuscript two days after submission.  

Use a plural term  

For example, do not write:  When an author revises his manuscript , he should consider the feedback provided by the peer reviewers.  

Instead, write:  When authors revise their manuscripts , they should consider the feedback provided by the peer reviewers.  

4. Describing Age  

As a general rule, refrain from mentioning a person’s age unless it is absolutely necessary for the context . In scientific writing, it is acceptable to use broad terms , such as infants, children, young adults, or older adults, to categorize age groups⁴ . This approach maintains inclusivity and respects individuals regardless of their age.   

Conclusion  

Embracing inclusive language in scholarly communication fosters a more welcoming environment for scholars from diverse backgrounds. It ensures that everyone, regardless of their life experiences, can equally benefit from advancements in science. It is worth noting that inclusive language constantly evolves with social development, which poses a great challenge for authors in terms of their English skills and the ability to pay attention to social trends.  

If you would like to achieve more efficient and inclusive expression in your papers, please choose Elsevier Language Services . Our professional editors, all native English speakers, with editing experience in more than 100 disciplines, can help you achieve professional, authentic, and inclusive academic expression in your papers, improve the chances of successful publication, and achieve long-term academic success.  

References:  

• University of Idaho Inclusive Writing Guide. (n.d.). https://www.uidaho.edu/brand/print-digital-content/inclusive-writing-guide  
• UNC-Chapel Hill Writing Center. (2023, December 8). Gender-Inclusive Language – The Writing Center. University of North Carolina at Chapel Hill. https://writingcenter.unc.edu/tips-and-tools/gender-inclusive-language/  
• Leu, P. (2020, July 2). Academic Writing: How do we use gender-inclusive language in academic writing? – Explorations in English Language Learning. Explorations in English Language Learning. https://englishexplorations.check.uni-hamburg.de/academic-writing-how-do-we-use-gender-inclusive-language-in-academic-writing/  
• Inclusive writing | York St John University. (n.d.). York St John University. https://www.yorksj.ac.uk/brand/our-writing-style/inclusive-writing/#age  

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Anticipating impacts: using large-scale scenario-writing to explore diverse implications of generative AI in the news environment

• Original Research
• Open access
• Published: 27 May 2024

Cite this article

You have full access to this open access article

• Kimon Kieslich   ORCID: orcid.org/0000-0002-6305-2997 1 ,
• Nicholas Diakopoulos   ORCID: orcid.org/0000-0001-5005-6123 2 &
• Natali Helberger   ORCID: orcid.org/0000-0003-1652-0580 1  

The tremendous rise of generative AI has reached every part of society—including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating the impact of generative AI is still in its infancy and mostly limited to the views of technology developers and/or researchers. In this paper, we aim to broaden the perspective and capture the expectations of three stakeholder groups (news consumers; technology developers; content creators) about the potential negative impacts of generative AI, as well as mitigation strategies to address these. Methodologically, we apply scenario-writing and use participatory foresight in the context of a survey (n = 119) to delve into cognitively diverse imaginations of the future. We qualitatively analyze the scenarios using thematic analysis to systematically map potential impacts of generative AI on the news environment, potential mitigation strategies, and the role of stakeholders in causing and mitigating these impacts. In addition, we measure respondents' opinions on a specific mitigation strategy, namely transparency obligations as suggested in Article 52 of the draft EU AI Act. We compare the results across different stakeholder groups and elaborate on different expected impacts across these groups. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment.

Avoid common mistakes on your manuscript.

1 I ntroduction

Whether overhyped or truly transformative, the growth of generative AI over the last years has been palpable as it washes over a range of domains from entertainment and law to marketing and news media. The technology leverages a powerful approach to training models from vast quantities of data that can then be prompted to create new pieces of media, whether that’s images, text, video, audio, 3D content, and so on, or extract bits of information from input media. The capabilities (and limitations) of generative AI are forcing broad rethinking on how information and media are created and how knowledge work itself is done.

Of particular interest in this work is how generative AI is increasingly used by news organizations and impacts the media environment. News organizations have already started experimenting with it, for example for summarizing content [ 58 ], supporting article writing [ 55 ], or moderating online content [ 30 , 33 ]. According to a survey conducted among news and media organizations in 2023, 85 percent of the respondents indicated they have experimented with generative AI [ 6 ]. The potential for the deployment of generative AI and its use in newsrooms is likely to continue to rise sharply, making discussions of the adverse consequences of large-scale adoption of generative AI, such as job losses, the spread of disinformation through deepfakes [ 60 ], accuracy issues (e.g., false source attributions) [ 22 ] or an increased offensive use of AI for manipulation or cyberattacks [ 49 ], essential. News media companies face the difficult task of navigating the possibilities and limitations of generative AI while maintaining their market position and upholding journalistic quality standards. In light of these potentially detrimental consequences of generative AI use [ 22 , 49 , 60 ], a systematic assessment of the impacts is a necessary element of any AI strategy. To account for the technological dynamics and societal complexities, such an impact assessment cannot be limited to analyzing the status quo but must be able to anticipate plausible future impacts as well.

The limitations and negative impacts of generative AI aren’t only an issue for the journalistic field though. Political and legal decision-makers have started to work on regulatory approaches to govern the impacts of AI systems in general, including the use of generative AI in the media sector. Recent regulatory initiatives such as the European Union’s draft AI Act or the Digital Services Act adopt a risk-based approach, thereby relying heavily on the ability of policy makers, regulators and regulated parties to be able to anticipate risks to fundamental rights and society [ 24 ]. The repeated references to “reasonably foreseeable risks” in the AI Act are a case in point, as are calls by the AI ethics community for more anticipatory studies on AI [ 17 , 51 ].

The responsible development, deployment and governance of AI requires new approaches to prospective research, i.e., research that can anticipate how AI will plausibly develop, what the ethical and societal consequences of this development might be, and how this can be proactively addressed by various stakeholders [ 12 , 50 , 72 ]. However, anticipating impact is a difficult task as there are an inevitable number of uncertainties that technology development and future prospection bring [ 13 , 50 , 53 ]. We do not know for certain how technology will develop, how consumers will use it, and what impacts it will have on society in light of potentially complex dynamics and feedback effects. For research in this area, the task is to make estimated projections of plausible future developments that justify AI’s implementation in practice [ 50 ].

To address these needs, in this paper we develop and refine an approach to study the potential impacts (and their mitigation) of the use of generative AI in the media environment. In particular, we utilize a scenario-writing method in an online survey among EU-member-state residents with a variety of different stakeholders of generative AI technology ( n  = 119), reflecting the roles and expertise sets of broad sub-groups of content creators, technology developers, and news consumers to collect cognitively diverse expectations of generative AI’s impact on the news environment. Furthermore, we also gauge respondents' ideas about mitigating the outlined harms as well as their opinion on the effectiveness of a specific policy proposal, namely transparency obligations as proposed in the EU AI Act.

In applying qualitative thematic analysis to the scenarios and the additional questions, we demonstrate how a scenario-based method can be a promising approach to conducting impact assessments, particularly due to its ability to create vivid projections and engage a diversity of perspectives. We highlight the alternating perspectives that different stakeholder groups bring in, stressing the need to ensure cognitive diversity in AI impact assessment. Further, we not only systematically map potential negative impacts, but also leverage the diversely sampled survey responses to help illuminate mitigation strategies to counter adverse consequences of generative AI. As such, our study contributes to the scientific literature on assessing AI impact, but also contains practical implications for policy makers or technology developers who are tasked with mitigating harms of such systems.

2 Related work

In this work, we address the aforementioned need for approaches to anticipate AI impacts. In the following subsections we examine the related work on anticipatory governance which motivates our study, the range of existing AI impact assessment approaches, the need for participatory approaches, and the background on the use of scenario-writing as the specific approach we leverage.

2.1 Anticipatory governance

The anticipation of the impact of technologies has been studied under the theoretical approach of anticipatory governance, which examines social impacts of technology in the early development phase [ 21 , 29 , 34 ]. One aim of anticipatory governance approaches is to help mitigate the uncertainty that inevitably surrounds emerging technologies [ 13 ]. In doing so, anticipatory approaches can illuminate the positive and negative aspects about the technology at hand [ 13 ] and are as such deeply connected to normative values of how society wants to deal with the respective technology [ 50 ]. Anticipatory approaches try to come to a point where technology development acknowledges potential risks and mitigate these beforehand [ 34 , 63 ]. Furthermore, anticipatory governance recognizes that the future can not be predicted per se and, thus, aims for showing possible future scenarios [ 56 ].

Methodologically, anticipatory governance deals with navigating the choice between different policy options and enabling the formation of a joint vision of how society wants to engage with technology [ 34 ]. Practically, anticipating the impact of emerging technology should be initiated when a technology “is sufficiently developed for meaningful discourse to be possible about the nature of the technology and its initial uses, but where there is still uncertainty about its future implications” [ 50 ]. In the deliberation process it is not about discussing certain future events, but to discuss how future scenarios align with (public) values. This depicts a translation process, in which underlying values in the form of reactions to exemplary scenarios are discussed that, in turn, are symbolic for plausible futures and can subsequently inform governance processes [ 50 ]. In making socio-technical consequences salient and agreeing on shared values for how the future ought to be, anticipatory governance enables empirical and value-oriented decision-making for stakeholders [ 34 , 50 ].

2.2 AI impact assessment

In a similar vein, scholars in the field of AI have proposed AI impact assessment to identify and anticipate the impact of AI technology [ 3 , 52 ]. Or, in the words of Selbst [ 63 ]: “An Algorithmic Impact Assessment is a process in which the developer of an algorithmic system aims to anticipate, test, and investigate potential harms of the system before implementation; document those findings; and then either publicize them or report them to a regulator”. In recent years numerous proposals on how to assess AI impact have been published (for an overview, see [ 68 ]). Impact assessments are particularly needed for novel technologies, where societal consequences have yet to be determined or are generally hard to measure [ 63 ]. That also means that impact assessments aim to identify risks that are not purely technical and are rather related to the sociotechnical nature of AI [ 52 ]. In conducting impact assessments, the approach can potentially detect “errors that would otherwise arise at unpredictable times and characterize performance in the long-tail of errors that is currently opaque” [ 3 ] (p. 134). Impact assessments refer to impacts that might affect individuals or groups of society [ 48 , 52 ]. In understanding AI as a sociotechnical system, impact classification should also not be limited to the technical components of AI systems, but should include the interplay between humans or society, respectively, and machines.

Existing AI impact assessment studies apply various methods to identify and categorize these impacts. One way to approach this is by conducting a literature review on existing studies focusing on AI impacts. Shelby et al. [ 65 ] performed a scoping review of the computer science literature to identify five groups of AI harms (representational, allocation, quality of service, interpersonal, social system). Hoffman and Frase [ 37 ], who distinguish between tangible and intangible harms, issues and events, and AI and non-AI-harms, develop their impact framework based on a review of AI incident reports and subsequent discussions with stakeholder organizations. Explicitly analyzing LLMs, Weidinger et al. [ 71 ] performed a multidisciplinary literature research of scientific papers, civil society reports and newspaper articles, to identify 21 risks that can be grouped into six categories including discrimination, exclusion and toxicity (e.g., representational harm), information hazards (e.g., data leaks), misinformation harms (e.g., disseminating misinformation), malicious uses (e.g., users actively engaging in harmful activity), human–computer interaction harms (e.g., manipulation), and automation, access, and environmental harms (e.g., environmental costs). In regard to text-to-image (TTI) technology (e.g., Dall-E, Midjourney), Bird et al. [ 8 ], relying on a literature review, distinguish between three broad risk categories (discrimination and exclusion, harmful misuse, misinformation and disinformation) and six stakeholder groups (system developers, data sources, data subjects, users, affected parties, and regulators) that build a framework for analyzing harms of TTI use.

Another way to apply impact assessment is to let users decide on the scenarios and impacts they want to focus on and provide them with a tool that helps them in mapping potential impacts. The AHA! (Anticipating Harms of AI) framework of Buçinca et al. [ 14 ] serves as a toolbox to create negative AI impact scenarios for different stakeholder groups. It requires the input of a user (e.g., developer) to first describe a deployment scenario and definition of potential risks to then systematically map out potential stakeholders and concrete impacts.

Another approach commonly taken in AI impact assessment is to collect the opinions of experts, sometimes also referred to as Delphi method. For instance, Solaiman et al. [ 67 ] conducted workshops with experts from different backgrounds, including researchers, government and civil society stakeholders as well as industry experts. As a result, they list trustworthiness and autonomy, inequality, marginalization and violence, concentration of authority, labor and creativity, as well as ecosystem and environment as potential evaluation categories for generative AI's impact on society and people.

However, all of these outlined approaches are top-down in the sense that they rely on domain experts or scientific viewpoints, an issue that has been acknowledged in potentially limiting the perspectives available [ 10 ]. What is currently missing are approaches that engage individual members of society and invite them to reflect on impacts without presenting them with a potential impact framework beforehand. In our study, using scenario-writing in a participatory foresight approach, we therefore pursue a bottom-up approach to help enrich the field of AI impact assessments.

2.3 Participatory foresight

In considering an anticipatory governance approach and in light of the prior work on impact assessment it is crucial to ask: whose prospections should guide anticipatory governance? An approach to leverage cognitive diversity in developing prospections is participatory foresight [ 12 , 54 ]. The idea of participatory foresight is to engage a diverse set of participants in anticipating future impact as studies have shown that expert foresight is prone to be biased [ 5 , 10 , 12 ]. Or, in the words of Metcalf and colleagues [ 48 ]: “The tools developed to identify and evaluate impacts will shape what harms are detected. Like all research questions, what is uncovered is a function of what is asked, and what is asked is a function of who is doing the work of asking." (p. 743) By establishing a deliberative social dialogue, a discussion about desirable futures can evolve that depict alternative visions of the future [ 54 ]. This also includes the perspectives of laypersons as they “bring the specialists’ knowledge down to earth and foresee its possible side-effects in everyday life. Thus they would illustrate the whole complexity of the social and cultural consequences, caused by the triumph of advanced techno-knowledge” [ 54 ]. In this way potential blind spots can be filled and pluralistic visions of the future can emerge that reflect the realities and circumstances of all involved—and not only one dominating group. Thus, participatory approaches can be seen as a way to democratize AI impact assessment [ 62 ].

Ultimately participatory foresight follows the goal to establish pluralistic perspectives from cognitively diverse individuals with different backgrounds, experiences, and expertise. Consequently, this will result in a far more nuanced and relatable picture of future AI impact. We stress that impacts of generative AI are not only defined in terms of technological issues, but also societal consequences like social inequalities, and human and infrastructural social impacts [ 67 ].

But how can society be included in anticipatory studies? In this work we develop an approach based on scenario-writing methods, which is outlined in the next section.

2.4 Scenario-writing

Scenario-writing can be defined as a method of anticipatory thinking, which can be used to outline a variety of different futures [ 2 , 3 , 11 , 15 , 64 ]. Scenarios describe in a creative way (e.g., through stories or motion pictures) future developments that are based on plausible and logical developments [ 2 , 11 , 32 ]. Importantly scenario-writing enables practitioners and researchers to envision a broad scope of different future alternatives [ 2 , 61 ]. As such, scenario-writing explicitly tries not to predict future events (as it is deemed as not possible), but acknowledges the uncertain character of the future and instead focuses on plausibility [ 57 ]. Further, scenarios can encompass a holistic, sociotechnical view that illuminates the relationship between different actors. Scenarios are placed in a real-word environment [ 2 ] and help “to reduce the overabundance of available knowledge to the most critical elements, and then blend combinations of those elements to create possible futures.” [ 15 ] (p. 49). Moreover, scenarios are especially suitable for identifying novel issues and impacts [ 2 ].

Börjeson et al. [ 11 ] distinguish between three categories of scenarios namely, predictive ( what will happen ?), explorative ( what can happen ?) and normative ( how can a specific target be reached ?) scenarios. In regard to anticipatory governance and AI impact assessment, explorative scenarios are most suitable for extrapolating future prospections from a starting point in the present and can serve as a “framework for the development and assessment of policies and strategies'' [ 11 ] (p. 727). Burnam-Fink [ 15 ] highlights that narratives are a common technique to make scenarios accessible to a broader audience. Thereby, ‘good’ scenarios, i.e. scenarios that are trusted and taken seriously by the audience (e.g., decision-makers), are characterized through a plausible story with compelling characters that makes future developments easy to understand [ 64 ]. This method has shown to be fruitful, for example as shown by Diakopoulos and Johnson, who used scenario-writing to explore the potential impact of deepfakes on the US presidential elections in 2020 [ 21 ]. Moreover, Meßmer and Degeling [ 47 ], in the use case of auditing recommender systems, discuss scenario definition as a crucial step to anticipate systemic risks.

3.1 Procedure and measurement

We conducted an online survey with an integrated scenario-writing exercise in which we recruited targeted sub-samples of EU residents, content creators, and technology developers (total N  = 156; n  = 52 participants for each group) living in member states of the European Union (EU). Crowdsourcing has proven to be a valuable method to identify diverse social impacts of AI systems [ 5 ], and the targeted sub-samples are intended to draw in different types of expertise and experience. Scenario-writing is an established method as well, which is used to capture prospections about the future [ 21 ] and can be implemented in surveys [ 11 ].

After receiving institutional ethics approval for our study, we created an online survey using the survey tool Qualtrics to assess the scenarios. The questionnaire was constructed in English language and structured as follows: First, respondents were introduced to the study’s objective and were informed about data collection and storage. After giving their informed consent to participate in the survey, respondents had to indicate some demographic information (gender, age, educational level, ethnicity, employment sector) as well as some information about AI related attitudes. Following this, participants were introduced to the scenario-writing exercise. Figure  1 shows the information we presented. Note that the survey instructions were the same for each respondent group, except for the main characters that respondents were asked to imagine. Respondents were tasked to write from the perspective of news consumers, and content creators as well as technology developers out of their respective expertise perspective. Following that, we offered participants some additional information on generative AI, outlining some of its capabilities (tasks generative AI can fulfill; media formats; examples), limitations (accuracy; attribution; biases) and trends. The full description of the technological information we gave to the respondents can be found in the “ Appendix ”.

Task description

In a last introduction step to the task, we provided respondents with information about the evaluation criteria of the scenarios. We highlighted that scenarios should be creative, specific, believable, and plausible as suggested by Diakopoulos and Johnson [ 21 ]. In addition to their base pay for completing the survey we offered a bonus payment of 2£ for the top 10% of scenarios as assessed by the authors according to these quality criteria. We tasked respondents to confirm that they understood the presented information and measured the time that respondents engaged with the introductory material.

On the following survey page, respondents typed in their scenario. To make the instructions clear, we repeated the instructions shown in Fig.  1 on the page. Additionally, respondents had the opportunity to click the “back” button and read the information about the technology and the evaluation criteria again. To discourage people from using generative AI tools to fulfill the task for them (which is a growing issue for certain kinds of online tasks [ 70 ]), we disabled copying text into the open text field such that respondents needed to type the story directly into the text box. Additionally, to secure adequate length of the scenarios, we set a minimum character number of 1,000. Participants could choose to write their scenario in English or their native language.

To gather respondents' thoughts about impact mitigation, we asked participants the following after submitting their scenarios: What could be done to mitigate the risks outlined in the scenario ? Please also indicate who you think should be responsible for mitigating the harm. This could be the characters in your story, but it could also be other people/organizations. Please write at least 50 words.

Additionally, we gathered respondents’ ideas about the potential influence of a policy approach that could be introduced to mitigate negative impacts, namely transparency obligations. We based this mitigation strategy on Article 52 of the draft EU AI Act [ 26 ]. We asked: Organization/persons that use generative AI systems have to fulfill the following legal obligation: Transparency/Disclosure: The use of generative AI must be made transparent, i.e. it must be clearly and visibly disclosed that generative AI was used in the creation of content. This can be done, for example through labeling. With the information provided, would the transparency requirement change your scenario ? Please write at least 50 words.

Lastly, respondents were asked to evaluate the scenario-writing task in regard to difficulties in the writing process and comprehensiveness of the introductory material. Afterwards, respondents were thanked and debriefed. We paid 11£ for participation in the study based on an estimate for the expected time for completion of the task and a reasonable wage. Footnote 1

3.2 Pre-test

To pilot our method and get feedback on the scenario task we conducted two in-person workshops with more than 30 participants. Based on workshop feedback we fine-tuned our research instrument in terms of task clarity as well as adjusting the content and the framing of the information presented. We then set up the online survey.

We pre-tested the online survey with 30 participants (news consumer sample) utilizing the survey panel provider Prolific. Data collection was conducted on July 21, 2023 with participants from one EU country (the Netherlands) and with a balanced sex distribution. Based on a close reading of the scenarios as well as the feedback of the respondents, we substantially revised the instructions for the scenarios. In particular, we decided to specify the role of the main character for each group (news consumer, content creator, technology developer) as respondents indicated having trouble imaging characters that they were not familiar with. Moreover, we restructured the presentation of the introductory material to make it more accessible. The changes resulted in the task as presented in Fig.  1 .

3.3 Study sample

In deploying the refined version of our survey, we collected 156 scenarios via Prolific. We collected 52 scenarios for each stakeholder group in the time span of August 3, 2023 to September 10, 2023. For news consumers, we chose to sample for EU residents as a large majority of people in the EU consume news information Footnote 2 and even those who do not formally consume it are still exposed to related information in the media environment. Content creators and technology developers were specifically selected using Prolific's sampling criteria. Content creators were defined as people that indicated at least one of their (current or former) employment roles as journalist , copywriter/marketing/communications role , and/or creative writing . For the survey of technology developers, we used the employment sector “ information technology ” provided by the survey platform as a selection criterion.

We decided to survey residents of all EU states since the EU, with the EU AI Act [ 24 ], on the one hand offers a joint approach to the development and use of AI, and, on the other hand, still leaves room for diversity given the cultural, political and socio-economical differences of the EU member states. Thus, we aimed for recruiting a variety of people living in a broad range of EU member states. To ensure diversity of EU member states, we grouped all EU countries into three groups based on the annual average salaries in the respective countries [ 27 ] and surveyed 17 respondents Footnote 3 in each group in each stakeholder group. Footnote 4 We chose this indicator because a soft launch (conducting the survey with a small subset of respondents to test its functioning) showed that a disproportionate number of respondents from low-income countries participated in the survey. This is plausible given that we decided to pay a fixed remuneration for taking part in the survey despite income differences in the EU member states that make that remuneration comparatively more or less valuable. To promote gender diversity, we used Prolific’s built-in feature to help balance the gender distribution of the sample for each sub-group. Footnote 5

Further, we only invited those respondents to participate in the survey, who indicated they speak English fluently. As in the pretest, the survey was conducted in English. However, participants had the opportunity to write their scenarios in their native language as we expected a higher quality of scenarios if participants felt comfortable with the language. We translated all scenarios using DeepL, a machine translation product with high performance. Automated translations have recently been established as a viable method in communication research [ 19 , 45 ] and we reason that any minor translation issues would not create a threat to the validity of our subsequent thematic analysis.

3.4 Data filtering

For data cleaning, first we identified scenarios that were thematically out of the scope of the task, i.e. that did not refer to the news environment or generative AI technology. Following this procedure, we removed twelve scenarios for the news consumer group, four from the content creator group and four from the technology developer group.

Second, although we took steps to prevent the use of LLMs in the scenario-writing exercise, we can not completely rule out the possibility of respondents using LLMs. Thus, we decided to flag scenarios with four criteria that could indicate such use. We filtered out those cases that were flagged from at least two of the criteria. First, we checked all scenarios for their likelihood of being written by an AI tool with GPTZero [ 69 ] Footnote 6 and flagged scenarios with a likelihood of being AI-generated of 50 percent or higher. Second, we flagged all scenarios that were written in under 20 min as this was determined in our in-person workshops as the minimum amount of time respondents needed to compose a reasonable scenario. Footnote 7 Third, we flagged those scenarios in which respondents read the instructions in under two minutes time. Again, the time flag was based on our experiences at the pre-test workshops. Fourth, we manually flagged scenarios as potentially being written by ChatGPT after reading them in detail. Individual flags are based on our own experiences prompting ChatGPT to write scenarios and the emerging patterns produced by those prompts. For example, if prompted with the instructions for our task ChatGPT often generates a very similar structure of output text. After applying these four flags and filtering those scenarios out with two or more flags, this resulted in an elimination of 17 scenarios, leaving us with 119 scenarios as the final sample for analysis.

Table 1 describes the sample statistics for each sub-sample. The samples predominantly consisted of white and well-educated respondents, a limitation we will return to in our discussion. Furthermore, men are slightly overrepresented in the content creator and technology developer sample. In addition, we managed to sample for a broad range of different countries.

3.5 Analysis methods

We analyzed the final set of scenarios using a qualitative thematic analysis approach including open and axial coding of themes. We constantly and iteratively used open coding techniques to gather excerpts of the scenarios, structure and typologize these and applied constant comparison to reevaluate the emerged codes and discuss them amongst the authors [ 20 , 31 , 46 ]. We additionally wrote memos to structure the interpretations of the findings.

In detail, the lead and second author of this paper read and coded 30 scenarios (10 for each stakeholder group) independently and derived the first set of open codes and impact classifications. Then, both authors compared and discussed their classification scheme and created an adapted and joint version of the classification scheme, which included the codes of both authors as well as the dimensions of “impact scope” and “agency”. Afterwards, the first author coded all remaining scenarios and enlarged the impact categorization scheme for new, emerging codes (impact themes and specific impacts). All corresponding quotes that were used for identifying a code were collected in a structured document, containing all impact themes and specific impacts. After coding all scenarios, the author team discussed and edited the categorization scheme. All authors read all excerpts and quotes derived from the scenarios and discussed the impact classification scheme and mitigation strategies classification scheme. After three extensive, consecutive meetings, agreement on the classification schemes were reached.

We structure our analysis along two themes. First, we will elaborate on the impacts enumerated in the scenarios. Second, we will outline mitigation strategies that were addressed by the respondents. In addition, we will also discuss the effectiveness of a specific mitigation strategy to counter the negative impacts of the scenarios: transparency obligations. In each section, we will compare the codes of the different stakeholder groups and highlight similarities as well as differences.

4.1 Enumerating impacts

The scenarios express a multitude of different impacts. Table 2 lists the various impact themes and the respective specific impacts that we observed, together with illustrative examples. In the following, we will elaborate on each of the impact themes. We also highlight differences and similarities between the stakeholder groups (i.e. news consumers, content creators, and technology developers) in how they raise awareness of the specific impacts.

In addition, we identified two dimensions that help to capture variance applied to different impact themes and are used to elaborate those descriptions below. First, we observed the impact scope of each specific impact. We define impact scope at three levels. Impacts can either occur at an individual (e.g., main characters), an organization (e.g., newsrooms), or a societal (e.g., political system) level. Where applicable, we discuss which impact scopes are (mainly) addressed within each theme. Further, we also identified agency as another dimension of impacts. We distinguish between causal agency, intentional agency, and triadic agency as proposed by Johnson and Verdicchio [ 38 ]. In their terminology, causal agency can be ascribed to AI systems as they can cause impacts, i.e. shape the world. However, AI systems, as they are technological artifacts, can not have intentions. Thus, the dimension of intentional agency can only be ascribed to humans. Intentional agency includes causal agency, but adds a layer that refers to a mental state of humans. As such intentional behavior can also cause impacts. Though, in terms of responsibility in an ethical sense, intentionality can be traced back on various mental states. For instance, stakeholders can act out of a malicious intent, but can also be negligent in a sense that they fail to anticipate negative impacts. They can also act in well-intentioned ways, but cause negative impacts. Intentional agency of human stakeholder can also be combined with causal agency of AI systems. This interplay is called triadic agency. Specifically, Johnson and Veridcchio describe it as follows: “Humans and artifacts work together, with humans contributing both intentionality and causal efficacy and artifacts supplying additional causal efficacy.” [ 38 ] In the following, if applicable to the impact themes, we will outline how agency plays a role in causing the impacts identified. We note that agency of characters can also be well intentioned and relating to mitigation strategies that are discussed in Sect.  4.2 .

4.1.1 Well-being

The scenarios described four different forms of impact on individual well-being. The mentioned mental impacts reached from negative emotions to severe mental illnesses (e.g., depression) that were caused by generative AI, for example, through online harassment based on fake news/images. Strongly connected to mental harm is also the impact theme addiction (e.g., to social media apps powered by generative AI). Reputational damage, for example, caused by a fake news campaign against a politician/journalist, but also a (news) organization, was also outlined in some scenarios. Reputational damage, then, was also oftentimes connected to mental harm. Lastly, some scenarios even pointed out physical harm (e.g., suicide) that was based on mental harm impacts caused by generative AI. All in all, well-being impacts were prevalent among the news consumer scenarios, whereas content creators and technology developers only addressed those impacts at a marginal level.

Well-being impacts mostly occur on an individual level as they are related to personal consequences for characters. However, some impacts also address the organizational level, for instance, if the reputation of a news company is damaged as a result of generative AI use.

4.1.2 Labor

Labor impacts are addressed frequently in the scenarios of all stakeholder groups. We found five sub-codes within the labor impact theme, namely competition , job loss , unemployment , loss of revenue , and changing job roles .

Some scenarios mention stronger competition due to the introduction of generative AI in the newsroom. Generative AI is expected to be a competitor as it replaces more and more tasks that were previously performed by humans. Competition, then, is highly interrelated with the notion of changing job roles. Some scenario-writers elaborate on economic pressure for content creators to learn new skills in order to adjust to the changes in the media environment. On the other hand, strong competition is also connected to the more severe impact of job loss that was one of the most prevalent codes among all scenarios in the sample. Scenarios frequently describe the fate of individual content creators that are pushed out of their job because of the use of generative AI. Consequently, these individual job losses can also be scaled up and address impacts at an organizational or societal level, and result in loss of revenue for (traditional) news organizations or potential unemployment. While job loss is connected to the individual level, the code unemployment addresses job losses at a macro scale in organizations (e.g., reduction of jobs in the newsroom and/or revenue loss) or societally in terms of the labor market (e.g., mass unemployment). Many scenario writers were worried about a loss of jobs in the media sector and described a future, where fewer jobs in the media sector are available as many are replaced by generative AI.

The labor impacts are highly interrelated with the loss of media quality. An often outlined connection is the economic pressure on journalists and/or journalistic organizations that lead to a frequent, and oftentimes unchecked, use of generative AI. This economic pressure coupled with the loss of human intervention in generating news, resulted in various journalistic quality issues that the scenarios described, like the loss of human touch, the influence of tech corporations, or the lack of credibility. Additionally, the economic impact on individuals (e.g., job loss) is also frequently connected to the well-being theme as characters suffer from the increased pressure or from financial shortages.

4.1.3 Autonomy

Another dimension that was predominantly found at the individual level, but was infrequently mentioned was connected to the relationship between humans and machines and the resulting impact on human autonomy, or independence to act. Specific instances of this sub-code were present in only a few scenarios, with news consumers mentioning loss of orientation and loss of human autonomy as potential negative impacts, while technology developers highlighted loss of control over AI. Impacts regarding the relation of humans and machines could not be found in the content creator scenarios.

4.1.4 Legal rights

Some scenarios described negative impacts in the legal domain. This theme contains impacts related to copyright issues, legal actions (e.g., lawsuits against characters), freedom of expression and the lack of regulation. The sub-codes either address the individual or the societal dimension. On the individual level, for example, content creators face the danger of their material being used for generative AI without their consent, which leads to copyright issues. On the other hand, some scenarios described legal actions because of the misuse of generative AI by some characters. On the societal level, a few scenarios outlined a lack of regulation that leads to the uncontrolled use of generative AI and associated other impacts like the spread of fake news. Altogether, legal rights impacts are only present in a few scenarios, but were mentioned in all stakeholder groups.

4.1.5 Media quality

One of the most mentioned impacts concerned the (loss of) media quality. Here, a plethora of different sub-codes emerged, namely accuracy issues, loss of human touch, sensationalism, credibility/authenticity, lack of diversity/bias, clickbait, journalistic integrity, reframing of narratives, attribution, distinction between journalism and ads, lack of fact-checking, explainability, superficiality, over-personalization, ethics, and accountability. Not all different sub-codes were present in all stakeholder groups (see Table  2 ), but the concern that media quality is endangered through the use of generative AI was present in nearly every scenario. Especially prevalent are concerns that are related to the inaccuracy of generative AI, which can lead to the dissemination of misinformation (which connects also to impact themes of political impact and/or social cohesion). Another topic that was frequently mentioned was the prioritization of easy and clickable news that was pushed with the use of AI. As such, sensationalism was a common concern amongst all stakeholder groups. Furthermore, biased news was also thematized in many scenarios that also relate to impacts on a societal dimension such as discrimination.

We also found that media quality issues emerge due to different reasons. Some scenario writers describe the emergence of media quality issues as a consequence that emerged through the negligence of actors, i.e. characters failed to anticipate negative impacts of generative AI. The causal agency of the AI system, then, can take various forms, for instance, through a lack of accuracy or sensationalism reinforced by the system's training. Some other scenarios see media quality issues as a result of economic competition (e.g., “Emily's struggle began with news organizations, seeking efficiency and cost reduction increasingly relied on AI-generated news articles.” [CC S13]) or as a consequence of intentional agency, specifically malicious use, by characters. Thereby, some characters are aware of potential quality issues, but they decide to take the risk nevertheless due to fear of job loss, economic competition, or personal ambition to work more efficiently. In this regard, some scenario writers also sketch moral dilemmas that characters face as they need to weigh up the pros and cons of using generative AI (e.g., “In fact, its [the generative AI; added by the authors] reliability could be very low, which makes Alejandro face a moral dilemma: should he make use of this technology to write his article or should he do his own research even if it takes more time and effort?” [CC S33]).

4.1.6 Security

Security impacts were seldom addressed in the scenarios, though when they were mentioned it was the technology developers who did so. We identified hacking and cybersecurity as sub-codes of the theme. In this theme, scenario writers outline potential lacks of safety that media organizations might have and that could be exploited by malicious actors. For instance, some scenarios describe hacker attacks on news organizations that, consequently, result in other impacts like the spread of misinformation.

4.1.7 Trustworthiness

On the level of the media system, the central consequence is a discussion about the trustworthiness of the media environment. Most of the scenarios outline that the trustworthiness of the media is reduced through the use of generative AI. This is expressed through the difficulty to discern between facts and fictional news. Other consequences are that people turn away from the news (media fatigue), consume low quality news, or show a tendency to distrust news altogether. Trustworthiness issues are most prominent in the news consumer scenarios, whereas the content creator scenarios focus more on the media quality aspects. Furthermore, trustworthiness issues are also highly connected to political impacts, especially to the spread of misinformation.

Trustworthiness issues occur either on an individual or on a societal level. Some scenarios describe how characters (e.g., news consumers) lose their trust in media content due to the spread of disinformation or elaborate on the struggle of characters to discern between generated and human written news articles. Some scenario writers also outline trustworthiness issues at a societal level and speak of an untrustworthy media environment, which is then also connected to political issues like manipulation. Impacts in the trustworthiness theme can, thus, be a result of intentional behavior by specific actors (e.g., political parties), or a consequence of the widespread use or malfunctioning of generative AI (causal agency).

4.1.8 Political

One of the most mentioned impact themes relates to the potential for political impact. The spread of fake news and misinformation was a central topic of the scenarios—over all stakeholder groups. Fake news, in the form of deepfakes, or factually false news, are perceived as a prevalent harm and oftentimes embedded in a specific setting, for example, in the context of elections, or news reporting about crises and wars. The spread of fake news is frequently connected to malicious intentions of specific actors that utilize generative AI for their purposes (e.g., “Pedro is cunning and embodies the bitterness of someone who doesn't look at the means to achieve his ends.” [TD S25]). While fake news was mostly connected to political issues, we note that some fake news scenarios also pick up on other topics like personal harassment, which is then connected to well-being. The dimension of fake news is also connected to purposeful manipulation (on a political level). In these scenarios, generative AI, mostly in the form of generating fake news, is frequently used to manipulate citizens’ behavior according to the will of a (mostly political) actor. Frequently, scenario-writers tie the political misuse of generative AI to right-wing and/or populist political parties and/or campaigns. Further, gaining an opinion monopoly was found as another negative impact consequence for the accelerated use of generative AI.

Again, political impacts can be found on the individual and societal dimension. On the individual dimension, it describes the susceptibility of news consumers to fake news or manipulation. Some scenarios, for example, point out how news consumers are misled by disinformation campaigns. Interestingly, a few scenarios also elaborate on specific population groups (e.g., old/illiterate people) that are highly susceptible to political misuse. Scenario writers also addressed the societal dimension of scaling up individual impacts and speculate, for instance, that political impacts can have consequences for election outcomes.

4.1.9 Social cohesion

Another central theme in the scenarios concerns social cohesion. The widespread use of generative AI, according to the fears of many scenario-writers, will lead to stronger polarization among the public. This polarization is also caused by the spread of fake news and misinformation on the political level. Polarization itself can also lead to real-world conflicts between societal groups. Related societal consequences outlined in the scenarios are a deepening of social divide, mistrust among societal groups, discrimination of minority groups, as well as stronger dissatisfactions within the population. These concerns are present in all stakeholder groups scenarios and are frequently addressed by scenario writers.

The social cohesion theme is mostly addressed at the societal dimension. Scenario writers, for example, describe high level impacts like fractured societies, polarization, hatred among communities, or international tensions. Interestingly, these impacts are mostly described as a result of other impacts, such as political and labor impacts that combine elements that, in the end, lead to social cohesion impacts.

Again, these impacts can be part of a consequence that is based on characters’ failure to anticipate negative impacts (negligence), for instance, the introduction of novel generative AI technology that promises to deliver news more efficiently and personalized, leads to further polarization. On the other hand, malicious actors can use generative AI to pursue their goals and actively strive for societal tensions. Again, the interplay between intentions of humans and the affordances of generative AI, then, causes the negative impact.

4.1.10 Education

Some scenarios also refer to educational impacts; whereas educational impacts were not frequently mentioned in total, they were articulated by every stakeholder group. Scenario writers speak of a loss of critical engagement as well as a lack of general literacy to deal with the negative impact of generative AI. Again, this dimension is related to several other impacts. For example, a lack of literacy leads to people's inability to discern factual and fake news or to the vulnerability for bad journalistic output. A general loss of literacy within the population can also be an outcome of the use of generative AI, as, so the concerns, the quality of journalistic content could decline on a large-scale and, as such, news consumers do not have the possibility to engage with high quality journalistic content.

4.2 Mitigation strategies

Many respondents, while not explicitly asked to do so, mentioned mitigation strategies for the negative impacts of generative AI on the news environment. These mitigation strategies are often linked to well-intentioned characters. A frequent theme among the scenarios, for example, was the brave (investigative) journalist that fought against the spread of misinformation (e.g., “Pablo is deeply motivated by the pursuit of truth and the belief of journalism is a matter of democracy. They strive daily to serve the public interest and hold those in power [accountable].” [TD S19]). Oftentimes, characters also joined forces to mitigate negative impacts of generative AI, for example, through collaboration. Another theme connected to well-intended mitigation was the invention of some technology solutions to strengthen high quality journalism or to prevent harm. Some characters are also internally motivated by normative values as they want to be on the good side, stand in for their beliefs, or protect their families and friends. In addition, some characters are acting out of democratic or public interest, for example, they want to make news more accessible.

For the analysis in this section, we also included the answers to the open questions following the scenarios (See Sect.  3.1 ) to develop the codes for mitigation strategies and transparency obligations.

4.2.1 Enumerating mitigation strategies

We identified four main codes for the mitigation strategies outlined in the scenarios: technological approaches, collective action to mitigate the negative impact of the use of generative AI, legal actions, and strategies that aim to restore journalistic quality.

The technological approaches that were suggested to mitigate (technological) shortcomings of generative AI encompassed a huge variety of techniques like updates and patches, oversight programs, automated fact-checking tools, fine-tuning of not-accurate models (e.g., self-correcting models), banning of hateful content and prompts, fake-news scanners, rigorous testing, and identity verification. Technological approaches to mitigate negative outcomes were by far most prevalent in the scenarios written by the technology developers. Some scenarios outlined in detail how the proposed tools would help in reducing the negative impacts posed by generative AI.

Collective action strategies were mentioned in all stakeholder groups and comprise the sub-codes of protest/social movement, public attention, public deliberation, and education. The most common theme among the scenarios was some form of social movement or protest that emerged as a reaction to malfunctioning generative AI or negative consequences of the use of generative AI (e.g., spread of fake news). Protests can take the form of raising public attention, and manage to involve citizens to exert pressure on technology companies and/or journalistic organizations. But not all scenarios involved public protest; some just referenced a rise of public attention or public deliberation as a more subtle form of public inclusion. Public deliberation, for example, was outlined as a mitigation strategy that highlighted people’s discussion about how generative AI should (not) be used. Gaining public attention, as a related code, relates to the attempts of actors to provide information about the negative impacts of generative AI in the public sphere. However, not all of these attempts, as outlined in scenarios, succeed. Public education was further discussed as a strategy to enable citizens and/or stakeholders to critically assess the changing media environment. In this context, some scenario-writers also ascribe responsibility to news consumers. According to some respondents, the audience also has the obligation to act and think critically and not blindly trust the news.

The legal actions outlined in the scenarios can be differentiated within the sub-codes of regulation and lawsuits. This mitigation strategy was mentioned with similar frequency in all three stakeholder groups. In the scenarios, regulation was mostly a result of the effort of well-intentioned characters made to create the conditions for good quality journalism. Connected with societal action, some scenarios described that regulators stepped in and enforced policies that, for example, foster transparency and accountability standards, user welfare, or stricter oversight of organizations. Other scenarios described lawsuits as a measure to counteract the consequences of some of the impacts outlined. For example, lawsuits can be targeted against specific people or groups that used generative AI with malicious intent.

Another frequently mentioned mitigation strategy concerns directly restoring journalistic quality. In this category, we identified sub-codes for responsible AI use, re-focusing on traditional journalism, fact-checking, accountability measures, investing in diversity, human oversight, and collaborations. Responsible AI use describes ways to ensure a thoughtful use of AI, for example, by following ethical guidelines. Several other related themes occur in this regard, like a need for journalists to fact-check their results continuously, ensuring diversity in the workforce or enforcing human oversight in the production of news. Collaboration between technology developers and people working in journalism is oftentimes proposed in the scenarios to restore journalistic quality. Interestingly, collaboration is foremost mentioned in the technology developers’ scenarios, who think of collaboration as a fruitful way to mitigate negative AI impacts. In addition, some scenarios also go one step further and propose a return to traditional journalism without the use of generative AI to avoid the negative impacts.

We further analyzed the answers to the open question that we asked after respondents submitted their scenario ( What could be done to mitigate the risks outlined in the scenario ?). We detected a substantial similarity between the codes that already emerged in the scenarios and the open answers (see superscripts in Table  3 that distinguish where codes were mentioned), but also found that some themes were more accentuated in the open answers. The most prevalent mitigation strategies mentioned in the open questions were regulation, the development of ethical guidelines that are connected to a responsible use of generative AI, the need to strengthen public education, and refraining from the use of generative AI at all. Additionally, some new codes emerged: we found more sub-codes in the areas of legal actions and restoring journalistic quality. Interestingly, also new suggestions for governance interventions were made that are not to our knowledge part of the present regulatory discourse around the European AI Act, such as restrictions on access to the technology for vetted personnel only, not using generative AI for particular forms of journalism (e.g., news coverage about political topics and/or war) but also the creation of “an organization with representatives of all the players involved, from companies and civil society, which would define and create a standard for conduct and behavior for AI technology to be developed, trying the best they can to eliminate biases and discrimination” (TD S43).

For legal actions, we identified various calls for independent oversight. Scenarios suggested, for example, an independent oversight organization and/or NGOs. This regulatory body should see to it that generative AI does not lead to detrimental consequences like the spread of fake news. Additionally, some respondents go one step further and plead for a restriction of the use of generative AI either a) by controlling access, i.e. that only some actors are eligible to use this technology, or b) completely banning the use in specific application areas like the news environment. Also on a structural level, a few respondents propose shifting the power balances in a way that profit-oriented corporations are limited in their ability to push their agenda, i.e. are limited by regulatory boundaries. Lastly, copyright protection was mentioned by some respondents; this was especially prevalent in the content creator group where the question of ownership of training data and/or the output is most vital to their daily work.

On the level of restoring journalistic quality, the sub-codes transparency mechanisms, training, and creation of new (specialized) jobs emerged. Transparency was often mentioned as a strategy to ensure the responsible use of generative AI; this code was also often connected to independent oversight and the need for human oversight. Transparency, for example, could be practically achieved with labeling AI generated content (e.g., with watermarks), or a clear communication of the sources and data used for training generative AI. In addition, training of people working in the media environment and the creation of new (fact-checking) jobs were described by a few participants as suitable measures to enhance journalistic integrity.

4.2.2 Evaluation of transparency obligations

While the open question invited participants to deliberate relatively freely potential mitigation strategies, we were also interested in respondents' opinion on a specific policy proposal, namely transparency obligations as proposed in article 52 of the draft EU AI Act [ 26 ]. According to the proposed Article 52 of the draft EU AI Act, consumers have a right to know whether they are interacting with an AI as well as a right to know if content has been artificially generated or manipulated.

We evaluated the open answers regarding the expected effectiveness of the transparency obligations to mitigate the risks outlined in their scenarios. We detected seven categories: high effectiveness ; partial/conditional effectiveness ; change of the scenario, but similar outcome ; unsureness about the effectiveness ; small effectiveness, but overall not really important ; no effectiveness at all ; and not applicable for the proposed scenario .

Overall, in each group the major share of respondents welcomed transparency obligations and expected some level of effectiveness. In fact, high effectiveness was the most frequent answer in every stakeholder group with 13 (34%) answers in the content creator group, 17 (44%) answers in the technology developer, and 13 (39%) answers in the news consumer group. The high effectiveness of the transparency obligations was mostly linked to the prevention of the spread of fake news as such an obligation would help to clearly label automatically generated content and offer people the opportunity to evaluate content more thoroughly. Strengthening news consumers' awareness was thus frequently mentioned in combination with a high effectiveness. It is also connected to enhancing journalistic quality in a way that it is deemed more trustworthy and helps in distinguishing real and fake content. Some respondents also outline that the scenario they described, would not have taken place from the beginning. For example, ensuring transparency would effectively combat the spread of disinformation as content would be clearly identified as artificially generated and, thus, news consumers would not be misled easily.

Additionally, seven respondents (18%) of the content creator, seven respondents of the technology developer (18%) and nine respondents (27%) of the news consumer group rated transparency obligations as partially effective. The common theme of the answers in this category is that transparency is a step in the right direction, but would not solve all harms caused by generative AI. For example, some respondents believe that transparency labeling is not very effective as news consumers get used to it—some of the answers compare it with warnings on cigarette packs that are judged as useful for only some part of the population, but not for all. Others also indicate that this would not keep content creators away from using generative AI as it is still much cheaper and less time intensive than writing own stories. Furthermore, some respondents, while generally welcoming transparency obligations, question the practical enforcement of it. They doubt that it can be usefully implemented and question the enforceability of such an obligation. Other respondents doubt that news consumers perceive labeling as important as they believe that there is a tendency in the public to trust in the output of algorithms anyway.

However, we also found a sizable portion of respondents, who indicated that transparency obligations would not be effective in counteracting the harms of generative AI. This view is most prevalent in the content creator group with eleven mentions (29%), while only seven technology developers (18%) and five news consumers (15%) expressed this view. The transparency obligation is judged as not effective because of several reasons. First, some respondents do not think that news consumers actually notice labeling of AI generated content either because they skip it or are not paying attention to it. This is also related to the argument of getting used to warning labels that was also mentioned in the partial effective answers. Second, respondents doubt that transparency obligations can be practically enforced. Third, some respondents outline that news consumers would just not care about generative AI created content at all. They portray news consumers as mostly passive and without the ability to critically assess news (anymore). Fourth, some respondents remark that malicious actors simply would not care about transparency obligations and will continue to exploit generative AI for spreading fake news or misinformation. This is also connected to the lack of enforcement that some respondents mention.

Besides the former answers, some respondents answered that they were not sure about the effectiveness of the transparency obligation or report that it would not matter for their scenario, for example, because it is already transparent that generative AI was used. In addition, some respondents believe that the transparency requirement would change the scenario, but nevertheless lead to the same outcome, for example, because malicious actors act the way they do regardless. Finally, a few respondents ascribe the transparency obligation only a small effect, but at the same time doubt that it will have a positive long-term effect.

5 Discussion

In the paper, we developed and refined a method to anticipate impacts as well as mitigation strategies for the use of generative AI in the news environment. By inviting different groups of stakeholders to anticipate future impacts of a particular technology, the impact assessment benefits from the insights, expertise and situated experiences of different groups in society. In so doing our more participatory method provides an alternative to predominant methods of impact assessment that are expert driven or grounded in literature reviews of established impacts. In this context, scenario writing is a tool to trigger engagement and reflection as well as sharing participants' own perspectives. Besides identifying new themes of impacts and differences between the kinds of impacts and mitigation strategies between the different stakeholder groups, the method also produces information about the causes of negative impacts in the form of character agencies, i.e. elaborating on stakeholders intentions that may lead to a specific impact. By helping to map the space of action and agency in scenarios the method further sets the stage for future ethics work such as downstream responsibility analysis [ 28 ] or the discussion of varying mitigation strategies per stakeholder group. In the following, we will discuss how scenario writing can serve as an impact assessment tool as well as a tool in current governance approaches or policy development. In addition, we will discuss the limitations and propose further research that could be built on our study.

5.1 scenario-writing as impact assessment tool

Our study provides rich insights into individual stakeholders’ anticipations of the negative impact of generative AI on the news environment. Generative AI already has far-reaching impacts on individuals and society, which will increase even further in the future. Identifying potential negative impacts—and with that informing anticipatory governance—can help in developing strategies on how to prevent this harm as it is easier and more affordable to implement changes earlier in the development and implementation process of emerging technologies. What is more, our method taps into the perceptions and anticipations of the public and, thus, engages a unique perspective that is currently underrepresented in AI impact assessment literature. There, the focus is predominantly on expert- or literature-led approaches [ 68 ]. As such, our findings provide valuable insights into a broader societal perspective on generative AI [ 34 ]. Especially due to the different roles of the respondents in regard to their interaction with the news environment, either as news consumer, technology developer, or content creator, the findings revealed a variety of perspectives and identification of impacts, but also mitigation strategies, indicating that risks but also mitigation strategies are not one-size-fits-all. As aimed for in the AI impact assessment literature [ 48 , 52 ], the findings also illustrate that use of generative AI is clearly not only and maybe not even most predominantly about individual harms, but also societal harms—whereas the societal harms are far less subject to regulation [ 66 ]. Furthermore scenario-writing enables us to tap into the socio-technical interplay [ 52 ] and identify impacts that would otherwise be opaque [ 3 ]. Thus, an important contribution of our approach is that we expand from a predominantly technical focus on the technology itself to anticipations on how this technology could actually be used in real-world settings by various stakeholder groups.

Compared to other existing AI impact assessment frameworks, we were able to identify some similarities (e.g. references to (mis)information harms, the role of different forms of agency including intentional misuse, security issues, etc.), but also some new aspects that were not identified beforehand. For example, current AI impact assessment of LLMs [ 71 ] and text-to-image technology [ 8 ] have a strong focus on societal and consumer impacts, but with the inclusion of perspectives of content creators and technology developers, we were also able to identify negative impacts that focus on the economic situation and the corresponding moral trade-offs of people actively working in this sector. In addition, in contrast to the related work on impact assessment, the impacts that were apparent in the scenarios were far more contextually meaningful, getting at quite specific ways that generative AI could impact media from personal well-being implications and the need to think about education, to trustworthiness, political implications, and broader concerns of social cohesion. Rather than examine only what impacts technology causes on people, the method allows for a fuller exploration of the sociotechnical interactions where impacts can arise (e.g., labor considerations, social cohesion). We suggest that the method developed can be tuned both based on the instructions and scope of the scenario-writing, as well as the specific sample of participants, in order to get far more fine-grained and contextually meaningful impacts than is apparent from methods relying only on experts.

While existing impact assessment methods tend to focus on mapping the potential risks, using our method we were able to illuminate the rationale and motivations of actors using and responding to generative AI—again highlighting the socio-technical perspective that this method brings to the table [ 52 ]. This offers the potential to also inform an eventual analysis of agency or responsibility, whether ethical or for informing formal regulatory approaches. As shown in the findings, characters in the media environment exhibited different types of agency. In the triadic agency sense, generative AI is only the tool (causal agency) that contributes to a negative impact. However, impacts always trace back to the intentions of characters. To discuss ethical responsibility, focusing on the different forms of intentions, then, makes a difference. For example, we found that characters in the scenarios acted out of malicious behavior (e.g., purposefully spreading fake news), but other impacts emerged mainly due to negligence (e.g., characters failed to anticipate technological malfunctioning), or describe the complex interplay between characters and generative AI (e.g., economic pressure on organizations/journalists lead to the use of generative AI that causes negative impacts). Some scenarios made also apparent the moral struggles that the use of generative AI can bring with it. The scenario-writing method therefore offers specific examples that can help fuel an (ethical) discussion about agency and responsibility.

The scenarios offer vivid examples of unique perspectives that are currently missing in high-level description and categorization schemes of AI impact assessments. These unique perceptions are also based on the concrete role that individuals take in the news environment. This can be traced back to the different roles these groups take in regard to generative AI. For news consumers, impacts regarding well-being are more prevalent as they imagine their characters in a user-setting, in which they describe how individuals interact with the emerging technology. Likewise, news consumers are concerned about the trustworthiness of the media content they consume. Technology developers bring their unique perspective when it comes to the articulation of safety issues. This is a rather technical perspective, which relates to their profession on how to build generative AI systems that are trustworthy and safe to use. This impact category is therefore also role-specific. Content creators also frequently highlight impacts that are related to their profession. We showed that content creators were overall concerned about generative AI’s impact on the media quality, highlighting diverse specific issues that could be endangered by the use of generative AI. As professionals, their scenarios describe unique and diverse possible impacts that could not be found (in such richness) within the scenarios of the other stakeholder groups. As this diverging presence of codes in each stakeholder group shows, news consumers, technology developers, and content creators raise awareness on different impacts, thus highlighting the value of sampling for cognitive diversity and a range of expertise and thus supporting the participatory foresight approach utilized in this study [ 12 , 54 ]. At the same time, we also acknowledge that some impact categories were equally mentioned in all stakeholder groups. These impact categories can be seen as issues of common concern that respondents are aware of regardless of their role in relation to the use of generative AI in the news environment. Those impact themes are labor, politics, legal rights, education, and social cohesion. This can be explained by the specific topics that these categories entail. As, for instance, disinformation and the potential threat for jobs are highly discussed in the public debate, it is not surprising that all stakeholder groups thematize these impacts. In addition, impact themes such as legal rights, education, and social cohesion are themes that are not specific to stakeholder’s roles and are, thus, mentioned equally across the groups.

At the same time as we see benefits to this method we also suggest there is still value in expert-driven impact assessment tools as some impacts identified by experts were not found in the scenarios in our sample. For example, the environmental costs of generative AI [ 9 , 18 , 35 , 67 , 71 ] are not mentioned in any of the scenarios, which corresponds to studies that highlight the unawareness of citizens and the public debate with the environmental costs of AI [ 1 , 42 , 44 ]. This also exposes a limitation of the method insofar as our task description specifically oriented respondents’ attention towards impacts on the media ecosystem, and in this case no respondent saw the connection to environmental concerns. Our approach can thus be seen as complementary to existing expert-led approaches and also subject to how the task is framed and presented to respondents.

5.2 Scenario-writing for impact assessments and policy development

Our study also demonstrated that scenario-writing can help not only in identifying individual and societal impacts but also mitigation strategies—a crucial component of anticipatory governance research [ 34 , 63 ] and AI impact assessment [ 3 , 52 ]. The fact that our respondents discussed mitigation strategies even without being actively asked to do so demonstrates how the scenario-writing exercise resulted in active engagement and stimulated critical thinking in the respondents. This also suggests the potential of using scenario-writing not only for negative impact identification, but to help develop mitigation strategies that are grounded in the experience of different affected stakeholder groups and that can again inform and inspire policy options. Any policy intervention is only as good as its enforceability, and information on what mitigation strategies stakeholders themselves consider effective can inform policy makers on what mitigation strategies are more likely to be supported ‘on the ground’. This could serve as a valuable addition to the usually expert driven and top-down methods to develop mitigation strategies. We identified technological approaches, collective action, legal action, and restoring journalistic quality as overlying mitigation strategies. Again, we could find some differences between the respondents’ role in regard to generative AI. Technology developers brought their professional experience in and highlighted technological fixes for malfunctioning generative AI systems as well as a strengthening of collaboration between journalists and technological experts. On the other hand, content creators emphasized copyright protection as an important mitigation strategy. As these findings show, personal and professional experiences informed also the proposal of mitigation strategies. Thus, sampling for cognitive diversity provided not only useful insights for the identification of impacts, but also for how these impacts could be mitigated.

The findings in our study also revealed novel ideas to mitigate harms that are currently not discussed prominently in existing policy debates around the AI Act. Examples for such policy options include the restriction of access to generative AI only for vetted personnel and the creation of an oversight organization for generative AI use in the news environment consisting of representatives of different stakeholder groups. The responses also made clear that there is not one single mitigation strategy, but that in order to mitigate the potential negative impacts from AI, a combination of different strategies is needed and reflects the societal complexity in which generative AI functions. Our approach and findings thus contribute to the identification of “reasonably foreseeable risks”, as called for in the draft EU AI-Act. Policy-makers have already deployed scenario-writing as a method to anticipate the impacts of new technologies on society and/or specific domains like journalism [ 25 , 39 ]. We add an academic perspective to the current approaches and utilize a diverse sampling to provide even more information for political decision-makers. The scenarios and the mitigation strategies can serve as a starting point for impact assessments, and to engage actively with policy-makers about possible mitigation measures. The added contribution of this particular paper is testing scenario-writing as a form of bottom-up impact mapping, and as the basis for future work on a critical analysis of existing regulatory approaches. The work is also useful in identifying the breadth of possible mitigation strategies and how these may differ between stakeholder groups. Future work could follow up with (selected) scenarios and/or mitigation strategies and discuss to what extent current governance approaches already address the concerns and proposed mitigation strategies, or where doing so could be a viable route for future policy development. For instance, our findings hint at policy areas, which are important for many respondents. Here, especially the strong emphasis on economic consequences for affected stakeholders can be highlighted; an issue, while recently addressed by some political decision makers and policy white papers (e.g., [ 7 ]), that is still less prominent in current policy debates and in scholarly research on generative AI ethics than other issues (e.g., fairness, safety, or toxicity) [ 36 ]. Consequently, this seems to be a crucial dimension of ethical impacts that needs to be addressed in future policy discussion.

It is interesting that individuals in the scenarios focus on collective social action as a mitigation strategy, whereas regulatory approaches, such as the AI Act focus much more on individual rights and protected interests, and far less on how to enable collective action or involvement of civil society. Insights like these beg the question how AI governance approaches could support collective action as a counterweight to the power and potential of AI in society. Utilizing scenario-writing to tap into lived realities of affected individuals as a means of revisiting some existing policy debates might thus be a useful practical addition to inform policy debates.

The transparency questions yielded further insights into how a specific policy action could help (or not) in mitigating impacts caused by AI. While there is a general tendency to approve and welcome the transparency obligation, content creators are a bit more skeptical about its effectiveness than respondents of the other groups. This skepticism is attributed to a lack of enforcement mechanism, or a general disbelief in the capabilities of the public to actually pay attention to transparency measures. All in all, the open answers to this question provide additional insights that can help decision makers deliberate about implementing and operationalizing transparency mechanisms. Furthermore, the variety of governance approaches that were hinted at as respondents talked about mitigation strategies could be subjected to the same kind of targeted evaluation as we did here for transparency as part of future work.

5.3 Limitations and outlook

There are some limitations that have to be acknowledged. First, our sampling strategy was aimed to ensure diversity in regard to respondents’ country of residence in the EU and gender distribution. However, our sample ultimately consisted predominantly of people who identify themselves as White and well-educated. As frequently pointed out in the scholarly debate about access and participation, voices from marginalized communities are direly needed as they bring in unique perspectives and raise awareness on aspects that are often overseen or neglected by the industry, developers or some scholars [ 18 , 52 , 59 ]. However, public opinion research also reports that it is difficult to reach these groups as they are particularly uninvolved in the public discourse on AI [ 4 , 43 ]. Future research should develop approaches that particularly aim to include the voices of those communities that are usually not well presented in the public debate as well as the scholarly debate about AI impacts. This could be achieved through targeted sampling in surveys with additional attention given to sampling on dimensions of factors such as gender identity and race, or workshops developed in collaboration with NGOs and interest groups.

Additionally, further research should develop approaches to validate and evaluate the findings of the scenarios. As of now, scenarios are tied to the imagination of the respondents and are not necessarily fully plausible from a technical, legal, or societal viewpoint. For example, a total ban of generative AI tools may conflict with citizens’ fundamental right to freedom of expression or economic freedoms. Banning generative AI altogether is, thus, not a viable suggestion. Consequently, validation and synthesis is a crucial next step to make scenarios useful for the policy debate about impact mitigation. Further, besides validation, the scenarios can also be evaluated in terms of different variables of interest. For instance, an insightful further dimension for impact assessment would be to rate the severity of the impacts or the likelihood of occurrence. This could be achieved through (expert) workshops, or Delphi studies that synthesize multiple expertise profiles to assess viability. Another option would be to conduct a quantitative survey among news consumers (or stakeholder groups) and let them evaluate other people’s scenarios or refined scenarios that are carefully constructed based on our findings, respectively. A quantitative validation could aim for different aspects such as plausibility of the scenarios, severity of impacts, likelihood of an impact materializing, but also (individual/societal) desirability of scenarios or easiness to understand. Further, also proposed policy options that emerged as a result of this study could be evaluated for their feasibility and effectiveness. This approach was recently used by Dobber et al. for the use case of veracity labeling in political advertising [ 23 ].

Further research can build on our groundwork and dig deeper into the notion of cognitive diversity. Relying on studies of AI narratives and imaginaries lead to the assumption that socio-demographic and AI-related factors have an influence on the emerging scenarios [ 16 , 18 , 40 , 59 ]. Tapping deeper into the underlying factors that influence the creation of the scenarios can further provide a better understanding of possible risk dynamics but also result in more diversity in the results—and better position the voices of particular stakeholder or minority groups. Anecdotal findings from our scenarios indeed point out that these factors matter in terms of scenario-writing: For example, we found that respondents from Poland thematized Russia's war of aggression on Ukraine in violation of international law and, in light of this, connected AI impacts to this topic, e.g. the rise and spread of disinformation in a political setting. However, our sample size is not suitable for a systematic and thorough analysis between respondents with different socio-demographic information (e.g., country of residence). Exploring those differences is a promising research avenue for future scholarly work.

In a connected world, the anticipation of generative AI’s impact in the news ecosystem is a global challenge. Especially in light of the potential negative impacts of generative AI on elections (like the upcoming US and EU elections in 2024), we need knowledge on how potential impacts can unfold and—even more importantly—how they could be addressed. Thus, there is a need to update and expand our approach to the global scale. Including residents of other countries beyond the EU might result in the identification of different impact classifications since political, cultural, and socio-economic factors influence the perspectives and imaginations of generative AI’s impact in these countries as well as perceptions regarding AI technology [ 41 ]. Anticipating impacts is also relevant in light of the emerging regulatory frameworks like the EU AI Act, Biden's Executive Order on AI, but also for countries that are in the process of developing legal frameworks on AI. Identifying potential detrimental impacts of generative AI such as those presented in this study could inform policy-making processes and shine light on issues that need to be addressed by legislators. These strategies, however, should also be informed by studies with residents of the respective countries. Our study offers an approach that can be applied by researchers as well as political decision makers tasked with developing governance strategies.

6 Conclusion

In this work we systematically anticipated and mapped the impacts of generative AI as well as corresponding mitigation strategies and a concrete policy proposal currently under discussion, namely transparency obligations as outlined in Article 52 of the draft EU AI Act. In applying scenario-writing we delve into the cognitively diverse future imaginations of news consumers, technology developers, and content creators. Our findings show that scenario-writing via diverse sampling on a survey platform is a promising approach for anticipating the impact of generative AI and related mitigation strategies. Further, different stakeholder groups raise awareness of a variety of potential impacts based on their own unique perspectives and expertise. In detail, we identified ten impact themes with fifty specific impacts, whereby the negative impacts of generative AI on media quality as well as economic impacts dominated. In regard to mitigation strategies, we identified four main categories with twenty specific strategies, including some that were novel to existing governance strategies. In addition, transparency obligations are seen as a viable measure to address some of the potential harms of generative AI.

We based our remuneration on the minimum wage in the Netherlands where the pre-test was conducted.

https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20220824-1 .

We approved three additional scenarios from respondents that, due to a technical error, were not counted as completed by Prolific. We paid the respective respondents and added the scenarios to the corpus, thus, resulting in a sample of 156 scenarios.

Low income group: Bulgaria, Croatia, Czech Republic, Greece, Hungary, Latvia, Poland, Romania, Slovakia; Medium income group: Cyprus, Estonia, Italy, Lithuania, Malta, Portugal, Slovenia, Spain; High income group: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Luxembourg, Netherlands, Sweden.

Prolific only offers “sex” as a filter variable for equal distributions among subsamples, using the response to the question: ‘What is your sex, as recorded on legal/official documents?' Participants answer this question with one of two options: [Male/Female]’ https://researcher-help.prolific.com/hc/en-gb/articles/360009221213-How-do-I-balance-my-sample-within-demographics . Although Prolific’s system only operates according to binary sex, in order to ensure representation of non-binary people, we included a more inclusive query for respondents’ gender, including the option “non-binary”, “prefer to self-describe” and “prefer not to answer”. We used this measure to describe our sample (see Table  1 ).

GPTZero is mostly trained on English language text and, thus, is less accurate for other languages. However, we assume that, if respondents decided to use LLMs to write their scenarios, the output text would most likely be in English as (a) copying the task description and running it provides an English output text and (b) even if they provide a task description before inserting our task description still provides an English output. Thus, participants would need to either specify that they want the LLM output in their native language or translate our task description before inserting it in a prompt. We believe that both ways are too time-intensive, especially for respondents that aim to reduce workload by using LLMs.

We also applied a statistical approach in determining outliers. However, given the skew in the data (many high outliers above the average time score), applying a statistical approach like mean-2SD resulted in negative time values and, thus, was not possible for our study.

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1.1 Introduction to generative AI

Generative AI is a technology that can create new content (e.g. text, images, audio, video) based on the content it was trained on.

1.2 Capabilities

Generative AI for text can be used to rewrite, summarize, personalize, translate, or extract data based on input texts. It can also be set up as a chatbot that end-users can interactively communicate with, and can be incorporated into other technologies like search engines.

Generative AI can also create images or videos.

These AI systems can be controlled using text-based prompts which provide task instructions and input data. For instance, you could prompt it with:

“Write three distinct headlines for the following news article: <article text>”

“Summarize the following text: <article text>”

“Translate the following text into English: <article text>”

“Explain <issue> in easy to understand language”

“Create an image/video showing <description of image>”

1.3 Limitations

Accuracy: This technology does not always output text that is accurate.

Attribution: This technology can’t accurately include footnotes or citations for information sources it uses to create its responses.

Biases: The outputs from this technology can be biased based on the data used to train the system, which typically reflects common societal biases (e.g. racial or gender).

This technology is already accessible by more than 100 million people and access to it by all types of people will only continue to increase.

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Kieslich, K., Diakopoulos, N. & Helberger, N. Anticipating impacts: using large-scale scenario-writing to explore diverse implications of generative AI in the news environment. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00497-4

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

Shots - Health News

Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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Binge drinking is a growing public health crisis − a neurobiologist explains how research on alcohol use disorder has shifted

Assistant Professor of Biology, Biomedical Engineering and Pharmacology, Penn State

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Nikki Crowley receives funding from The National Institutes of Health, The Brain and Behavior Research Foundation, and the Penn State Huck Institutes of the Life Sciences endowment funds.

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With the new Amy Winehouse biopic “Back to Black ” in U.S. theaters as of May 17, 2024, the late singer’s relationship with alcohol and drugs is under scrutiny again. In July 2011, Winehouse was found dead in her flat in north London from “death by misadventure” at the age of 27. That’s the official British term used for accidental death caused by a voluntary risk.

Her blood alcohol concentration was 0.416%, more than five times the legal intoxication limit in the U.S. – leading her cause of death to be later adjusted to include “alcohol toxicity” following a second coroner’s inquest.

Nearly 13 years later, alcohol consumption and binge drinking remain a major public health crisis , not just in the U.K. but also in the U.S.

Roughly 1 in 5 U.S. adults report binge drinking at least once a week, with an average of seven drinks per binge episode . This is well over the amount of alcohol thought to produce legal intoxication, commonly defined as a blood alcohol concentration over 0.08% – on average, four drinks in two hours for women, five drinks in two hours for men.

Among women, days of “heavy drinking” increased 41% during the COVID-19 pandemic compared with pre-pandemic levels , and adult women in their 30s and 40s are rapidly increasing their rates of binge drinking , with no evidence of these trends slowing down. Despite efforts to comprehend the overall biology of substance use disorders, scientists’ and physicians’ understanding of the relationship between women’s health and binge drinking has lagged behind.

I am a neurobiologist focused on understanding the chemicals and brain regions that underlie addiction to alcohol . I study how neuropeptides – unique signaling molecules in the prefrontal cortex , one of the key brain regions in decision-making, risk-taking and reward – are altered by repeated exposure to binge alcohol consumption in animal models.

My lab focuses on understanding how things like alcohol alter these brain systems before diagnosable addiction, so that we can better inform efforts toward both prevention and treatment.

The biology of addiction

While problematic alcohol consumption has likely occurred as long as alcohol has existed, it wasn’t until 2011 that the American Society of Addiction Medicine recognized substance addiction as a brain disorder – the same year as Winehouse’s death. A diagnosis of an alcohol use disorder is now used over outdated terms such as labeling an individual as an alcoholic or having alcoholism.

Researchers and clinicians have made great strides in understanding how and why drugs – including alcohol, a drug – alter the brain. Often, people consume a drug like alcohol because of the rewarding and positive feelings it creates, such as enjoying drinks with friends or celebrating a milestone with a loved one. But what starts off as manageable consumption of alcohol can quickly devolve into cycles of excessive alcohol consumption followed by drug withdrawal.

While all forms of alcohol consumption come with health risks, binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain. For example, for some people, alcohol use can lead to “ hangxiety ,” the feeling of anxiety that can accompany a hangover.

Repeated episodes of drinking and drunkenness, coupled with withdrawal, can spiral, leading to relapse and reuse of alcohol. In other words, alcohol use shifts from being rewarding to just trying to prevent feeling bad.

It makes sense. With repeated alcohol use over time, the areas of the brain engaged by alcohol can shift away from those traditionally associated with drug use and reward or pleasure to brain regions more typically engaged during stress and anxiety .

All of these stages of drinking, from the enjoyment of alcohol to withdrawal to the cycles of craving, continuously alter the brain and its communication pathways . Alcohol can affect several dozen neurotransmitters and receptors , making understanding its mechanism of action in the brain complicated.

Work in my lab focuses on understanding how alcohol consumption changes the way neurons within the prefrontal cortex communicate with each other. Neurons are the brain’s key communicator, sending both electrical and chemical signals within the brain and to the rest of your body.

What we’ve found in animal models of binge drinking is that certain subtypes of neurons lose the ability to talk to each other appropriately. In some cases, binge drinking can permanently remodel the brain. Even after a prolonged period of abstinence, conversations between the neurons don’t return to normal .

These changes in the brain can appear even before there are noticeable changes in behavior . This could mean that the neurobiological underpinnings of addiction may take root well before an individual or their loved ones suspect a problem with alcohol.

Researchers like us don’t yet fully understand why some people may be more susceptible to this shift, but it likely has to do with genetic and biological factors, as well as the patterns and circumstances under which alcohol is consumed.

Women are forgotten

While researchers are increasingly understanding the medley of biological factors that underlie addiction, there’s one population that’s been largely overlooked until now: women.

Women may be more likely than men to have some of the most catastrophic health effects caused by alcohol use, such as liver issues, cardiovascular disease and cancer . Middle-aged women are now at the highest risk for binge drinking compared with other populations.

When women consume even moderate levels of alcohol, their risk for various cancers goes up, including digestive, breast and pancreatic cancer , among other health problems – and even death. So the worsening rates of alcohol use disorder in women prompt the need for a greater focus on women in the research and the search for treatments.

Yet, women have long been underrepresented in biomedical research.

It wasn’t until 1993 that clinical research funded by the National Institutes of Health was required to include women as research subjects. In fact, the NIH did not even require sex as a biological variable to be considered by federally funded researchers until 2016. When women are excluded from biomedical research, it leaves doctors and researchers with an incomplete understanding of health and disease, including alcohol addiction.

There is also increasing evidence that addictive substances can interact with cycling sex hormones such as estrogen and progesterone . For instance, research has shown that when estrogen levels are high, like before ovulation, alcohol might feel more rewarding , which could drive higher levels of binge drinking. Currently, researchers don’t know the full extent of the interaction between these natural biological rhythms or other unique biological factors involved in women’s health and propensity for alcohol addiction.

Looking ahead

Researchers and lawmakers are recognizing the vital need for increased research on women’s health. Major federal investments into women’s health research are a vital step toward developing better prevention and treatment options for women.

While women like Amy Winehouse may have been forced to struggle both privately and publicly with substance use disorders and alcohol, the increasing focus of research on addiction to alcohol and other substances as a brain disorder will open new treatment avenues for those suffering from the consequences.

For more information on alcohol use disorder, causes, prevention and treatments, visit the National Institute on Alcohol Abuse and Alcoholism .

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How Does Writing Fit Into the ‘Science of Reading’?

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In one sense, the national conversation about what it will take to make sure all children become strong readers has been wildly successful: States are passing legislation supporting evidence-based teaching approaches , and school districts are rushing to supply training. Publishers are under pressure to drop older materials . And for the first time in years, an instructional issue—reading—is headlining education media coverage.

In the middle of all that, though, the focus on the “science of reading” has elided its twin component in literacy instruction: writing.

Writing is intrinsically important for all students to learn—after all, it is the primary way beyond speech that humans communicate. But more than that, research suggests that teaching students to write in an integrated fashion with reading is not only efficient, it’s effective.

Yet writing is often underplayed in the elementary grades. Too often, it is separated from schools’ reading block. Writing is not assessed as frequently as reading, and principals, worried about reading-exam scores, direct teachers to focus on one often at the expense of the other. Finally, beyond the English/language arts block, kids often aren’t asked to do much writing in early grades.

“Sometimes, in an early-literacy classroom, you’ll hear a teacher say, ‘It’s time to pick up your pencils,’” said Wiley Blevins, an author and literacy consultant who provides training in schools. “But your pencils should be in your hand almost the entire morning.”

Strikingly, many of the critiques that reading researchers have made against the “balanced literacy” approach that has held sway in schools for decades could equally apply to writing instruction: Foundational writing skills—like phonics and language structure—have not generally been taught systematically or explicitly.

And like the “find the main idea” strategies commonly taught in reading comprehension, writing instruction has tended to focus on content-neutral tasks, rather than deepening students’ connections to the content they learn.

Education Week wants to bring more attention to these connections in the stories that make up this special collection . But first, we want to delve deeper into the case for including writing in every step of the elementary curriculum.

Why has writing been missing from the reading conversation?

Much like the body of knowledge on how children learn to read words, it is also settled science that reading and writing draw on shared knowledge, even though they have traditionally been segmented in instruction.

“The body of research is substantial in both number of studies and quality of studies. There’s no question that reading and writing share a lot of real estate, they depend on a lot of the same knowledge and skills,” said Timothy Shanahan, an emeritus professor of education at the University of Illinois Chicago. “Pick your spot: text structure, vocabulary, sound-symbol relationships, ‘world knowledge.’”

The reasons for the bifurcation in reading and writing are legion. One is that the two fields have typically been studied separately. (Researchers studying writing usually didn’t examine whether a writing intervention, for instance, also aided students’ reading abilities—and vice versa.)

Some scholars also finger the dominance of the federally commissioned National Reading Panel report, which in 2000 outlined key instructional components of learning to read. The review didn’t examine the connection of writing to reading.

Looking even further back yields insights, too. Penmanship and spelling were historically the only parts of writing that were taught, and when writing reappeared in the latter half of the 20th century, it tended to focus on “process writing,” emphasizing personal experience and story generation over other genres. Only when the Common Core State Standards appeared in 2010 did the emphasis shift to writing about nonfiction texts and across subjects—the idea that students should be writing about what they’ve learned.

And finally, teaching writing is hard. Few studies document what preparation teachers receive to teach writing, but in surveys, many teachers say they received little training in their college education courses. That’s probably why only a little over half of teachers, in one 2016 survey, said that they enjoyed teaching writing.

Writing should begin in the early grades

These factors all work against what is probably the most important conclusion from the research over the last few decades: Students in the early-elementary grades need lots of varied opportunities to write.

“Students need support in their writing,” said Dana Robertson, an associate professor of reading and literacy education at the school of education at Virginia Tech who also studies how instructional change takes root in schools. “They need to be taught explicitly the skills and strategies of writing and they need to see the connections of reading, writing, and knowledge development.”

While research supports some fundamental tenets of writing instruction—that it should be structured, for instance, and involve drafting and revising—it hasn’t yet pointed to a specific teaching recipe that works best.

One of the challenges, the researchers note, is that while reading curricula have improved over the years, they still don’t typically provide many supports for students—or teachers, for that matter—for writing. Teachers often have to supplement with additions that don’t always mesh well with their core, grade-level content instruction.

“We have a lot of activities in writing we know are good,” Shanahan said. “We don’t really have a yearlong elementary-school-level curriculum in writing. That just doesn’t exist the way it does in reading.”

Nevertheless, practitioners like Blevins work writing into every reading lesson, even in the earliest grades. And all the components that make up a solid reading program can be enhanced through writing activities.

4 Key Things to Know About How Reading and Writing Interlock

Want a quick summary of what research tells us about the instructional connections between reading and writing?

1. Reading and writing are intimately connected.

Research on the connections began in the early 1980s and has grown more robust with time.

Among the newest and most important additions are three research syntheses conducted by Steve Graham, a professor at the University of Arizona, and his research partners. One of them examined whether writing instruction also led to improvements in students’ reading ability; a second examined the inverse question. Both found significant positive effects for reading and writing.

A third meta-analysis gets one step closer to classroom instruction. Graham and partners examined 47 studies of instructional programs that balanced both reading and writing—no program could feature more than 60 percent of one or the other. The results showed generally positive effects on both reading and writing measures.

2. Writing matters even at the earliest grades, when students are learning to read.

Studies show that the prewriting students do in early education carries meaningful signals about their decoding, spelling, and reading comprehension later on. Reading experts say that students should be supported in writing almost as soon as they begin reading, and evidence suggests that both spelling and handwriting are connected to the ability to connect speech to print and to oral language development.

3. Like reading, writing must be taught explicitly.

Writing is a complex task that demands much of students’ cognitive resources. Researchers generally agree that writing must be explicitly taught—rather than left up to students to “figure out” the rules on their own.

There isn’t as much research about how precisely to do this. One 2019 review, in fact, found significant overlap among the dozen writing programs studied, and concluded that all showed signs of boosting learning. Debates abound about the amount of structure students need and in what sequence, such as whether they need to master sentence construction before moving onto paragraphs and lengthier texts.

But in general, students should be guided on how to construct sentences and paragraphs, and they should have access to models and exemplars, the research suggests. They also need to understand the iterative nature of writing, including how to draft and revise.

A number of different writing frameworks incorporating various degrees of structure and modeling are available, though most of them have not been studied empirically.

4. Writing can help students learn content—and make sense of it.

Much of reading comprehension depends on helping students absorb “world knowledge”—think arts, ancient cultures, literature, and science—so that they can make sense of increasingly sophisticated texts and ideas as their reading improves. Writing can enhance students’ content learning, too, and should be emphasized rather than taking a back seat to the more commonly taught stories and personal reflections.

Graham and colleagues conducted another meta-analysis of nearly 60 studies looking at this idea of “writing to learn” in mathematics, science, and social studies. The studies included a mix of higher-order assignments, like analyses and argumentative writing, and lower-level ones, like summarizing and explaining. The study found that across all three disciplines, writing about the content improved student learning.

If students are doing work on phonemic awareness—the ability to recognize sounds—they shouldn’t merely manipulate sounds orally; they can put them on the page using letters. If students are learning how to decode, they can also encode—record written letters and words while they say the sounds out loud.

And students can write as they begin learning about language structure. When Blevins’ students are mainly working with decodable texts with controlled vocabularies, writing can support their knowledge about how texts and narratives work: how sentences are put together and how they can be pulled apart and reconstructed. Teachers can prompt them in these tasks, asking them to rephrase a sentence as a question, split up two sentences, or combine them.

“Young kids are writing these mile-long sentences that become second nature. We set a higher bar, and they are fully capable of doing it. We can demystify a bit some of that complex text if we develop early on how to talk about sentences—how they’re created, how they’re joined,” Blevins said. “There are all these things you can do that are helpful to develop an understanding of how sentences work and to get lots of practice.”

As students progress through the elementary grades, this structured work grows more sophisticated. They need to be taught both sentence and paragraph structure , and they need to learn how different writing purposes and genres—narrative, persuasive, analytical—demand different approaches. Most of all, the research indicates, students need opportunities to write at length often.

Using writing to support students’ exploration of content

Reading is far more than foundational skills, of course. It means introducing students to rich content and the specialized vocabulary in each discipline and then ensuring that they read, discuss, analyze, and write about those ideas. The work to systematically build students’ knowledge begins in the early grades and progresses throughout their K-12 experience.

Here again, available evidence suggests that writing can be a useful tool to help students explore, deepen, and draw connections in this content. With the proper supports, writing can be a method for students to retell and analyze what they’ve learned in discussions of content and literature throughout the school day —in addition to their creative writing.

This “writing to learn” approach need not wait for students to master foundational skills. In the K-2 grades especially, much content is learned through teacher read-alouds and conversation that include more complex vocabulary and ideas than the texts students are capable of reading. But that should not preclude students from writing about this content, experts say.

“We do a read-aloud or a media piece and we write about what we learned. It’s just a part of how you’re responding, or sharing, what you’ve learned across texts; it’s not a separate thing from reading,” Blevins said. “If I am doing read-alouds on a concept—on animal habitats, for example—my decodable texts will be on animals. And students are able to include some of these more sophisticated ideas and language in their writing, because we’ve elevated the conversations around these texts.”

In this set of stories , Education Week examines the connections between elementary-level reading and writing in three areas— encoding , language and text structure , and content-area learning . But there are so many more examples.

Please write us to share yours when you’ve finished.

Want to read more about the research that informed this story? Here’s a bibliography to start you off.

Berninger V. W., Abbott, R. D., Abbott, S. P., Graham S., & Richards T. (2002). Writing and reading: Connections between language by hand and language by eye. J ournal of Learning Disabilities. Special Issue: The Language of Written Language, 35(1), 39–56 Berninger, Virginia, Robert D. Abbott, Janine Jones, Beverly J. Wolf, Laura Gould, Marci Anderson-Younstrom, Shirley Shimada, Kenn Apel. (2006) “Early development of language by hand: composing, reading, listening, and speaking connections; three letter-writing modes; and fast mapping in spelling.” Developmental Neuropsychology, 29(1), pp. 61-92 Cabell, Sonia Q, Laura S. Tortorelli, and Hope K. Gerde (2013). “How Do I Write…? Scaffolding Preschoolers’ Early Writing Skills.” The Reading Teacher, 66(8), pp. 650-659. Gerde, H.K., Bingham, G.E. & Wasik, B.A. (2012). “Writing in Early Childhood Classrooms: Guidance for Best Practices.” Early Childhood Education Journal 40, 351–359 (2012) Gilbert, Jennifer, and Steve Graham. (2010). “Teaching Writing to Elementary Students in Grades 4–6: A National Survey.” The Elementary School Journal 110(44) Graham, Steve, et al. (2017). “Effectiveness of Literacy Programs Balancing Reading and Writing Instruction: A Meta-Analysis.” Reading Research Quarterly, 53(3) pp. 279–304 Graham, Steve, and Michael Hebert. (2011). “Writing to Read: A Meta-Analysis of the Impact of Writing and Writing Instruction on Reading.” Harvard Educational Review (2011) 81(4): 710–744. Graham, Steve. (2020). “The Sciences of Reading and Writing Must Become More Fully Integrated.” Reading Research Quarterly, 55(S1) pp. S35–S44 Graham, Steve, Sharlene A. Kiuhara, and Meade MacKay. (2020).”The Effects of Writing on Learning in Science, Social Studies, and Mathematics: A Meta-Analysis.” Review of Educational Research April 2020, Vol 90, No. 2, pp. 179–226 Shanahan, Timothy. “History of Writing and Reading Connections.” in Shanahan, Timothy. (2016). “Relationships between reading and writing development.” In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (2nd ed., pp. 194–207). New York, NY: Guilford. Slavin, Robert, Lake, C., Inns, A., Baye, A., Dachet, D., & Haslam, J. (2019). “A quantitative synthesis of research on writing approaches in grades 2 to 12.” London: Education Endowment Foundation. Troia, Gary. (2014). Evidence-based practices for writing instruction (Document No. IC-5). Retrieved from University of Florida, Collaboration for Effective Educator, Development, Accountability, and Reform Center website: http://ceedar.education.ufl.edu/tools/innovation-configuration/ Troia, Gary, and Steve Graham. (2016).“Common Core Writing and Language Standards and Aligned State Assessments: A National Survey of Teacher Beliefs and Attitudes.” Reading and Writing 29(9).

A version of this article appeared in the January 25, 2023 edition of Education Week as How Does Writing Fit Into the ‘Science of Reading’?

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Scientists use generative AI to answer complex questions in physics

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When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language , a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.

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Researchers at MIT and elsewhere have developed a new machine-learning model capable of “predicting a physical system’s phase or state,” report Kyle Wiggers and Devin Coldewey for TechCrunch . 

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