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Virtual Lab Online Research Studies

The pimco decision research virtual lab at the roman family center for decision research allows people from around the world to take paid research studies online using surveys, zoom video calls, and other remote tools..

Video Transcript

Transcript coming soon.

By participating in online behavioral science studies, you play a vital role in helping Chicago Booth researchers better understand judgment and decision-making.

Note: the Virtual Lab will be closed June 3-7.

Here's how you can take paid surveys and interactive studies from the comfort of home. Sign Up (New Participants)   Log In (Existing Participants)

Take Studies in the Virtual Lab

You'll need:.

• A computer, smartphone, or tablet
• Internet connection

How to Get Started

• Sign up for an account. Within 1 business day, you'll receive an email with your login info and other important information.
• Tell us about yourself. The first time you log into your account, you'll be prompted to take a short prescreen survey about you and your background. Watch a video tutorial.
• Sign up for paid surveys: When you log in, you'll see the studies you are eligible to take. By default, participants are eligible for surveys, one of our two types of studies.
• Optional: Take the Zoom Prerequisite Study Pays $3 / 10-15 min. / Appointments Tu-Fri, 11am-4pm CT Many of our highest-paying studies are conducted via Zoom, a video chat platform. To become eligible for these more advanced studies, you must pass the Zoom Prerequisite Study. You'll need a computer with a working microphone, webcam, and audio. Video tutorial .

After completing these introductory tasks, you'll be able to sign up for behavioral science studies on our online research platform, Sona . Sign Up to Participate

Compensation

Participants are paid at a rate of $12 per hour ($1 for every 5 minutes). After completing a study, you'll be emailed an Amazon digital gift card as payment , typically within two business days.

Please note that if you are located outside of the United States, you will only be able to redeem Amazon electronic gift cards via the USA-based amazon.com site, and not on Amazon sites based in other countries.

What to Expect

Behavioral science combines psychology, economics, and other fields to better understand human decision-making.The Virtual Lab's online research studies involve simple, everyday tasks like filling out surveys, providing your opinions, or chatting with a study partner.

Interactive studies are conducted using Zoom, a video chat platform. These tend to be our highest paying studies and require appointments scheduled Tuesday-Friday. Before taking Zoom studies, you must first complete the Zoom Prerequisite Study. Tips for using Zoom .

We pride ourselves on creating an inclusive and safe environment for all participants and researchers, so please review the Virtual Lab code of conduct before participating.

Questions? Email us at [email protected] .

Current Participants

Log into your account to see the paid research studies you are currently eligible to complete. Don't see any studies? Check back soon! New studies & time slots are posted each weeknight by 8pm.

Surveys are available on-demand 24/7 and can be completed any time before the study's deadline.

Zoom studies are conducted Tuesday-Friday 11am-4 pm CT (UTC -5).

For Researchers

If you are a researcher interested in conducting online studies in the Virtual Lab, please visit the Researcher Portal  (requires CNET ID) or contact the Virtual Lab to discuss your options. The labs team can help with IRB approval, study design, data analysis, and more.

Follow the Roman Family Center for Decision Research

DEPARTMENT OF PSYCHOLOGY

• Participate in Research Studies

Paid Research Opportunities

The following studies are recruiting participants and pay for your time. Read the descriptions and requirements. If you are interested, email the researcher asking to participate. Information on some studies is also posted on bulletin boards in Swift Hall.

Daily Experiences Across Relationships (DEAR) Study

Northwestern’s Relationships and Motivation Lab and University of Chicago’s CEDAR Lab are conducting a 6-month study about people’s life experiences and romantic relationships. This study consists of all online surveys that can be done from home. It involves a quick introductory zoom session with a member of our research team, an hour-long initial survey, 2 weeks of short 5-minute surveys, and three 25-minute follow-up surveys that come 2, 4, and 6 months later. You and your partner can earn up to $120 each for participating ($240 per couple).

You may be eligible if you:

• Have been in a relationship for at least 6-months
• Both of you are at least 25 years old, current US residents, and fluent English-speakers
• Have regular internet access
• Have a romantic partner who is willing to participate

Please contact Erin at [email protected] for more information!

Principle investigator: Eli Finkel

Study Title: Daily Experiences Across Relationships Study

IRB #: STU00219294-MOD0001

Cognitive Architecture of Bilingual Language Processing

The  Bilingualism and Psycholinguistics Research Group  at Northwestern University is looking for  Korean-English bilinguals  for an EEG study on language and cognition. We are interested in how languages are represented in the mind. Electroencephalography (EEG) is a safe and non-invasive neuroimaging technique. We are recording the neural activity at the surface of the scalp as it naturally occurs in the brain. The testing session takes approximately 3 hours to complete. For your time and effort, you will be compensated $15 per hour.

You may be eligible to participate if: 

-You are proficient in Korean and English.

-You are between the ages of 18 and 35.

-You are right-handed.

-You have normal or corrected-to-normal vision (glasses, contacts).

-You have no history of neurologic, cognitive, or psychiatric disorders. 

The study takes place at Northwestern’s Evanston campus at 2240 Campus Drive (Frances Searle Building) in room 3-367. Appointments will be scheduled at a time that is most convenient for you. 

If you are interested, please email Ashley Chung-Fat-Yim at  [email protected]  or give us a call at 847-467-2709.

Principal Investigator:  Dr. Viorica Marian

Study Title:  Cognitive Architecture of Bilingual Language Processing

IRB #:  STU00023477

Can over-the-counter hearing aids help with hearing loss?

The Hearing Aid Laboratory at Northwestern University is looking for participants for a research study about how over-the-counter (OTC) hearing aids impact how we listen and communicate with others. We are looking for adults with known or suspected mild-to-moderate hearing loss to bring a communication partner with them (i.e., spouse, friend, neighbor, adult child, etc.) to have a conversation together while wearing OTC hearing aids.

What to expect:

• The study involves a total of 2 visits to our lab.
• The first visit involves tests of memory, hearing, and communication with a partner for one visit to our lab, lasting approximately 2 hours. We will provide you with a free comprehensive hearing test, and you will be fit with OTC hearing aids during your visit to the lab.
• The second visit involves a test of speech in background noise while wearing OTC hearing aids.

You may be eligible to participate if:

• You have diagnosed or suspected mild to moderate hearing loss in both ears

• You are 18 years or older

• You are able to bring someone with you to your first visit

• You have normal or corrected-to-normal vision (glasses, contacts)

• No history of neurologic, cognitive, or psychiatric disorders

The person you bring to the study with you is eligible if:

• They have no hearing loss, OR wear hearing aids consistently if they have hearing loss

• Are at least 18 years of age

• English is their primary language

• Normal or corrected-to-normal vision (glasses, contacts)

The study takes place in either Northwestern’s main campus at 2240 Campus Drive in Evanston, or at our downtown location at 710 N Lake Shore Drive in Chicago. Visits will be scheduled at the located that is most convenient for you. 

If you are interested, please email us at [email protected]  or give us a call at 847-467-0897.

You can also fill out an initial interest form by clicking here.  

Principal Investigator: Dr. Pamela Souza

Study Title: Investigation of direct-to-consumer hearing aids on conversation efficiency and listening effort

IRB #: STU00217791

Visual Adaptation, Selective Attention, and Shape Coding: An Integrative Investigation of Visual Attention; Understanding the Mechanisms that Control the Dynamics of Perceptual Switches

The laboratories of Dr. Satoru Suzuki and Dr. Marcia Grabowecky are currently seeking healthy adults to participate in research on perception. Studies take place on the Northwestern University Evanston campus. Participants are compensated $15/hour for volunteering. Note: no transportation or parking costs will be covered.

If you are interested in participating, please contact our laboratory by telephone (847) 467-6539, or email for more information: [email protected]  

Once you contact the laboratory, you will be informed of studies in progress and their specific requirements (for example, handedness, age range, gender) and procedures. Typically, studies involve responding to images or sounds presented by a computer and last from 1-2 hours. Some studies also require responding to personality or mood questionnaires, or having physiological responses recorded (for example, brain waves or eye movements). The details of the particular study will be provided when you contact the laboratory. If you are interested in volunteering and you qualify to participate in any ongoing studies, an appointment will be scheduled.

Principal Investigators: Dr. Satoru Suzuki and Dr. Marcia Grabowecky

Study Title: Visual Adaptation, Selective Attention, and Shape Coding: An Integrative Investigation of Visual Attention; Understanding the Mechanisms that Control the Dynamics of Perceptual Switches 

IRB #: CR1_STU00013229 

Psychosis Risk Outcomes Network Study

We are seeking young people who are concerned about recent changes in mood, thinking or behavior. This research project aims to increase understanding of mental health concerns in young people and how to prevent the development of a more serious mental illness such as psychosis.

You may be eligible for the study if you meet any of the following criteria:

• Ages 12 - 30
• Noticing a recent change in thinking, behavior, or experiences, such as:
• Confusion about what is real or imaginary
• Feeling not in control of your own thoughts of ideas
• Feeling suspicious or paranoid
• Having experiences that may not be real, such as hearing sounds or seeing things that may not be there
• Having trouble communicating clearly

The study would entail visits over a 2-year period, and you would be paid $30 per hour for your participation.  Eligible participants will be asked to come in for various assessments including:

• clinical interviews
• biological assessments (MRI & EEG brain scans; blood and saliva testing)
• cognitive testing

If you are interested, please email us at [email protected]  or fill out this online eligibility survey , and a member of our team will get back to you shortly.

Principal investigator: Dr. Vijay Mittal Study Title: ProNET IRB #: STU00215145

Good at sleeping?

The Cognitive Neuroscience Lab in the Department of Psychology at Northwestern is recruiting volunteers to participate in sleep research ( STU00034353 )

 Compensation is provided for studies ($12.50/hr)

You can participate in Chicago or at our sleep lab on the Evanston campus.

 To sign up and learn more about the The Paller Lab, visit:    www.northwestern.edu/people/kap/apply  

Principal Investigator: Dr. Ken Paller

Study Title: Strategically strengthening declarative memories during sleep: Learning, Creative Problem-Solving, REM Sleep, and Dreaming

IRB# STU00034353-MOD0044

• Be a fluent speaker of English
• Be at least 18 years old
• Have a Paypal account to receive payments or be able to accept an Amazon.com gift certificate for participation

To be eligible to receive payment for our study you must be using a web browser with cookies enabled :

• Windows: Internet Explorer verison 5 or later
• Windows: Netscape 6 or later
• Mac: Safari
• Any: Firefox

If this sounds like you, please complete the following page. If you qualify, we will contact you by email in advance of the experiment.

Will I get paid?

You will not receive payment for filling out the form to register for our Online Panel. However, you will be paid for completing our online studies you will be invited to. Payments are mainly handled by PayPal ( www.paypal.com/ ) a free service. Therefore, make sure that the email you sign up with is the same email address associated with your Paypal account . This way your earnings can be directly deposited in your account. Alternatively, in many of our studies, you can choose to receive payment in the form of an Amazon.com gift certificate. If you do not wish to be paid via Paypal or with an Amazon.com gift certificate, please do not participate in this research. Payment is approximately $16 per hour , however this varies depending on the length of the study. You may also earn bonus payments, which depend on the study as well.

How is data I provide stored?

Information about you will be kept in strict confidentiality and stored on a remote server hosted by Amazon Web Services (AWS). Your information will not be shared with anyone outside the Center for Decision Sciences and you will not be contacted except as it concerns the Center for Decision Sciences Virtual Lab. Your personal information will be kept indefinitely until request for deletion and will under no circumstances be given out to third parties. You may choose to remove yourself from our online participant panel at any time without penalty by emailing [email protected] with the email address associated with your account. Likewise, the Center for Decision Sciences reserves the right to remove participants from our panel at any time. Please note that removal from the online panel itself doesn’t guarantee removal from past studies in which you have already participated. If removal occurs, periodic emails will be sent to ask if you prefer to have your information completely deleted from our system.  

What do I consent to?

By registering in the CDS Virtual Lab Online Panel, you consent to receive email invitations from the Center for Decision Sciences to participate in online surveys for which you may qualify. Except as it concerns online studies, you will not be contacted. Your participation in online studies is entirely voluntary. You may choose to participate in certain surveys and decline to participate in others. However, you will not be able to participate in our web-based online studies if you do not join the online panel.

What research studies am I going to participate in?

For more information on the Center for Decision Sciences and our research projects please visit our homepage . If you have any questions or concerns, please contact us at [email protected] .  

If at any time you have questions or concerns about your rights or welfare as a research subject, contact the Columbia University Morningside Institutional Review Board (IRB) by email at [email protected] , or by phone at 212-851-7040.

What are the research purpose, risks, and benefits?

The purpose of the CDS Virtual Lab Online Panel is to create a mailing list of participants (also called panelists) that r eceive invitations to participate in paid online surveys.   There are no foreseeable risks to joining the online panel.   There is no direct benefit to joining the panel (payment is not considered a benefit of research). It will take approximately 5 minutes to complete the registration form.

  You can register now by clicking the button below!  

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ivetriedthat

35 Paid Online Research Studies Seeking Participants

How can one participate in paid online research studies and get paid for your brain, your health, and your opinions?

• Inbox Dollars - Get paid to check your email. $5 bonus just for signing up!
• Survey Junkie - The #1 survey site that doesn't suck. Short surveys, high payouts, simply the best.
• Nielsen - Download their app and get paid $50!

Well, you’re in the right place.

Today, let’s look at 35 different opportunities to get paid as a participant in research studies.

Types of Paid Online Research Studies

A medical study involves a group of people within an age group, gender, race, ethnic group, or individuals with the same specific health issues.

Participating in these studies often involves answering a combination of interviews, tests, surveys, or experimentation to be able to answer questions on how to diagnose, treat, prevent, or cure health disorders and diseases.

Aside from paid medical studies, market research makes use of paid online research to find out what customers want or need from various products and companies.

The cool thing about paid online research studies available today is that even if you are not a part of the target audience, you can still participate in the study in another capacity.

Online research studies can be either quantitative or qualitative.

Quantitative studies are the ones with static, pre-planned answers. A questionnaire with multiple-choice answers is a good example of this study. It is made as such so that the researcher can easily analyze the results.

Qualitative studies are a bit more complex since they involve open-ended answers.

However, this type of study ends up with better data. Focus groups and interviews are both methods used in qualitative studies.

How Much Can You Earn from Paid Research Studies?

Imagine earning up to $1,000 just for sharing your opinion, review of a product, or thoughts about a particular experience without even stepping out of the house.

You don’t even have to spend a cent to participate in these research studies.

Most of the time, you’d only have to be at least 18 years old and currently live in the US.

Even when you’re below 18 years old, researchers sometimes allow you to participate as long as you had a waiver from your parent or legal guardian.

The amount you earn from joining research studies depend on the following:

• Method of research — Did you join a focus group? Answered a lengthy interview? Filled out a survey form?
• Length of study — Some interviews only take an hour and earn you $150. Some focus groups could take several sessions and end only after several weeks.
• Type of payment — Not all companies pay cash. Some use PayPal, while others prefer checks, gift cards, prepaid cards, and so on.

Quick surveys can be as little as $10 and high as $100.

Focus group sessions range between $50 and $500 per session.

Interviews can earn you somewhere from $50 to $400 for an hour of your time.

Simply put, the amount you’ll be paid will vary from study to study.

Join These to Start Making Money Today!

Before digging into the list below, I suggest you sign up for the 3 best focus group/market research companies.

Anyone can participate and you will be invited to take part in research studies, focus groups, and product testing opportunities.

These companies pay in cash and offer cash signup bonuses to get you started.

• SurveyJunkie - Get paid CASH to share your thoughts on some of the world's biggest brands.
• Branded Surveys - Work directly with companies like Nike, Samsung, Amazon, and Disney to improve their product lines.

On to the list!

35 Ways to Get Paid for Research Studies Online

The following universities have year-long research studies in a wide range of topics.

1. Northwestern University Department of Psychology

Earn from $10 to $40 an hour if you participate in one of the school’s online studies.

The studies change regularly and vary widely from topics such as phone usage, changes in thoughts and feelings, Artificial Intelligence,  sleeping patterns, psychosis, aging, and even products like hearing aids.

2. Harvard University Psychology Study Pool

Join Harvard’s Psychology Study Pool and earn from $10 to $25 an hour, paid via gift cards.

The online studies are available year-round for both Harvard students and guests.

3. Carnegie Mellon University

If you’re at least 18 years old, can read and speak English, and have never joined any research studies at the Center for Behavioral and Decision Research at Carnegie Mellon, you can sign up for their paid online research studies.

Topics range from personal beliefs, attitudes, decision-making, human judgment, interpersonal perceptions, and group performance, among others.

You’ll earn $8 an hour, paid in gift cards. Each study takes anywhere from 5 and 20 minutes.

Paid participants are needed for in-person studies in labs on campus, but may sometimes be able to participate online on a home computer.

Note that only students are accepted (ID will be requested).

4. Center for Decision Sciences Columbia Business School

For participating in an online survey or study, you can earn $16 an hour, as long as you’re 18+ years old and have a PayPal or Amazon.com account to receive payments.

No need to be a student at Columbia Business School, but you need to register in the CDS Virtual Lab Online Panel.

5. MIT Behavioral Research Lab

There are in-person and paid online research studies available at MIT.

Payment amounts vary from study to study, but participants usually earn $11 to $20 per hour for online studies.

Anyone aged 18 or older, residing in the United States, and proficient in spoken and written English can join the BRL participant pool.

Membership is open to all; you don’t need any association with MIT to participate. Other requirements may be needed for studies requiring specific respondents.

6. University of Maryland Robert H. Smith School of Business

As long as you’re a current Smith student (and eligible for certain studies), you can get paid for research studies online here.

Make sure you create an account, sign up for the studies that you want to participate in, and get paid once you fulfill your role.

7. Purdue University

You can find a lot of paid online studies here.

Currently, they have studies on Parkinson’s disease (and other neurodegenerative diseases), flavored water, biosensors, mushroom nutrition, linguistics, cancer, and so on.

Participants are paid somewhere between $10 and $500.

What’s great about Purdue University is that the studies are varied and open to the public.

8. Stanford Graduate School of Business

You’ll be paid up to $25 an hour for online research studies, but you’d have to be eligible and complete a prescreen form.

You also can’t participate in the same study more than once.

9. UCLA Anderson School of Management

The behavioral lab has some paid online studies, if you’re interested in topics like consumer behavior, organizational behavior, judgment, and other similar topics.

Most studies here pay from $10 to $20 an hour.

They also have in-lab and in-person studies.

10. Boston University Behavioral Lab

If you’re interested in human behavior and is willing to participate in online studies, try to join if you’re eligible.

These studies pay between $10 and $20 an hour.

The studies from Boston University’s Behavioral Lab is open to both BU students and the general public.

11. University of Maryland Department of Psychology

You need to create an account at the SONA System website to see available research studies.

Each of the studies have different eligibilities and payment.

12. University of Nebraska-Lincoln

This college holds a wide range of research studies revolving MRI research, human brain, behavior, and so on.

There are studies exclusively for seniors, and those that are for teens.

There are two ways to volunteer for these studies:

• Join the CB3 Research Participant Volunteer Registry (and wait for them to e-mail you)
• Pick the study and contact researchers directly.

Pay can go as high as $80 per study.

13. American University Psychology Department

Topics vary widely, but they are related to psychology and human behavior.

You can earn up to $20 an hour for just filling out a form as a smoker’s first-hand experience during stressful situations.

14. Respondent.IO

This next one isn’t a university, but it’s a comprehensive resource if you plan to participate in numerous market research and other online studies.

Pay ranges from $25 to $200.

Eligibility requirements vary between studies.

Make sure to check details and never pay to join a focus group or study.

15. Brand Institute

Want to be at the forefront of the pharmaceutical industry?

Join consumer market research panel groups by signing up with Brand Institute.

16. mindswarms

It’s sort of like an interview since you are required to answer ten questions with a video.

In exchange for your thoughts, you’ll be paid $50.

Earn somewhere between $50 and $250 by participating in healthcare or consumer market research studies.

18. Probe Market Research

The company pays people for online, phone or group interviews about their clients’ products, services, ads, or other campaigns.

Payment goes as high as $400.

19. Penn State University

(Quick shoutout to my Alma Mater… We Are!)

… and they are seeking just about anyone who’s alive to participate in a research study. With over 200 current open studies, odds are, you’ll qualify for something they have available.

Keep an eye out for “Total Compensation” to see just what the study pays.

20. User Interviews

Get paid for your feedback on real projects.

Create an account by filling up a form and wait for project invitations if you’re eligible.

There are online and in-phone interviews available.

21. Yale School of Management

If you live anywhere near Yale campus, be sure to sign up for their newsletter as they frequently put out requests for both in person and paid online research studies.

You will be paid, in cash, at the completion of your study.

They also have a Facebook group that announces when new studies are available to participate in.

22. Georgetown University Department of Psychology

Georgetown’s Department of Psychology is regularly looking for both students and non-students alike to participate in studies.

Average pay will run you about $10 per hour, so it can be some nice change to pick up in your spare time.

Their research includes personality, memory, and impulse control tests to name a few.

23. UCLA Semel Institute

UCLA offers both in-person and online research studies to check out.

They have a massive list of open opportunities for you to click through. The highest paying ones often need you to come into their offices for scans and interviews, but there are a lot of remote positions available too.

24. PingPong

Web designers and developers working on websites and applications value the input of real-life users and are willing to pay for these users’ opinions and insights

You can get paid anywhere from €15 to €40 per hour and even up to €100 for special projects.

Payment is credited through Transferwise or PayPal.

25. American Consumer Opinion

Yet another survey site, American Consumer Opinion (ACOP) pays you for your answers to their survey questions.

You can even get paid up to $100 if you fit a certain demographic and able to participate in special projects.

Research studies on this site may not be constantly available, though. It’s best to check back frequently.

26. Recruit and Field

Recruit and Field is a market research company that hosts paid online research studies all around the US and even in international locations.

They look for participants from any gender and age for their studies, including professionals and medical professionals (doctor, nurse, lab technician).

They normally pay via PayPal but also offer Amazon or digital gift cards.

The pay ranges from $100 to $275 for phone interviews, online surveys, and sometimes in-home product testing.

27. Focus Group

Focus Group is an aptly named online community comprised of participants interested in sharing opinions and views on popular products and brands through in-person, telephone, or online surveys.

The pay ranges from $75 to $200, and specialized health studies may pay more.

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Since 1986, 20|20 Panel has been recruiting participants to share their opinion on various companies.

They specialize in qualitative market research, which is achieved via in-person or online roundtable discussions. You can get paid from $50 to $350 to participate in these discussions.

They also send out quick surveys for which you can get paid smaller amounts (from $1 to $10).

29. FindFocusGroups

Wouldn’t it be great if there were a directory of all the paid research opportunities in the country?

FindFocusGroups is probably the closest one, as it lists more than 75,000 verified and legitimate focus groups in the country.

It’s quite simple to search by city and state, and check the information for details on whether the discussions are online or in-person.

It’s difficult to know how much the average payment would be, but upon browsing the first few studies on the homepage, they range from $50 to $300.

30. SIS International

SIS International conducts focus group discussions in cities all over the US and globally and collects consumer feedback on anything from appliances, skincare products, gadgets, and just about anything.

Rates range from $25 to $200 for 2 to 3 hours of your time.

31.  Apex Focus Group

Apex Focus Group connects regular people like you and me with researchers, who will pay for participants to join clinical research trials, phone interviews and focus groups.

As a participant at any Apex Focus Group study, you can be paid up to $750 a week.

Online and in-person studies are available.

32.  Fieldwork

If you live near New Jersey, New York City, Phoenix, San Francisco, Seattle, Boston, Chicago, Denver, Los Angeles, or Minneapolis, you can participate in current Fieldwork research studies.

Most of the paid focus groups are face-to-face (but online are sometimes available).

Each study lasts about 1 or 2 hours. Participants earn between $75 and $100 for their time.

33. Rare Patient Voice

This company mostly looks for participants who have rare diseases and medical conditions. As such, only eligible people can benefit from the studies.

However, anyone who qualifies will receive $120/hour.

You can share your views via online surveys, clinical trials, or web-assisted phone interviews.

34. ClinicalTrials.gov

The federal government continually seeks individuals willing to participate in clinical trials testing different medications and treatments.

Studies are often conducted by the National Institutes of Health.

You can check out a list of ongoing clinical trials at clinicaltrials.gov.

Unlike other paid online research studies on this list, DScout is an app you can download.

You need to register to become a “scout” and participate in research “missions,” which will earn you money after completion.

DScout studies are usually 1-on-1 interviews or video responses, so you’d have to be comfortable in front of the camera.

Missions pay from $50 to $100, each lasting about 30 minutes.

Can I turn these Paid Online Research Studies into a Full-Time Job?

While paid online studies are highly interesting and offer legitimate side cash, this gig cannot replicate the steady income and benefits you can get with a full-time job.

You’d have to consider that many research studies:

• have eligibility requirements (which means you’re not guaranteed a slot every time)
• cannot be joined twice (once you’ve participated in a specific study, you can’t do a repeat)
• have varying payments (there’s no stability in such income)

I do think it’s a legitimate side gig if you’re in between jobs or have a lot of free time on your hands.

You can also get paid answering surveys , joining focus groups , or testing products .

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• Open access
• Published: 31 May 2024

The role of medical schools in UK students’ career intentions: findings from the AIMS study

• Tomas Ferreira 1 , 3 ,
• Alexander M. Collins 2 , 3 ,
• Arthur Handscomb 3 ,
• Dania Al-Hashimi 4 &

the AIMS Collaborative

BMC Medical Education volume  24 , Article number:  604 ( 2024 ) Cite this article

313 Accesses

19 Altmetric

Metrics details

To investigate differences in students’ career intentions between UK medical schools.

Cross-sectional, mixed-methods online survey.

The primary study included all 44 UK medical schools, with this analysis comprising 42 medical schools.

Participants

Ten thousand four hundred eighty-six UK medical students.

Main outcome measures

Career intentions of medical students, focusing on differences between medical schools. Secondary outcomes included variation in medical students’ satisfaction with a prospective career in the NHS, by medical school.

2.89% of students intended to leave medicine altogether, with Cambridge Medical School having the highest proportion of such respondents. 32.35% of respondents planned to emigrate for practice, with Ulster medical students being the most likely. Of those intending to emigrate, the University of Central Lancashire saw the highest proportion stating no intentions to return. Cardiff Medical School had the greatest percentage of students intending to assume non-training clinical posts after completing FY2. 35.23% of participating medical students intended to leave the NHS within 2 years of graduating, with Brighton and Sussex holding the highest proportion of these respondents. Only 17.26% were satisfied with the prospect of working in the NHS, with considerable variation nationally; Barts and the London medical students had the highest rates of dissatisfaction.

Conclusions

This study reveals variability in students’ career sentiment across UK medical schools, emphasising the need for attention to factors influencing these trends. A concerning proportion of students intend to exit the NHS within 2 years of graduating, with substantial variation between institutions. Students’ intentions may be shaped by various factors, including curriculum focus and recruitment practices. It is imperative to re-evaluate these aspects within medical schools, whilst considering the wider national context, to improve student perceptions towards an NHS career. Future research should target underlying causes for these disparities to facilitate improvements to career satisfaction and retention.

Peer Review reports

Introduction

The rapidly changing dynamics of modern healthcare require a comprehensive understanding of the driving forces behind the career trajectories of doctors. As the landscape of patient care, healthcare policy, and medical technology continues to evolve, so too do the career choices of emerging doctors. These choices, as research increasingly demonstrates, are not solely the product of personal inclination or market demand but are deeply influenced by their experiences in medical school [ 1 ].

In recent years, the recruitment and retention of doctors within the United Kingdom’s (UK) National Health Service (NHS) have emerged as pressing concerns, requiring a detailed analysis of the factors influencing the career intentions of medical students [ 2 , 3 , 4 ]. To address this, the Ascertaining the career Intentions of Medical Students (AIMS) study — the largest ever UK medical student survey — delineated the career intentions and underlying motivations of students, highlighting a significant trend towards alternative careers or emigration, influenced predominantly by remuneration, work-life balance, and working conditions within the NHS [ 5 ].

Expanding upon the insights of the AIMS study, we seek to further explore the nuanced differences in career intentions among medical students, in relation to their institutional affiliations, and foster a dialogue concerning medical education and workforce planning in the UK, highlighting the role of medical schools in shaping career trajectories. It is posited that these educational institutions, with their diverse curricular designs and teaching philosophies, may play a pivotal role in shaping the prospective professional trajectories of their students. Furthermore, the distinct socio-economic and cultural environments in which these schools are situated, and those of the students they attract, may also contribute to the varied perspectives and career aspirations of students. Historically, the field of medical education has been subject to a variety of pedagogical philosophies, curricular reforms, and institutional priorities. These variations across medical schools, while often subtle, can result in significant differences in the way students perceive their roles, responsibilities, and opportunities within the broader healthcare ecosystem. Literature suggests that various elements including the culture of a medical school and its sociocultural context play a significant role in shaping the professional aspirations of its students [ 1 , 6 ].

This manuscript seeks to identify and characterise these differences, with a focused analysis on how various medical schools in the UK might be influencing the career preferences and intended paths of their students. These findings may hold significant implications for various stakeholders within the healthcare sector. Policymakers could find guidance for strategic investments and resource allocation to areas anticipated to experience shortages, while educationalists could gain an opportunity for reflection on the potential influence of their institutions on student aspirations, thereby considering necessary adjustments. Furthermore, it affords insights for improved recruitment strategies, critical to ensuring the NHS’s continued role in the UK.

Study design

The AIMS study was a national, cross-sectional, multi-centre study of medical students conducted according to its published protocol and extensively described in its main publication [ 5 , 7 ]. Participants from 44 UK medical schools recognised by the General Medical Council (GMC) were recruited through a non-random sampling method via a novel, self-administered, 71-item questionnaire. The survey was hosted on the Qualtrics survey platform (Provo, Utah, USA), a GDPR-compliant online platform that supports both mobile and desktop devices.

Participant recruitment and eligibility

In an attempt to minimise bias and increase the survey’s reach to promote representativeness, a network of approximately 200 collaborators was recruited across 42 medical schools – one collaborator per year group, per school – prior to the study launch to disseminate the study. All students were eligible to apply to become a collaborator. This approach aimed to obtain a representative sample and improve our findings’ generalisability. The survey was disseminated between 16 January 2023 and 27 March 2023, by the AIMS Collaborative via social media (including Instagram, Facebook, WhatsApp, and LinkedIn), word of mouth, medical student newsletters/bulletins, and medical school emailing lists.

Individuals were eligible to participate in the survey if they were actively enrolled in a UK medical school acknowledged by the GMC and listed by the Medical School Council (MSC). Certain new medical schools had received approval from the GMC but were yet to admit their inaugural cohort of students, so were excluded from the study.

Data processing and storage

To prevent data duplication, each response was restricted to a single institutional email address. Any replicated email entries were removed prior to data analysis. In cases where identical entries contained distinct responses, the most recent entry was kept. Responses for which valid institutional email addresses were missing were removed prior to data analysis to preserve the study’s integrity.

The findings of this subanalysis, and the AIMS study, were reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [ 8 ].

Quantitative data analysis

Descriptive analysis was carried out with Microsoft Excel (V.16.71) (Arlington, Virginia, USA), and statistical inference was performed using RStudio (V.4.2.1) (Boston, Massachusetts, USA). Tables and graphs were generated using GraphPad Prism (V.9.5.0) (San Diego, California, USA). ORs, CIs and p values were computed by fitting single-variable logistic regression models to explore the effect of various demographic characteristics on students’ career intentions. CIs were calculated at 95% level. We used p  < 0.05 to determine the statistical significance for all tests.

Study population and exclusion

All current students of all year groups at UK medical schools recognised by the GMC and the MSC were eligible for participation. Brunel Medical School and Kent and Medway Medical School were excluded from this current analysis due to the limited number of respondents from these institutions ( n  < 30), to avoid misrepresenting the career intentions and characteristics of their broader student populations.

Ethical approval

Ethical approval was granted by the University of Cambridge Research Ethics Committee (reference PRE.2022.124) on the 5th of January 2023. Prior to completing the survey, all participants provided informed consent. Participating medical schools were contacted prior to data collection to seek support and request permission to contact their students.

Demographics

In total, 10,486 students across all 44 UK medical schools participated in the survey. To enable comparison of students’ career intentions between medical schools, only 42 medical schools were considered due to the sample size gathered. The average number of responses per medical school was 244, with a median of 203 (IQR 135–281). Participants had a median age of 22 (IQR 20–23). Among the participants, 66.5% were female ( n  = 6977), 32.7% were male ( n  = 3429), 0.6% were non-binary ( n  = 64), and 16 individuals chose not to disclose their gender. A detailed breakdown of participant characteristics, including gender, ethnicity, previous schooling, and course type, is illustrated in Supplemental Figs.  1 a-d.

A total of 303/10,486 (2.89%, CI: 2.59, 3.23%) medical students intended to leave the profession entirely, either immediately after graduation ( n  = 104/303, 34.32%, CI: 29.20, 39.84%), after completion of FY1 ( n  = 132/303, 43.56%, CI: 38.1, 49.19%), or after completion of FY2 ( n  = 67/303, 22.11%, CI: 17.8, 27.12%). Figure  1 illustrates the distribution of these students throughout UK medical schools as a percentage of total response numbers per school. The medical schools of Cambridge, Oxford, and Imperial College medical schools had the highest proportion of students intending to leave the profession altogether.

Proportion of Medical Students Intending to Leave the Profession Across UK Medical Schools. The figure depicts the percentage of students at each UK medical school who intend to exit the medical field entirely. Percentages are calculated as a proportion of total respondents from each individual school

Furthermore, 32.35% of participating medical students ( n  = 3392/10,486, CI: 31.46, 33.25%) expressed intentions to emigrate to practise medicine, either immediately after graduation ( n  = 220/3292, 6.49%, CI: 5.71, 7.36%), after completion of FY1 ( n  = 1101/3292 32.46%, CI: 30.90, 34.05%) or after FY2 ( n  = 2071/3292, 61.06%, CI: 59.40, 62.68%). Figure  2 a demonstrates the distribution of these intentions across UK medical schools, relative to total response rates per school. Notably, Ulster University had the highest proportion of students considering emigration (45.45%), in contrast to Edge Hill, where 19.64% held similar intentions. Among students intending to emigrate, 49.56% ( n  = 1681, CI: 47.88, 51.24%) planned a return to the UK after a few years abroad, while 7.87% ( n  = 267, CI: 7.01, 8.83%) expected to return after completing their medical training abroad. The remaining 42.57% ( n  = 1444, CI: 40.92, 44.24%) expressed no plans to return to practise in the UK, as demonstrated in Fig.  2 b.

Proportion of Medical Students Intending to Emigrate Across UK Medical Schools (a) and Return Prospects (b). a illustrates the proportion of students from each UK medical school who intend to emigrate for medical practice, relative to total respondents from each school. b delineates the return prospects among students planning to emigrate

Of the 8806 respondents intending to complete both FY1 and FY2, 48.76% ( n  = 4294, CI: 47.72, 49.81%) planned to enter specialty training in the UK immediately thereafter; 21.11% ( n  = 1859, CI: 20.27, 21.98%) intended to enter a non-training clinical job in the UK (commonly comprising an ‘F3’ year, including a junior clinical fellowship or clinical teaching fellowship, or in locum roles). These ‘non-training’ roles, although valuable for gaining clinical experience, are largely standalone posts which do not contribute to accreditation within medical specialties. The school with the highest proportion of responses indicating plans to enter specialty training immediately after FY2 was Edge Hill (64.29%), whereas at Cardiff only 25.62% shared this intention. Cardiff students were also most likely to plan to enter non-training clinical posts after FY2, at 29.06%. Students from the University of Buckingham were, by far, the least likely to look to pursue non-training posts (2.70%). Figure  3 a and b present the distribution of these intentions across UK medical schools.

Distribution of Post-Foundation Programme Career Intentions Among UK Medical Students by School. a illustrates the proportion of students at each UK medical school intending to enter specialty training immediately following the Foundation Programme. b presents the proportion of students planning to enter non-training clinical roles (comprising ‘F3’ year roles, junior clinical fellowships, clinical teaching fellowships, or locum positions) in the UK after FY2

In total, 35.23% (3695/10,486) of medical students intend to leave the NHS within 2 years of graduating, either to practise abroad or leave medicine. Respondents from Brighton and Sussex Medical School expressed this intention most often (47.78%), whilst those from Aston Medical School were the least likely to do so (20.77%) (Fig.  4 ).

Proportion of UK Medical Students Intending to Leave the NHS Within 2 Years of Graduation, by School

To better ascertain the medical student population’s sentiments towards working in the NHS, respondents were asked to share their degree of satisfaction with several factors. Likert scale matrices were employed, with options ranging from ‘Very satisfied’ to ‘Not at all satisfied’. An important aspect was students’ overall satisfaction with the prospect of working within the NHS, with which only 17.26% of students were either satisfied or very satisfied. This figure varied substantially by institution as illustrated in Fig.  5 . Surveyed students from Barts and the London, Liverpool, and King’s College London GKT schools of medicine were the most dissatisfied, with dissatisfaction rates of 76.07, 72.48 and 66.84% respectively. Conversely, students from Aberdeen (43.27%), Buckingham (34.78%) and Ulster medical schools (33.33%) were those least dissatisfied with the prospect of working in the NHS.

Medical Students’ Overall Satisfaction with the Prospect of Working in the NHS, by School. The figure illustrates the variation in levels of career satisfaction across UK medical schools

Principal findings

This study identified considerable institutional variation in students’ career intentions and sentiment about their future careers.

Our results show that, in each UK medical school, over a fifth of participating medical students intend to leave the NHS within 2 years of graduation – and in some medical schools, this figure was approximately half. Nationally, this figure surpassed a third of surveyed medical students. Most would-be leavers plan to emigrate, many permanently, while a notable minority of respondents plan to leave the profession altogether. Here, we consider possible reasons for these trends, and offer potential means of adapting medical schools to avert the loss of these medics from the NHS workforce.

The levels of satisfaction among medical students concerning their prospective employment within the NHS displayed marked disparities, influenced potentially by institutional factors. In certain schools, up to 76% of students expressed dissatisfaction with the prospect of a career within the NHS, contrasted with the 48% recorded in others. The national average of 60% dissatisfaction is concerning and warrants further investigation to identify the underlying causes of this marked variability across different medical schools. Understanding the specific factors influencing medical students’ satisfaction levels could be critical in developing strategies to improve their perceptions of careers in the NHS.

Differing career sentiment between medical schools

Many differences exist between medical schools, some inherent or incidental, and others the result of decisions taken by medical faculties. Naturally, there is variation by geography, in the clinical environments and patient populations to which students are exposed, or in differences in the NHS between the UK’s devolved nations. The composition of the student body, in terms of various demographic characteristics also differs considerably between schools (Supplemental Figs.  1 a-d). Additionally, despite meeting minimum standards set by the GMC, medical schools are distinct in their curriculum delivery and priorities, culture, and other factors. This ‘hidden curriculum’ can be influential in students’ outlook towards medicine and their careers [ 9 ]. Medical schools’ autonomy extends to setting local recruitment practices, leading to differences in entry requirements and favoured attributes for which candidates are selected [ 10 ].

Curriculum focus and its influence

Certain faculties may favour students for academic potential or other attributes that may not necessarily correspond to their aptitude or interest in clinical medicine. At these schools, medical curricula may be more science-focused, such as by employing the ‘traditional’ model of medical education which firmly separates preclinical and clinical studies. During the early years of study, in which clinical exposure is low, students may find themselves detached from the medical field and begin considering alternative careers. This may be especially true where intercalated degrees form mandatory components of the curriculum – the receipt of which would enable pursuit of graduate roles or postgraduate degrees. Moreover, some institutions emphasising academic achievement may offer academic opportunities which could further distance those enrolled from the profession. For instance, previous graduates of MB/PhD programmes, an option to intercalate a PhD degree offered by only a limited number of universities, have gone onto careers in academia, industry, and business [ 11 , 12 ].

Recruitment practices

Despite the inherent importance of academic ability, it is important to recognise that a ‘good’ doctor requires a balance of various attributes including empathy, resilience, and communication skills. Furthermore, a clear understanding and realistic expectations of the profession are critical. The possible discrepancy between academic aptitude and the day-to-day reality of medical practice may be a contributing factor to the observed trends of students contemplating leaving the profession. Therefore, ensuring a balanced and holistic approach in selection processes could contribute to cultivating a workforce committed to pursuing medical practice in the NHS long term. Currently, prospective students undergo varying forms of interviews, which, due to their brevity and the substantial volume of applications, may not adequately capture a candidate’s realistic expectations and motivations towards a medical career. To increase the robustness of the selection process, medical schools should consider revisiting the structure of their interview processes, potentially incorporating methods to more accurately assess applicants’ understanding and enthusiasm for a medical career within the NHS more accurately. This approach could include comprehensive discussions focusing on the complexities and realities associated with a medical career [ 13 ]. Moreover, there are relevant differences in institutions’ selection criteria, with some valuing extracurricular activities, while some place greater emphasis on personal statements more, and others prioritise results achieved in admission exams [ 10 ]. Implementing such changes in the recruitment process can be a proactive step towards retaining talent within the NHS and encouraging more students to envisage a fulfilling career within the medical profession.

Institutional reputation

Respondents from institutions which place highly in national and international university rankings exhibited a greater propensity to consider leaving the profession [ 14 , 15 ]. Notably, the universities of Cambridge (8.59%), Oxford (8.26%), and Imperial College London (8.24%) led this trend. Attending these, and other, historically prestigious schools, may boost non-clinical career opportunities, so their students may be attracted to the perceived benefits of alternative careers over those in clinical practice. This institutional reputation may have initially attracted some students, for whom the career opportunities outside clinical practice now offer more compelling options compared to working in the NHS. This, coupled with growing reports of doctors looking to leave the health service, may partly explain the trend observed [ 3 ]. However, it is important to note that this phenomenon is neither new nor limited to the UK, with a 2001 study identifying growing numbers of medical students in the United States intending to pursue non-clinical, non-academic careers over time [ 16 ]. Notably, only four schools had 0% of students intending to leave the profession.

Demographic influences

Moreover, the composition of the student body, particularly in terms of demographic makeup may represent another potential influence on career intentions. For instance, if data indicate that students from certain demographics were more likely to pursue a certain career path, a school with a higher proportion of such students may appear to exhibit a similar inclination. It is important to note that these tendencies may be reflective of broader societal and demographic differences, rather than factors intrinsic to the respective institutions. A deeper analysis of demographic nuances may elucidate the intricate interplay of background and career choices, offering valuable insights for future policy and institutional strategies. Furthermore, it would be prudent to recognise that certain students, particularly those from widening participation backgrounds, may have limited agency regarding the career pathway they pursue. For some, this limitation may be financial in nature or due to caring responsibilities, while for others it may be more strongly related to the awarding gap [ 17 ].

Proposed solutions and future directions

Our findings underscore the need to explore the reasons for the observed disparities in students’ career sentiment across medical schools. Using this information, medical courses may be adapted to improve students’ feelings about their future medical careers in the NHS or otherwise. As students’ perspectives are guided by their educational experiences, undergraduate training they deem suboptimal could contribute to a diminished enthusiasm for a career in medicine. Higher standards of teaching may increase interest and engagement in the medical profession, while inadequate teaching quality could engender frustration and disillusionment. Unsatisfied students may opt to pursue alternative careers or relocate to destinations where they perceive education and training standards to be higher [ 18 ]. To substantiate this, further studies could endeavour to quantify perceptions towards teaching standards at medical school and the impact of teaching quality on students’ career choices, potentially guiding improvements in curriculum design and faculty development.

It is important to note that many respondents will have been studying medicine during the COVID-19 pandemic. During this period, medical schools had the difficult task of balancing infection risk with maintaining educational standards. Centres will have differed in their approach, and negative experiences - educational or otherwise - from this period may have adversely influenced students’ attitudes towards medicine [ 19 ].

Furthermore, the structure or variety of clinical placements used by some medical schools could more effectively convey a positive outlook of medical careers or the NHS. This is often contingent on the clinical environments in which medical students rotate. For instance, limited exposure to certain specialties or sub-specialties—only available at select centres—may inadvertently obscure potentially rewarding career paths. Similarly, limited opportunities in rural medicine, public health, or other non-hospital-based pathways may also achieve the same effect [ 20 ]. Spaces and learning opportunities may also be shared with increasing cohort sizes or, depending upon geography, with students from other medical schools, potentially diluting learning opportunities [ 21 ]. Staffing levels, workplace culture and health outcomes also vary geographically, both within and between the UK’s devolved nations [ 22 , 23 , 24 , 25 ]. These factors inform students’ perceptions of the career and may contribute to their decision-making. To mitigate this, medical faculties would benefit from establishing or expanding student feedback mechanisms. The objective is to identify factors affecting training experiences and to ensure equitable access across the UK, irrespective of the medical school attended.. Such engagement may also reveal which career paths are under-explored in individual medical curricula. In response to students’ views, or from faculties’ own understanding of where these deficits may lie, schools may consider offering means of addressing this, such as through optional specialty taster days.

Where higher proportions of students expressed interests in either relocating to work abroad or in leaving the profession entirely, there may be benefit in fostering a culture of mentorship and guidance around medical careers. Mentorship can support students to navigate systems used during applications for increasingly competitive specialty training programmes [ 26 , 27 ]. Guidance from medics acquainted with these processes can support students to pursue their preferred specialty and could consequently reduce attrition by improving their perceived career prospects.

Findings in context

The AIMS study highlighted a wide range of factors which contribute to medical students’ career sentiment and their intended career trajectory [ 5 ]. Here, we explored the role of medical schools in this complex equation and, although influential, this must be considered in that wider context. While national policy reform addressing factors such as remuneration and working conditions are required to reverse current trends in students’ career intentions, the strategies proposed in this manuscript may serve to address regional disparities.

Limitations

Despite the AIMS study constituting the largest ever study of UK medical students, due to the methods of dissemination, the number of students who saw the invitation to participate in the study is unknown, and therefore we are unable to calculate the response rate. Consequently, the sample may have been subject to selection bias, possibly driven by greater response rates among students with existing interests in this subject. Additionally, the questions in our survey instruct students to be definitive even when they might not yet have formulated their career plans, a not-improbable situation, particularly for those in the early years of medical school.

Moreover, being a cross-sectional study, it is not possible to comment on changes to medical students’ career sentiment with time. Although informed by their undergraduate training and experiences therefrom, at the time of participation, respondents had not yet worked as medical doctors. As such, their opinions may change once immersed in the career and working in the health service. In anticipation of this limitation, the questionnaire sought consent for a planned follow-up study, to which a 71.29% positive response rate was captured. It is hoped that this study’s findings may be validated by tracking changes in sentiment over time.

Importantly, there was also variability in the number of responses achieved from each medical school. This occurred despite recruitment of a large medical student collaborator network. This discrepancy might be attributed to various factors, including the approach of dissemination undertaken by university or medical school administrators, the design of clinical placements, or the presence and influence of local student societies, among other considerations. To avoid potential misrepresentation due to inadequate sample sizes, we opted to exclude data from the two medical schools that obtained fewer than 30 responses.

While the broader trends of medical students intending to leave the NHS within 2 years of graduating are concerning, the variation in career sentiment across UK medical schools requires consideration. This analysis implicates a complex interplay of factors—ranging from curriculum focus and cohort demographics to recruitment strategies, teaching quality, and clinical experience—in shaping these career intentions. Such variation in career sentiment between institutions may be indicative of deeper issues, possibly rooted in educational approaches and experiences at undergraduate level - on which the potential impact of the COVID-19 pandemic should be noted.

It is evident that approaches taken to recruitment, educational framework, and support within medical schools require reassessment. Subsequent investigations should examine the underlying causes of disparities in career sentiment by institution, aiming to cultivate resilience, dedication, and - critically - professional fulfilment among the future medical workforce in the UK.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request once all planned subsequent analyses are completed.

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Acknowledgements

We would like to thank all students that participated in this study.

The AIMS Collaborative.

Tomas Ferreira 1 , Alexander M. Collins 2 , Rita Horvath 1 , Oliver Feng 4 , Richard J Samworth 4 , Mario K Teo 6 , Crispin C Wigfield 6 , Maeve K Mulchrone 7 , Alisha Pervaiz 8 , Heather A Lewis 7 , Anson Wong 7 , Buzz Gilks 1 , Charlotte Casteleyn 9 , Sara Kidher 10 , Erin Fitzsimons-West 1 , Tanzil Rujeedawa 1 , Meghna Sreekumar 1 , Eliza Wade 11 , Juel Choppy-Madeleine 8 , Yasemin Durmus 12 , Olivia King 10 , Yu Ning Ooi 8 , Malvi Shah 9 , Tan Jit Yih 13 , Samantha Burley 1 , Basma R Khan 4 , Emma Slack 1 , Rishik S Pilla 14 , Jenny Yang 1 , Vaishvi Dalal 8 , Brennan L Gibson 7 , Emma Westwood 9 , Brandon S H Low 6 , Sara R Sabur 9 , Wentin Chen 7 , Maryam A Malik 9 , Safa Razzaq 10 , Amardeep Sidki 10 , Giulia Cianci 15 , Felicity Greenfield 3 , Sajad Hussain 3 , Alexandra Thomas 11 , Annie Harrison 16 , Hugo Bernie 3 , Luke Dcaccia 11 , Linnuel J Pregil 13 , Olivia Rowe 11 , Ananya Jain 17 , Gregory K Anyaegbunam 8 , Syed Z Jafri 18 , Sudhanvita Arun 4 , Alfaiya Hashmi 19 , Ankith Pandian 15 , Joseph R Nicholson 20 , Hannah Layton-Joyce 21 , Kouther Mohsin 7 , Matilda Gardener 3 , Eunice C Y Kwan 18 , Emily R Finbow 4 , Sakshi Roy 22 , Zoe M Constantinou 13 , Mackenzie Garlick 3 , Clare L Carney 23 , Samantha Gold 24 , Bilal Qureshi 25 , Daniel Magee 26 , Grace Annetts 25 , Khyatee Shah 27 , Kholood T Munir 14 , Timothy Neill 22 , Gurpreet K Atwal 28 , Anesu Kusosa 18 , Anthony Vijayanathan 14 , Mia Mäntylä 8 , Momina Iqbal 27 , Sara Raja 29 , Tushar Rakhecha 3 , Muhammad H Shah 22 , Pranjil Pokharel 30 , Ashna Anil 31 , Kate Stenning 21 , Katie Appleton 18 , Keerthana Uthayakumar 28 , Rajan Panacer 32 , Yasmin Owadally 17 , Dilaxiha Rajendran 33 , Harsh S Modalavalasa 15 , Marta M Komosa 13 , Morea Turjaka 18 , Sruthi Saravanan 27 , Amelia Dickson 24 , Jack M Read 24 , Georgina Cooper 26 , Wing Chi Do 34 , Chiamaka Anthony-Okeke 35 , Daria M Bageac 24 , David C W Loh 28 , Rida Khan 19 , Ruth Omenyo 31 , Aidan Baker 34 , Imogen Milner 23 , Kavyesh Vivek 17 , Manon Everard 36 , Wajiha Rahman 14 , Denis Chen 26 , Michael E Bryan 34 , Shama Maliha 26 , Vera Onongaya 31 , Amber Dhoot 17 , Catherine L Otoibhi 35 , Harry Donkin-Everton 14 , Mia K Whelan 24 , Claudia S F Hobson 37 , Anthony Haynes 20 , Joshua Bayes-Green 35 , Mariam S Malik 28 , Subanki Srisakthivel 24 , Sophie Kidd 28 , Alan Saji 11 , Govind Dhillon 16 , Muhammed Asif 38 , Riya Patel 30 , Jessica L Marshall 20 , Nain T Raja 29 , Tawfique Rizwan 38 , Aleksandra Dunin- Borkowska 17 , James Brawn 23 , Karthig Thillaivasan 9 , Zainah Sindhoo 27 , Ayeza Akhtar 25 , Emma Hitchcock 36 , Kelly Fletcher 38 , Lok Pong Cheng 37 , Medha Pillaai 28 , Sakshi Garg 15 , Wajahat Khan 12 , Ben Sweeney 20 , Ria Bhatt 39 , Madison Slight 40 , Adan M I Chew 32 , Cameron Thurlow 41 , Kriti Yadav 39 , Niranjan Rajesh 39 , Nathan-Dhruv Mistry 16 , Alyssa Weissman 37 , Juan F E Jaramillo 30 , William Thompson 42 , Gregor W Abercromby 20 , Emily Gaskin 4 , Chloe Milton 43 , Matthew Kokkat 36 , Momina Hussain 26 , Nana A Ohene-Darkoh 39 , Syeda T Islam 33 , Anushruti Yadav 31 , Eve Richings 44 , Samuel Foxcroft 44 , Sukhdev Singh 32 , Vivek Sivadev 40 , Guilherme Movio 30 , Ellena Leigh 45 , Harriet Charlton 44 , James A Cairn 45 , Julia Shaaban 23 , Leah Njenje 43 , Mark J Bishop 44 , Humairaa Ismail 30 , Sarah L Henderson 44 , Daniel C Chalk 20 , Daniel J Mckenna 26 , Fizah Hasan 43 , Kanishka Saxena 32 , Iona E Gibson 44 and Saad Dosani 38 .

1 School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.

2 School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom.

3 Bristol Medical School, University of Bristol, Bristol, United Kingdom.

4 Sheffield Medical School, University of Sheffield, Sheffield, United Kingdom.

5 Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK.

6 Department of Neurosurgery, Southmead Hospital, Bristol, UK.

7 School of Medicine, University of Birmingham, Birmingham.

8 School of Medicine, University of Glasgow, Glasgow.

9 UCL Medical School, University College London, London.

10 School of Medicine, University of Leicester, Leicester, UK.

11 School of Medicine, University of Southampton, Southampton, UK.

12 School of Medicine, University of Leeds, Leeds, UK.

13 School of Medicine and Dentistry, Queen Mary University of London, London, UK.

14 GKT School of Medical Education, King’s College London, London, UK.

15 School of Medicine, University of Nottingham, Nottingham, UK.

16 School of Medicine, University of Liverpool, Liverpool, UK.

17 Faculty of Medicine, Imperial College London, London, UK.

18 Norwich Medical School, University of East Anglia, Norwich, UK.

19 St George’s, University of London, London, UK.

20 Peninsula Schools of Medicine and Dentistry, Plymouth University, Plymouth, UK.

21 School of Medicine, University of Warwick, Coventry, UK.

22 School of Medicine, Queen’s University Belfast, Belfast, UK.

23 School of Medicine, Swansea University, Swansea, UK.

24 School of Medicine, University of Exeter, Exeter, UK.

25 Medical Sciences Division, University of Oxford, Oxford, UK.

26 School of Medicine, Keele University, Keele, UK.

27 Lincoln Medical School, University of Nottingham, Lincoln, UK.

28 School of Medicine, University of Dundee, Dundee, UK.

29 School of Medicine and Dentistry, University of Aberdeen, Aberdeen, UK.

30 School of Medicine, Lancaster University, Lancaster, UK.

31 School of Medicine, Cardiff University, Cardiff, UK.

32 School of Medicine, Aston University, Birmingham, UK.

33 School of Medicine, University of Sunderland, Sunderland, UK.

34 School of Medicine, The University of Edinburgh, Edinburgh, UK.

35 School of Medical Education, Newcastle University, Newcastle, UK.

36 Hull York Medical School, Hull and York, UK.

37 School of Medicine, University of Buckingham, Buckingham, UK.

38 School of Medicine, University of Manchester, Manchester, UK.

39 School of Medicine, University of Central Lancashire, Preston, UK.

40 School of Medicine, University of St Andrews, St Andrews, UK.

41 Brighton and Sussex Medical School, Brighton and Sussex, UK.

42 School of Medicine, Ulster University, Coleraine, UK.

43 School of Medicine, Anglia Ruskin University, Chelmsford, UK.

44 Scottish Graduate Entry Medicine (ScotGEM) Programme, Universities of St Andrews and Dundee, Scotland, UK.

45 School of Medicine, Edge Hill University, Ormskirk, UK.

TF is the guarantor.

Queens’ College, University of Cambridge. The institution has had no role in the design of the study, nor collection, analysis, and interpretation of data and in writing the manuscript.

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• Tomas Ferreira

School of Public Health, Faculty of Medicine, Imperial College London, London, UK

Alexander M. Collins

Bristol Medical School, University of Bristol, Bristol, UK

Tomas Ferreira, Alexander M. Collins & Arthur Handscomb

Sheffield Medical School, University of Sheffield, Sheffield, UK

Dania Al-Hashimi

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• , Alexander M. Collins
• , Rita Horvath
• , Oliver Feng
• , Richard J. Samworth
• , Mario K. Teo
• , Crispin C. Wigfield
• , Maeve K. Mulchrone
• , Alisha Pervaiz
• , Heather A. Lewis
• , Anson Wong
• , Buzz Gilks
• , Charlotte Casteleyn
• , Sara Kidher
• , Erin Fitzsimons-West
• , Tanzil Rujeedawa
• , Meghna Sreekumar
• , Eliza Wade
• , Juel Choppy-Madeleine
• , Yasemin Durmus
• , Olivia King
• , Yu Ning Ooi
• , Malvi Shah
• , Tan Jit Yih
• , Samantha Burley
• , Basma R. Khan
• , Emma Slack
• , Rishik S. Pilla
• , Jenny Yang
• , Vaishvi Dalal
• , Brennan L. Gibson
• , Emma Westwood
• , Brandon S. H. Low
• , Sara R. Sabur
• , Wentin Chen
• , Maryam A. Malik
• , Safa Razzaq
• , Amardeep Sidki
• , Giulia Cianci
• , Felicity Greenfield
• , Sajad Hussain
• , Alexandra Thomas
• , Annie Harrison
• , Hugo Bernie
• , Luke Dcaccia
• , Linnuel J. Pregil
• , Olivia Rowe
• , Ananya Jain
• , Gregory K. Anyaegbunam
• , Syed Z. Jafri
• , Sudhanvita Arun
• , Alfaiya Hashmi
• , Ankith Pandian
• , Joseph R. Nicholson
• , Hannah Layton-Joyce
• , Kouther Mohsin
• , Matilda Gardener
• , Eunice C. Y. Kwan
• , Emily R. Finbow
• , Sakshi Roy
• , Zoe M. Constantinou
• , Mackenzie Garlick
• , Clare L. Carney
• , Samantha Gold
• , Bilal Qureshi
• , Daniel Magee
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T.F. responsible for conceptualisation. T.F. responsible for obtaining funding and ethical approval. T.F. responsible for collaborator recruitment and management. T.F. responsible for project administration. All authors responsible for writing the manuscript. All authors responsible for editing and revising the manuscript. T.F. responsible for supervision. T.F. is the guarantor. All authors have read and approved the manuscript.

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Ferreira, T., Collins, A.M., Handscomb, A. et al. The role of medical schools in UK students’ career intentions: findings from the AIMS study. BMC Med Educ 24 , 604 (2024). https://doi.org/10.1186/s12909-024-05366-6

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Where will the world's first genetic sequencing of a corn dog take place at the minnesota state fair.

There are lots of things that happen only at the Minnesota State Fair: Butter busts . Costumed llamas . Traffic-cone-flavored lip balm .

This summer you can add another: Gene sequencing of a corn dog.

University of Minnesota researchers are aiming to crack the genetic code of the quintessential State Fair snack at the school's Driven to Discover research building on the fairgrounds.

It's a lighthearted way to draw attention to the more serious research being done by university scientists to track infectious diseases. Gene sequencing technology is being used at the U for everything from tracking respiratory pathogens, to breeding more productive cows, to developing better cancer treatments.

The corn dog project is the brainchild of Dr. Beth Thielen, assistant professor of pediatrics and an infectious-disease expert at the U of M Medical School, and Christopher Faulk, associate professor of functional genomics at the university's Department of Animal Science.

Thielen is hoping to use the corn dog project to recruit potential participants at the fair for a study formally titled "Prospective Surveillance for Respiratory Pathogens and Antimicrobial Resistance in Diverse Regional Populations: A Pathogen Genomics Center of Excellence Collaboration between the Minnesota Department of Health and University of Minnesota."

The informal name for the study is "Got Snot?"

The mission of the study is to follow 500 households in Minnesota and neighboring states, asking them to mail in nasal swabs if they get sick. The researchers will then use the latest gene sequencing technology to figure out which respiratory diseases are emerging and how they're spreading.

"We wanted to show prospective participants how nucleic acid sequencing works and from there the corn dog sequencing experiment was born," Thielen said. "For safety reasons, we didn't think working with actual infectious agents at the fair was a good idea."

All organisms — from respiratory bugs to the once-living ingredients in a corn dog — have genetic material that can be sequenced.

"Since all once-living things have nucleic acid, we figured sequencing a corn dog would be a good tribute to the fair," Thielen said.

But what will they learn about corn dogs?

"So our hypothesis is that a corn dog contains components of formerly living organisms. So there's corn, which has DNA. There's beef, we think," Thielen said. "By reading out the sequences that we get, we can determine the relative proportions of those different materials that contributed to the corn dog in our sample."

Thielen and Faulk will also be showing off advances in gene sequencing equipment.

Instead of large machines costing millions of dollars, the researchers will be bringing to the fairgrounds a sequencing device the size of a Snickers bar that plugs into a laptop and costs less than $2,000.

It's the sort of portable gear Thielen and Faulk will be using overseas if they get funding to expand their emerging pathogen tracking research in places like Uganda, Malaysia and Ecuador.

Could the experiment win an Ig Nobel Prize , the annual Harvard-based ceremony that celebrates offbeat research?

"We can only hope," Thielen said. "All publicity is good publicity. It's a little bit of a fun experiment, but underlying it is an important point which is this is something that's up and coming and has a lot of potential for a lot of areas, and we want people to learn about it and be excited about science."

Richard Chin is a feature reporter with the Star Tribune in Minneapolis. He has been a longtime Twin Cities-based journalist who has covered crime, courts, transportation, outdoor recreation and human interest stories.

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What does the public in six countries think of generative ai in news.

An electronic screen displaying Japan's Nikkei share average. Credit: Reuters / Issei Kato

DOI: 10.60625/risj-4zb8-cg87

Executive summary.

Based on an online survey focused on understanding if and how people use generative artificial intelligence (AI), and what they think about its application in journalism and other areas of work and life across six countries (Argentina, Denmark, France, Japan, the UK, and the USA), we present the following findings.

Findings on the public’s use of generative AI

ChatGPT is by far the most widely recognised generative AI product – around 50% of the online population in the six countries surveyed have heard of it. It is also by far the most widely used generative AI tool in the six countries surveyed. That being said, frequent use of ChatGPT is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have used it just once or twice, and it is yet to become part of people’s routine internet use.

In more detail, we find:

• While there is widespread awareness of generative AI overall, a sizable minority of the public – between 20% and 30% of the online population in the six countries surveyed – have not heard of any of the most popular AI tools.
• In terms of use, ChatGPT is by far the most widely used generative AI tool in the six countries surveyed, two or three times more widespread than the next most widely used products, Google Gemini and Microsoft Copilot.
• Younger people are much more likely to use generative AI products on a regular basis. Averaging across all six countries, 56% of 18–24s say they have used ChatGPT at least once, compared to 16% of those aged 55 and over.
• Roughly equal proportions across six countries say that they have used generative AI for getting information (24%) as creating various kinds of media, including text but also audio, code, images, and video (28%).
• Just 5% across the six countries covered say that they have used generative AI to get the latest news.

Findings on public opinion about the use of generative AI in different sectors

Most of the public expect generative AI to have a large impact on virtually every sector of society in the next five years, ranging from 51% expecting a large impact on political parties to 66% for news media and 66% for science. But, there is significant variation in whether people expect different sectors to use AI responsibly – ranging from around half trusting scientists and healthcare professionals to do so, to less than one-third trusting social media companies, politicians, and news media to use generative AI responsibly.

• Expectations around the impact of generative AI in the coming years are broadly similar across age, gender, and education, except for expectations around what impact generative AI will have for ordinary people – younger respondents are much more likely to expect a large impact in their own lives than older people are.
• Asked if they think that generative AI will make their life better or worse, a plurality in four of the six countries covered answered ‘better’, but many have no strong views, and a significant minority believe it will make their life worse. People’s expectations when asked whether generative AI will make society better or worse are generally more pessimistic.
• Asked whether generative AI will make different sectors better or worse, there is considerable optimism around science, healthcare, and many daily routine activities, including in the media space and entertainment (where there are 17 percentage points more optimists than pessimists), and considerable pessimism for issues including cost of living, job security, and news (8 percentage points more pessimists than optimists).
• When asked their views on the impact of generative AI, between one-third and half of our respondents opted for middle options or answered ‘don’t know’. While some have clear and strong views, many have not made up their mind.

Findings on public opinion about the use of generative AI in journalism

Asked to assess what they think news produced mostly by AI with some human oversight might mean for the quality of news, people tend to expect it to be less trustworthy and less transparent, but more up to date and (by a large margin) cheaper for publishers to produce. Very few people (8%) think that news produced by AI will be more worth paying for compared to news produced by humans.

• Much of the public think that journalists are currently using generative AI to complete certain tasks, with 43% thinking that they always or often use it for editing spelling and grammar, 29% for writing headlines, and 27% for writing the text of an article.
• Around one-third (32%) of respondents think that human editors check AI outputs to make sure they are correct or of a high standard before publishing them.
• People are generally more comfortable with news produced by human journalists than by AI.
• Although people are generally wary, there is somewhat more comfort with using news produced mostly by AI with some human oversight when it comes to soft news topics like fashion (+7 percentage point difference between comfortable and uncomfortable) and sport (+5) than with ‘hard’ news topics, including international affairs (-21) and, especially, politics (-33).
• Asked whether news that has been produced mostly by AI with some human oversight should be labelled as such, the vast majority of respondents want at least some disclosure or labelling. Only 5% of our respondents say none of the use cases we listed need to be disclosed.
• There is less consensus on what uses should be disclosed or labelled. Around one-third think ‘editing the spelling and grammar of an article’ (32%) and ‘writing a headline’ (35%) should be disclosed, rising to around half for ‘writing the text of an article’ (47%) and ‘data analysis’ (47%).
• Again, when asked their views on generative AI in journalism, between a third and half of our respondents opted for neutral middle options or answered ‘don’t know’, reflecting a large degree of uncertainty and/or recognition of complexity.

Introduction

The public launch of OpenAI’s ChatGPT in November 2022 and subsequent developments have spawned huge interest in generative AI. Both the underlying technologies and the range of applications and products involving at least some generative AI have developed rapidly (though unevenly), especially since the publication in 2017 of the breakthrough ‘transformers’ paper (Vaswani et al. 2017) that helped spur new advances in what foundation models and Large Language Models (LLMs) can do.

These developments have attracted much important scholarly attention, ranging from computer scientists and engineers trying to improve the tools involved, to scholars testing their performance against quantitative or qualitative benchmarks, to lawyers considering their legal implications. Wider work has drawn attention to built-in limitations, issues around the sourcing and quality of training data, and the tendency of these technologies to reproduce and even exacerbate stereotypes and thus reinforce wider social inequalities, as well as the implications of their environmental impact and political economy.

One important area of scholarship has focused on public use and perceptions of AI in general, and generative AI in particular (see, for example, Ada Lovelace Institute 2023; Pew 2023). In this report, we build on this line of work by using online survey data from six countries to document and analyse public attitudes towards generative AI, its application across a range of different sectors in society, and, in greater detail, in journalism and the news media specifically.

We go beyond already published work on countries including the USA (Pew 2023; 2024), Switzerland (Vogler et al. 2023), and Chile (Mellado et al. 2024), both in terms of the questions we cover and specifically in providing a cross-national comparative analysis of six countries that are all relatively privileged, affluent, free, and highly connected, but have very different media systems (Humprecht et al. 2022) and degrees of platformisation of their news media system in particular (Nielsen and Fletcher 2023).

The report focuses on the public because we believe that – in addition to economic, political, and technological factors – public uptake and understanding of generative AI will be among the key factors shaping how these technologies are being developed and are used, and what they, over time, will come to mean for different groups and different societies (Nielsen 2024). There are many powerful interests at play around AI, and much hype – often positive salesmanship, but sometimes wildly pessimistic warnings about possible future risks that might even distract us from already present issues. But there is also a fundamental question of whether and how the public at large will react to the development of this family of products. Will it be like blockchain, virtual reality, and Web3? All promoted with much bombast but little popular uptake so far. Or will it be more like the internet, search, and social media – hyped, yes, but also quickly becoming part of billions of people’s everyday media use.

To advance our understanding of these issues, we rely on data from an online survey focused on understanding if and how people use generative AI, and what they think about its application in journalism and other areas of work and life. In the first part of the report, we present the methodology, then we go on to cover public awareness and use of generative AI, expectations for generative AI’s impact on news and beyond, how people think AI is being used by journalists right now, and how people think about how journalists should use generative AI, before offering a concluding discussion.

As with all survey-based work, we are reliant on people’s own understanding and recall. This means that many responses here will draw on broad conceptions of what AI is and might mean, and that, when it comes to generative AI in particular, people are likely to answer based on their experience of using free-standing products explicitly marketed as being based on generative AI, like ChatGPT. Most respondents will be less likely to be thinking about incidents where they may have come across functionalities that rely in part on generative AI, but do not draw as much attention to it – a version of what is sometimes called ‘invisible AI’ (see, for example, Alm et al. 2020). We are also aware that these data reflect a snapshot of public opinion, which can fluctuate over time.

We hope the analysis and data published here will help advance scholarly analysis by complementing the important work done on the use of AI in news organisations (for example, Beckett and Yaseen 2023; Caswell 2024; Diakopoulos 2019; Diakopoulos et al 2024; Newman 2024; Simon 2024), including its limitations and inequities (see, for example, Broussard 2018, 2023; Bender et al. 2021), and help centre the public as a key part of how generative AI will develop and, over time, potentially impact many different sectors of society, including journalism and the news media.

Methodology

The report is based on a survey conducted by YouGov on behalf of the Reuters Institute for the Study of Journalism (RISJ) at the University of Oxford. The main purpose is to understand if and how people use generative AI, and what they think about its application in journalism and other areas of work and life.

The data were collected by YouGov using an online questionnaire fielded between 28 March and 30 April 2024 in six countries: Argentina, Denmark, France, Japan, the UK, and the USA.

YouGov was responsible for the fieldwork and provision of weighted data and tables only, and RISJ was responsible for the design of the questionnaire and the reporting and interpretation of the results.

Samples in each country were assembled using nationally representative quotas for age group, gender, region, and political leaning. The data were weighted to targets based on census or industry-accepted data for the same variables.

Sample sizes are approximately 2,000 in each country. The use of a non-probability sampling approach means that it is not possible to compute a conventional ‘margin of error’ for individual data points. However, differences of +/- 2 percentage points (pp) or less are very unlikely to be statistically significant and should be interpreted with a very high degree of caution. We typically do not regard differences of +/- 2pp as meaningful, and as a general rule we do not refer to them in the text.

It is important to note that online samples tend to under-represent the opinions and behaviours of people who are not online (typically those who are older, less affluent, and have limited formal education). Moreover, because people usually opt in to online survey panels, they tend to over-represent people who are well educated and socially and politically active.

Some parts of the survey require respondents to recall their past behaviour, which can be flawed or influenced by various biases. Additionally, respondents’ beliefs and attitudes related to generative AI may be influenced by social desirability bias, and when asked about complex socio-technical issues, people will not always be familiar with the terminology experts rely on or understand the terms the same way. We have taken steps to mitigate these potential biases and sources of error by implementing careful questionnaire design and testing.

1. Public awareness and use of generative AI

Most of our respondents have, by now, heard of at least some of the most popular generative AI tools. ChatGPT is by far the most widely recognised of these, with between 41% (Argentina) and 61% (Denmark) saying they’d heard of it.

Other tools, typically those built by incumbent technology companies – such as Google Gemini, Microsoft Copilot, and Snapchat My AI – are some way behind ChatGPT, even with the boost that comes from being associated with a well-known brand. They are, with the exception of Grok from X, each recognised by roughly 15–25% of the public.

Tools built by specialised AI companies, such as Midjourney and Perplexity, currently have little to no brand recognition among the public at large. And there’s little national variation here, even when it comes to brands like Mistral in France; although it is seen by some commentators as a national champion, it clearly hasn’t yet registered with the wider French population.

We should also remember that a sizable minority of the public – between 19% of the online population in Japan and 30% in the UK – have not heard of any of the most popular AI tools (including ChatGPT) despite nearly two years of hype, policy conversations, and extensive media coverage.

While our Digital News Report (Newman et al. 2023) shows that in most countries the news market is dominated by domestic brands that focus on national news, in contrast, the search and social platform space across countries tends to feature the same products from large technology companies such as Google, Meta, and Microsoft. At least for now, it seems like the generative AI space will follow the pattern from the technology sector, rather than the more nationally oriented one of news providers serving distinct markets defined in part by culture, history, and language.

The pattern we see for awareness in Figure 1 extends to use, with ChatGPT by far the most widely used generative AI tool in the six countries surveyed. Use of ChatGPT is roughly two or three times more widespread than the next products, Google Gemini and Microsoft Copilot. What’s also clear from Figure 2 is that, even when it comes to ChatGPT, frequent use is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have only used it once or twice, and it is yet to become part of people’s routine internet use.

Use of ChatGPT is slightly more common among men and those with higher levels of formal education, but the biggest differences are by age group, with younger people much more likely to have ever used it, and to use it on a regular basis (Figure 3). Averaging across all six countries, 16% of those aged 55 and over say they have used ChatGPT at least once, compared to 56% of 18–24s. But even among this age group infrequent use is the norm, with just over half of users saying they use it monthly or less.

Although people working in many different industries – including news and journalism – are looking for ways of deploying generative AI, people in every country apart from Argentina are slightly more likely to say they are using it in their private life rather than at work or school (Figure 4). If providers of AI products convince more companies and organisations that these tools can deliver great efficiencies and new opportunities this may change, with professional use becoming more widespread and potentially spilling over to people’s personal lives – a dynamic that was part of how the use of personal computers, and later the internet, spread. However, at this stage private use is more widespread.

Averaging across six countries, roughly equal proportions say that they have used generative AI for getting information (24%) as creating media (28%), which as a category includes creating images (9%), audio (3%), video (4%), code (5%), and generating text (Figure 5). When it comes to creating text more specifically, people report using generative AI to write emails (9%) and essays (8%), and for creative writing (e.g. stories and poems) (7%). But it’s also clear that many people who say they have used generative AI for creating media have just been playing around or experimenting (11%) rather than looking to complete a specific real-world task. This is also true when it comes to using generative AI to get information (9%), but people also say they have used it for answering factual questions (11%), advice (10%), generating ideas (9%), and summarisation (8%).

An average of 5% across the six countries say that they have used generative AI to get the latest news, making it less widespread than most of the other uses that were mentioned previously. One reason for this is that the free version of the most widely used generative AI product – ChatGPT – is not yet connected to the web, meaning that it cannot be used for the latest news. Furthermore, our previous research has shown that around half of the most widely used news websites are blocking ChatGPT (Fletcher 2024), and partly as a result, it is rarely able to deliver the latest news from specific outlets (Fletcher et al. 2024).

The figures for using generative AI for news vary by country, from just 2% in the UK and Denmark to 10% in the USA (Figure 6). The 10% figure in the USA is probably partly due to the fact that Google has been trialling Search Generative Experiences (SGE) there for the last year, meaning that people who use Google to search for a news-related topic – something that 23% of Americans do each week (Newman et al. 2023) – may see some generative AI text that attempts to provide an answer. However, given the documented limitations of generative AI when it comes to factual precision, companies like Google may well approach news more cautiously than other types of content and information, and the higher figure in the USA may also simply be because generative AI is more widely used there generally.

Numerous examples have been documented of generative AI giving incorrect answers when asked factual questions, as well as other forms of so-called ‘hallucination’ that result in poor- quality outputs (e.g. Angwin et al. 2024). Although some are quick to point out that it is wrong to expect generative AI to be good at information-based tasks – at least at its current state of development – some parts of the public are experimenting with doing exactly that.

Given the known problems when it comes to reliability and veracity, it is perhaps concerning that our data also show that users seem reasonably content with the performance – most of those (albeit a rather small slice of the online population) who have tried to use generative AI for information-based tasks generally say they trusted the outputs (Figure 7).

In interpreting this, it is important to keep in mind two important caveats.

First, the vast majority of the public has not used generative AI for information-based tasks, so we do not know about their level of trust. Other evidence suggests that trust among the large part of the public that has not used generative AI is low, meaning overall trust levels are likely to be low (Pew 2024).

Second, people are more likely to say that they ‘somewhat trust’ the outputs rather than ‘strongly trust’, which indicates a degree of scepticism – their trust is far from unconditional. However, this may also mean that from the point of view of members of the public who have used the tools, information from generative AI while clearly not perfect is already good enough for many purposes, especially tasks like generating ideas.

When we ask people who have used generative AI to create media whether they think the product they used did it well or badly, we see a very similar picture. Most of those who have tried to use generative AI to create media think that it did it ‘very’ or ‘somewhat’ well, but again, we can only use this data to know what users of the technology think.

The general population’s views on the media outputs may look very different, and while early adopters seem to have some trust in generative AI, and feel these technologies do a somewhat good job for many tasks, it is not certain that everyone will feel the same, even if or when they start using generative AI tools.

2. Expectations for generative AI’s impact on news and beyond

We now move from people’s awareness and use of generative AI products to their expectations around what the development of these technologies will mean. First, we find that most of the public expect generative AI to have a large impact on virtually every sector of society in the next five years (Figure 8). For every sector, there is a smaller number who expect low impact (compared to a large impact), and a significant number of people (roughly between 15% and 20%) who answer ‘don’t know’.

Averaging across six countries, we find that around three-quarters of respondents think generative AI will have a large impact on search and social media companies (72%), while two-thirds (66%) think that it will have a large impact on the news media – strikingly, the same proportion who think it will have a large impact upon the work of scientists (66%). Around half think that generative AI will have a large impact upon national governments (53%) and politicians and political parties (51%).

Interestingly, there are generally fewer people who expect it will have a large impact on ordinary people (48%). Much of the public clearly thinks the impact of generative AI will be mediated by various existing social institutions.

Bearing in mind how different the countries we cover are in many respects, including in terms of how people use and think about news and media (see, for example, Newman et al. 2023), it is striking that we find few cross-country differences in public expectations around the impact of generative AI. There are a few minor exceptions. For example, expectations around impact for politicians and political parties are a bit higher than average in the USA (60% vs 51%) and a bit lower in Japan (44% vs 51%) – but, for the most part, views across countries are broadly similar.

For almost all these sectors, there is little variation across age and gender, and the main difference when it comes to different levels of education is that respondents with lower levels of formal education are more likely to respond with ‘don’t know’, and those with higher levels of education are more likely to expect a large impact. The number who expect a small impact remains broadly stable across levels of education.

The only exception to this relative lack of variation by demographic factors is expectations around what impact generative AI will have for ordinary people. Younger respondents, who, as we have shown in earlier sections, are much more likely to have used generative AI tools, are also much more likely to expect a large impact within the next five years than older people, who often have little or no personal experience of using generative AI (Figure 9).

Expectations around the impact of generative AI, whether large or small, in themselves say nothing about how people think about whether this impact will, on balance, be for better or for worse.

Because generative AI use is highly mediated by institutions, and our data document that much of the public clearly recognise this, a useful additional way to think about expectations is to consider whether members of the public trust different sectors to make responsible use of generative AI.

We find that public trust in different institutions to make responsible use of generative AI is generally quite low (Figure 10). While around half in most of the six counties trust scientists and healthcare professionals to use generative AI responsibly, the figures drop below 40% for most other sectors in most countries. Figures for social media companies are lower than many other sectors, as are those for news media, ranging from 12% in the UK to 30% in Argentina and the USA.

There is more cross-country variation in public trust and distrust in different institutions’ potential use of generative AI, partly in line with broader differences from country to country in terms of trust in institutions.

But there are also some overarching patterns.

First, younger people, while still often sceptical, are for many sectors more likely to say they trust a given institution to use generative AI responsibly, and less likely to express distrust. This tendency is most pronounced in the sectors viewed with greatest scepticism by the public at large, including the government, politicians, and ordinary people, as well as news media, social media, and search engines.

Second, a significant part of the public does not have a firm view on whether they trust or distrust different institutions to make responsible use of generative AI. Varying from sector to sector and from country to country, between roughly one-quarter and half of respondents answer ‘neither trust nor distrust’ or ‘don’t know’ when asked. There is much uncertainty and often limited personal experience; in that sense, the jury is still out.

Leaving aside country differences for a moment and looking at the aggregate across all six countries, we can combine our data on public expectations around the size of the impact that generative AI will have with expectations around whether various sectors will use these technologies responsibly. This will provide an overall picture of how people think about these issues across different social institutions (Figure 11).

If we compare public perceptions relative to the average percentage of respondents who expect a large impact across all sectors (58%, marked by the vertical dashed line in Figure 11) and the average percentage of respondents who distrust actors in a given sector to make responsible use of generative AI (33%, marked by the horizontal dashed line), we can group expectations from sector to sector into four quadrants.

• First, there are those sectors where people expect generative AI to have a relatively large impact, but relatively few expect it will be used irresponsibly (e.g. healthcare and science).
• Second, there are sectors where people expect the impact may not be as great, and relatively fewer fear irresponsible use (e.g. ordinary people and retailers).
• Third, there are sectors where relatively few people expect a large impact, and relatively more people are worried about irresponsible use (e.g. government and political parties).
• Finally, there are sectors where more people expect large impact, and more people fear irresponsible use by the actors involved (e.g. social media and the news media, who are viewed very similarly by the public in this respect).

It is important to keep this quite nuanced and differentiated set of expectations in mind in interpreting people’s general expectations around what impact they think generative AI will have for them personally, as well as for society at large.

Asked if they think that generative AI will make their life better or worse, more than half of our respondents answer ‘neither better nor worse’ or ‘don’t know’, with a plurality in four of the six countries covered answering ‘better’, and a significant minority ‘worse’ (Figure 12). The large number of people with no strong expectations either way is consistent across countries, but the balance between more optimistic responses and more pessimistic ones varies.

People’s expectations when asked whether generative AI will make society better or worse are more pessimistic on average. There are about the same number of optimists, but significantly more pessimists who believe generative AI will make society worse. Expectations around what generative AI might mean for society are more varied across the six countries we cover. In two (France and the UK), there are more who expect it will make society worse than better. In another two (Denmark and the USA), there are as many pessimists as optimists. And in the remaining two (Argentina and Japan) more respondents expect generative AI products will make society better than expect them to make society worse.

Looking more closely at people’s expectations, both in terms of their own life and in terms of society, younger people and people with more formal education also often opt for ‘neither better nor worse’ or ‘don’t know’, but in most countries – Argentina being the exception – they are more likely to answer ‘better’ (Figure 13).

Asked whether they think the use of generative AI will make different areas of life better or worse, again, much of the public is undecided, either opting for ‘neither better nor worse’ or answering ‘don’t know’, underlining that it is still early days.

We now look specifically at the percentage point difference between optimists who expect AI to make things better and pessimists who expect it to make them worse gives a sense of public expectations across different areas (Figure 14). Large parts of the public think generative AI will make science (net ‘better’ of +44 percentage points), healthcare (+36), and many daily routine activities, including transportation (+26), shopping (+22), and entertainment (+17), better, even though there is much less optimism when it comes to core areas of the rule of law, including criminal justice (+1) and more broadly legal rights and due process (-3), and considerable pessimism for some very bread-and-butter issues, including cost of living (-6), equality (-6), and job security (-18).

News and journalism is also an area where, on balance, there is more pessimism than optimism (-8) – a striking contrast to another area involving the media, namely entertainment (+17). But there is a lot of national variation here. In countries that are more optimistic about the potential effects of generative AI, namely Argentina (+19) and Japan (+8), the proportion that think it will make news and journalism better is larger than the proportion that think it will become worse. The UK public are particularly negative about the effect of generative AI on journalism, with a net score of -35. There is a similar lack of consensus across different countries on whether crime and justice, legal rights and due process, cost of living, equality, and job security will be made better or worse.

3. How people think generative AI is being used by journalists right now

Many of the conversations around generative AI and journalism are about what might happen in the future – speculation about what the technology may or may not be able to do one day, and how this will shape the profession as we know it. But it is important to remember that some journalists and news organisations are using generative AI right now, and they have been using some form of AI in the newsroom for several years.

We now focus on how much the public knows about this, what they think journalists currently use generative AI for, and what processes they think news media have in place to ensure quality.

In the survey, we showed respondents a list of journalistic tasks and asked them how often they think journalists perform them ‘using artificial intelligence with some human oversight’. The tasks ranged from behind-the-scenes work like ‘editing the spelling and grammar of an article’ and ‘data analysis’ through to much more audience-facing outputs like ‘writing the text of an article’ and ‘creating a generic image/illustration to accompany the text of an article’.

We specifically asked about doing these ‘using artificial intelligence with some human oversight’ because we know that some newsrooms are already performing at least some tasks in this way, while few are currently doing them entirely using AI without a human in the loop. Even tasks that may seem fanciful to some, like ‘creating an artificial presenter or author’, are not without precedent. In Germany, for example, the popular regional newspaper Express has created a profile for an artificial author called Klara Indernach, 1 which it uses as the byline for its articles created with the help of AI, and several news organisations across the world already use AI-generated artificial presenters for various kinds of video and audio.

Figure 15 shows that a substantial minority of the public believe that journalists already always or often use generative AI to complete a wide range of different tasks. Around 40% believe that journalists often or always use AI for translation (43%), checking spelling and grammar (43%), and data analysis (40%). Around 30% think that journalists often or always use AI for re-versioning – whether it’s rewriting the same article for different people (28%) or turning text into audio or video (30%) – writing headlines (29%), or creating stock images (30%).

In general, the order of the tasks in Figure 15 reflects the fact that people – perhaps correctly – believe that journalists are more likely to employ AI for behind-the-scenes work like spellchecking and translation than they are for more audience-facing outputs. This may be because people understand that some tasks carry a greater reputational risk for journalists, and/or that the technology is simply better at some things than others.

The results may also reveal a degree of cynicism about journalism from some parts of the public. The fact that around a quarter think that journalists always or often use AI to create an image if a real photograph is not available (28%) and 17% think they create an artificial presenter or author may say more about their attitudes towards journalism as an institution than about how they think generative AI is actually being used. However unwelcome they might be – and however wrong they are about how many news media use AI – these perceptions are a social reality, shaping how parts of the public think about the intersection between journalism and AI.

Public perceptions of what journalists and news media already use AI for are quite consistent across different genders and age groups, but there are some differences by country, with respondents in Argentina and the USA a little more likely to believe that AI is used for each of these tasks, and respondents in Denmark and the UK less likely.

Among those news organisations that have decided to implement generative AI for certain tasks, the importance of ‘having a human in the loop’ to oversee processes and check errors is often stressed. Human oversight is nearly always mentioned in public-facing guidelines on the use of AI for editorial work, and journalists themselves mention it frequently (Becker et al. 2024).

Large parts of the public, however, do not think this is happening (Figure 16). Averaging across the six countries, around one-third think that human editors ‘always’ or ‘often’ check AI outputs to make sure they are correct or of a high standard before publishing them. Nearly half think that journalists ‘sometimes’, ‘rarely’, or ‘never’ do this – again, perhaps, reflecting a level of cynicism about the profession among the public, or a tendency to judge the whole profession and industry on the basis of how some parts of it act.

The proportion that think checking is commonplace is lowest in the UK, where only one-third of the population say they ‘trust most news most of the time’ (Newman et al. 2023), but we also see similarly low figures in Denmark, where trust in the news is much higher. The results may, therefore, also partly reflect more than just people’s attitudes towards journalism and the news media.

4. What does the public think about how journalists should use generative AI?

Various forms of AI have long been used to produce news stories by publishers including, for example, Associated Press, Bloomberg, and Reuters. And content produced with newer forms of generative AI has, with mixed results, been published by titles including BuzzFeed, the Los Angeles Times , the Miami Herald , USA Today , and others.

Publishers may be more or less comfortable with how they are using these technologies to produce various kinds of content, but our data suggest that much of the public is not – at least not yet. As we explore in greater detail in our forthcoming 2024 Reuters Institute Digital News Report (Newman et al. 2024), people are generally more comfortable with news produced by human journalists than by AI.

However, averaging across six countries, younger people are significantly more likely to say they are comfortable with using news produced in whole or in part by AI (Figure 17). The USA and Argentina have somewhat higher levels of comfort with news made by generative AI, but there too, much of the public remains sceptical.

We also asked respondents whether they are comfortable or uncomfortable using news produced mostly by AI with some human oversight on a range of different topics. Figure 18 shows the net percentage point difference between those that selected ‘very’ or ‘somewhat’ comfortable and those that selected ‘very’ or ‘somewhat’ uncomfortable (though, as ever, a significant minority selected the ‘neither’ or ‘don’t know’ options). Looking across different topics, there is somewhat more comfort with using news produced mostly by AI with some human oversight when it comes to ‘softer’ news topics, like fashion (+7) and sports (+5), than ‘hard’ news topics including politics (-33) and international affairs (-21).

But in every area, at this point in time, only for a very small number of topics are there more people uncomfortable with relying on AI-generated news than comfortable. As with overall comfort, there is somewhat greater acceptance of the use of AI for generating various kinds of news with at least some human oversight in the USA and Argentina.

Putting aside country differences, there is again a marked difference between our respondents overall and younger respondents. Among respondents overall, there are only three topic areas out of ten where slightly more respondents are comfortable with news made mostly by AI with some human oversight than are uncomfortable with this. Among respondents aged 18 to 24, this rises to six out of ten topic areas.

It is important to remember that much of the public does not have strong views either way, at least at this stage. Between one-quarter and one-third of respondents answer either ‘neither comfortable nor uncomfortable’ or ‘don’t know’ when asked the general questions about comfort with different degrees of reliance on generative AI versus human journalists, and between one-third and half of respondents do the same when asked about generative AI news for specific topics. It is an open question as to how these less clearly formed views will evolve.

One way to assess what the public expects it will mean if and when AI comes to play a greater role in news production is to gauge people’s views on how it will change news, compared to a baseline of news produced entirely by human journalists.

We map this by asking respondents if they think that news produced mostly by AI with some human oversight will differ from what most are used to across a range of different qualities and attributes.

Between one-third and half of our respondents do not have a strong view either way. Focusing on those respondents who do have a view, we can look at the net percentage point difference between how many respondents think AI will make the news somewhat more or much more (e.g. more ‘up to date’ or more ‘transparent’), versus somewhat less or much less, of each, helping to provide an overarching picture of public expectations.

On balance, more respondents expect news produced mostly by AI with some human oversight to be less trustworthy (-17) and less transparent (-8), but more up to date (+22) and – by a large margin – cheaper to make (+33) (Figure 19). There is considerable national variation here, but with the exception of Argentina, the balance of public opinion (net positive or negative) is usually the same for these four attributes. For the others, the balance often varies.

Essentially our data suggest that the public, at this stage, primarily think that the use of AI in news production will help publishers by cutting costs, but identify few, if any, ways in which they expect it to help them – and several key areas where many expect news made with AI to be worse.

In light of this, it makes sense that, when asked if news produced mostly by AI with some human oversight is more or less worth paying for than news produced entirely by a human journalist, an average of 41% across six countries say less worth paying for (Figure 20). Just 8% say they think that news made in this way will be more valuable.

There is some variation here by country and by age, but even among the generally more AI-positive younger respondents aged 18–24, most say either less worth paying for (33%) or about the same (38%). The implications of the spread of generative AI and how it is used by publishers for people’s willingness to pay for news will be interesting to follow going forward, as tensions may well mount between the ‘pivot to pay’ we have seen from many news media in recent years and the views we map here.

Looking across a range of different tasks that journalists and news media might use generative AI for, and in many cases already are using generative AI for, we can again gauge how comfortable the public is by looking at the balance between how many are comfortable with a particular use case and how many are uncomfortable.

As with several of the questions above, about a third have no strong view either way at this stage – but many others do. Across six countries, the balance of public opinion ranges from relatively high levels of comfort with back-end tasks, including editing spelling and grammar (+38), translation (+35), and the making of charts (+28), to widespread net discomfort with synthetic content, including creating an image if a real photo is not available (-13) and artificial presenters and authors (-24) (Figure 21).

When asked if it should be disclosed or labelled as such if news has been produced mostly by AI with some human oversight, only 5% of our respondents say none of the use cases included above need to be disclosed, and the vast majority of respondents say they want some form of disclosure or labelling in at least some cases. Research on the effect of labelling AI-generated news is ongoing, but early results suggest that although labelling may be desired by audiences, it may have a negative effect on trust (Toff and Simon 2023).

There is, however, less consensus on what exactly should be disclosed or labelled, except for somewhat lower expectations around the back-end tasks people are frequently comfortable with AI completing (Figure 22). Averaging across six countries, around half say that ‘creating an image if a real photograph is not available’ (49%), ‘writing the text of an article’ (47%), and ‘data analysis’ (47%) should be labelled as such if generative AI is used. However, this figure drops to around one-third for ‘editing the spelling and grammar of an article’ (32%) and ‘writing a headline’ (35%). Again, variation exists between both countries and demographic groups that are generally more positive about AI.

Based on online surveys of nationally representative samples in six countries, we have, with a particular focus on journalism and news, documented how aware people are of generative AI, how they use it, and their expectations on the magnitude of impact it will have in different sectors – including whether it will be used responsibly.

We find that most of the public are aware of various generative AI products, and that many have used them, especially ChatGPT. But between 19% and 30% of the online population in the six countries surveyed have not heard of any of the most popular generative AI tools, and while many have tried using various of them, only a very small minority are, at this stage, frequent users. Going forward, some use will be driven by people seeking out and using stand-alone generative AI tools such as ChatGPT, but it seems likely that much of it will be driven by a combination of professional adaptation, through products used in the workplace, and the introduction of more generative AI-powered elements into platforms already widely used in people’s private lives, including social media and search engines, as illustrated with the recent announcements of much greater integration of generative AI into Google Search.

When it comes to public expectations around the impact of generative AI and whether these technologies are likely to be used responsibly, we document a differentiated and nuanced picture. First, there are sectors where people expect generative AI will have a greater impact, and relatively fewer people expect it will be used irresponsibly (including healthcare and science). Second, there are sectors where people expect the impact may not be as great, and relatively fewer fear irresponsible use (including from ordinary people and retailers). Third, there are sectors where relatively fewer people expect large impact, and relatively more people are worried about irresponsible use (including government and political parties). Fourth, there are sectors where more people expect large impact, and more people fear irresponsible use by the actors involved (this includes social media and the news media).

Much of the public is still undecided on what the impact of generative AI will be. They are unsure whether, on balance, generative AI will make their own lives and society better or worse. This is understandable, given many are not aware of any of these products, and few have personal experience of using them frequently. Younger people and those with higher levels of formal education – who are also more likely to have used generative AI – are generally more positive.

Expectations around what generative AI might mean for society are more varied across the six countries we cover. In two, there are more who expect it will make society worse than better, in another two, there are as many pessimists as optimists, and in the final two, more respondents expect generative AI products will make society better than expect them to make society worse. These differences may also partly reflect the current situation societies find themselves in, and whether people think AI can fundamentally change the direction of those societies. To some extent we also see this pattern reflected in how people think about AI in news. Across a range of measures, in some countries people are generally more optimistic, but in others more pessimistic.

Looking at journalism and news media more closely, we have found that many believe generative AI is already relatively widely used for many different tasks, but that they are, in most cases, not convinced these uses of AI make news better – they mostly expect it to make it cheaper to produce.

While there is certainly curiosity, openness to new approaches, and some optimism in parts of the public (especially when it comes to the use of these technologies in the health sector and by scientists), generally, the role of generative AI in journalism and news media is seen quite negatively compared to many other sectors – in some ways similar to how much of the public sees social media companies. Basically, we find that the public primarily think that the use of generative AI in news production will help publishers cut costs, but identify few, if any, ways in which they expect it to help them as audiences, and several key areas where many expect news made with AI to be worse.

These views are not solely informed by how people think generative AI will impact journalism in the future. A substantial minority of the public believe that journalists already always or often use generative AI to complete a wide range of different tasks. Some of these are tasks that most are comfortable with, and are within the current capabilities of generative AI, like checking spelling and grammar. But many others are not. More than half of our respondents believe that news media at least sometimes use generative AI to create images if no real photographs are available, and as many believe that news media at least sometimes create artificial authors or presenters. These are forms of use that much of the public are uncomfortable with.

Every individual journalist and every news organisation will need to make their own decisions about which, if any, uses of generative AI they believe are right for them, given their editorial principles and their practical imperatives. Public opinion cannot – and arguably should not – dictate these decisions. But public opinion provides a guide on which uses are likely to influence how people judge the quality of news and their comfort with relying on it, and thus helps, among other things, to identify areas where it is particularly important for journalists and news media to communicate and explain their use of AI to their target audience.

It is still early days, and it remains to be seen how public use and perception of generative AI in general, and its role in journalism and news specifically, will evolve. On many of the questions asking respondents to evaluate AI in different sectors and for different uses, between roughly a quarter and half of respondents pick relatively neutral middle options or answer ‘don’t know’. There is still much uncertainty around what role generative AI should and will have, in different sectors, and for different purposes. And, especially in light of how many have limited personal experience of using these products, it makes sense that much of the public has not made up their minds.

Public debate, opinion commentary, and news coverage will be among the factors influencing how this evolves. So will people’s own experience of using generative AI products, whether for private or professional purposes. Here, it is important to note two things. First, younger respondents generally are much more open to, and in many cases optimistic about, generative AI than respondents overall. Second, despite the many documented limitations and problems with state-of-the-art generative AI products, those respondents who use these tools themselves tend to offer a reasonably positive assessment of how well they work, and how much they trust them. This does not necessarily mean that future adopters will feel the same. But if they do, and use becomes widespread and routine, overall public opinion will change – in some cases perhaps towards a more pessimistic view, but, at least if our data are anything to go by, in a more grounded and cautiously optimistic direction.

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1 https://www.express.de/autor/klara-indernach-594809

About the authors

Dr Richard Fletcher is Director of Research at the Reuters Institute for the Study of Journalism. He is primarily interested in global trends in digital news consumption, the use of social media by journalists and news organisations, and, more broadly, the relationship between computer-based technologies and journalism.

Professor Rasmus Kleis Nielsen is Director of the Reuters Institute for the Study of Journalism, Professor of Political Communication at the University of Oxford, and served as Editor-in-Chief of the International Journal of Press/Politics from 2015 to 2018. His work focuses on changes in the news media, political communication, and the role of digital technologies in both.

Acknowledgements

We would like to thank Caryhs Innes, Xhoana Beqiri, and the rest of the team at YouGov for their work on fielding the survey. We would also like to thank Felix Simon for his help with the data analysis. We are grateful to the other members of the research team at RISJ for their input on the questionnaire and interpretation of the results, and to Kate Hanneford-Smith, Alex Reid, and Rebecca Edwards for helping to move this project forward and keeping us on track.

Funding acknowledgement

Report published by the Reuters Institute for the Study of Journalism (2024) as part of our work on AI and the Future of News , supported by seed funding from Reuters News and made possible by core funding from the Thomson Reuters Foundation.

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